Confabulation

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In psychology, confabulation is a memory error defined as the production of fabricated, distorted, or misinterpreted memories about oneself or the world. People who confabulate present incorrect memories ranging from "subtle alterations to bizarre fabrications", and are generally very confident about their recollections, despite contradictory evidence.

Description

Confabulation is distinguished from lying as there is no intent to deceive and the person is unaware the information is false. Although individuals can present blatantly false information, confabulation can also seem to be coherent, internally consistent, and relatively normal.

Most known cases of confabulation are symptomatic of brain damage or dementias, such as aneurysm, Alzheimer's disease, or Wernicke–Korsakoff syndrome (a common manifestation of thiamine deficiency caused by alcoholism). Additionally confabulation often occurs in people who are suffering from anticholinergic toxidrome when interrogated about bizarre or irrational behaviour.

Confabulated memories of all types most often occur in autobiographical memory and are indicative of a complicated and intricate process that can be led astray at any point during encoding, storage, or recall of a memory. This type of confabulation is commonly seen in Korsakoff's syndrome.

Distinctions

Two types of confabulation are often distinguished:

• Provoked (momentary, or secondary) confabulations represent a normal response to a faulty memory, are common in both amnesia and dementia, and can become apparent during memory tests.
• Spontaneous (or primary) confabulations do not occur in response to a cue and seem to be involuntary. They are relatively rare, more common in cases of dementia, and may result from the interaction between frontal lobe pathology and organic amnesia.

Another distinction is that between:

• Verbal confabulations- spoken false memories, most common type
• Behavioral confabulations- occur when an individual acts on their false memories

Signs and symptoms

Confabulation is associated with several characteristics:

1. Typically verbal statements but can also be non-verbal gestures or actions.
2. Can include autobiographical and non-personal information, such as historical facts, fairy-tales, or other aspects of semantic memory.
3. The account can be fantastic or coherent.
4. Both the premise and the details of the account can be false.
5. The account is usually drawn from the patient's memory of actual experiences, including past and current thoughts.
6. The patient is unaware of the accounts' distortions or inappropriateness, and is not concerned when errors are pointed out.
7. There is no hidden motivation behind the account.
8. The patient's personality structure may play a role in his/her readiness to confabulate.^[3]

Theories

Theories of confabulation range in emphasis. Some theories propose that confabulations represent a way for memory-disabled people to maintain their self-identity. Other theories use neurocognitive links to explain the process of confabulation. Still other theories frame confabulation around the more familiar concept of delusion. Other researchers frame confabulation within the fuzzy-trace theory. Finally, some researchers call for theories that rely less on neurocognitive explanations and more on epistemic accounts.

Neuropsychological theories

The most popular theories of confabulation come from the field of neuropsychology or cognitive neuroscience. Research suggests that confabulation is associated with dysfunction of cognitive processes that control the retrieval from long-term memory. Frontal lobe damage often disrupts this process, preventing the retrieval of information and the evaluation of its output. Furthermore, researchers argue that confabulation is a disorder resulting from failed "reality monitoring/source monitoring" (i.e. deciding whether a memory is based on an actual event or whether it is imagined).

Some neuropsychologists suggest that errors in retrieval of information from long-term memory that are made by normal subjects involve different components of control processes than errors made by confabulators. Kraepelin distinguished two subtypes of confabulation, one of which he called simple confabulation, caused partly by errors in the temporal ordering of real events. The other variety he called fantastic confabulation, which was bizarre and patently impossible statements not rooted in true memory. Simple confabulation may result from damage to memory systems in the medial temporal lobe. Fantastic confabulations reveal a dysfunction of the Supervisory System, which is believed to be a function of the frontal cortex.

Self-identity theory

Some argue confabulations have a self-serving, emotional component in those with memory deficits that aids to maintain a coherent self-concept. In other words, people who confabulate are motivated to do so, because they have gaps in their memory that they want to fill in and cover up.

Temporality theory

Support for the temporality account suggests that confabulations occur when an individual is unable to place events properly in time. Thus, an individual might correctly state an action he/she performed, but say he/she did it yesterday, when he/she did it weeks ago. In the Memory, Consciousness, and Temporality Theory, confabulation occurs because of a deficit in temporal consciousness or awareness.

Monitoring theory

Along a similar notion are the theories of reality and source monitoring theories. In these theories, confabulation occurs when individuals incorrectly attribute memories as reality, or incorrectly attribute memories to a certain source. Thus, an individual might claim an imagined event happened in reality, or that a friend told him/her about an event he/she actually heard about on television.

Strategic retrieval account theory

Supporters of the strategic retrieval account suggest that confabulations occur when an individual cannot actively monitor a memory for truthfulness after its retrieval. An individual recalls a memory, but there is some deficit after recall that interferes with the person establishing its falseness.

Executive control theory

Still others propose that all types of false memories, including confabulation, fit into a general memory and executive function model. In 2007, a framework for confabulation was proposed that stated confabulation is the result of two things: Problems with executive control and problems with evaluation. In the executive control deficit, the incorrect memory is retrieved from the brain. In the evaluative deficit, the memory will be accepted as a truth due to an inability to distinguish a belief from an actual memory.

In the context of delusion theories

Recent models of confabulation have attempted to build upon the link between delusion and confabulation. More recently, a monitoring account for delusion, applied to confabulation, proposed both the inclusion of conscious and unconscious processing. The claim was that by encompassing the notion of both processes, spontaneous versus provoked confabulations could be better explained. In other words, there are two ways to confabulate. One is the unconscious, spontaneous way in which a memory goes through no logical, explanatory processing. The other is the conscious, provoked way in which a memory is recalled intentionally by the individual to explain something confusing or unusual.

Fuzzy-trace theory

Fuzzy-trace theory, or FTT, is a concept more commonly applied to the explanation of judgement decisions. According to this theory, memories are encoded generally (gist), as well as specifically (verbatim). Thus, a confabulation could result from recalling the incorrect verbatim memory or from being able to recall the gist portion, but not the verbatim portion, of a memory.

FTT uses a set of five principles to explain false-memory phenomena. Principle 1 suggests that subjects store verbatim information and gist information parallel to one another. Both forms of storage involve the surface content of an experience. Principle 2 shares factors of retrieval of gist and verbatim traces. Principle 3 is based on dual-opponent processes in false memory. Generally, gist retrieval supports false memory, while verbatim retrieval suppresses it. Developmental variability is the topic of Principle 4. As a child develops into an adult, there is obvious improvement in the acquisition, retention, and retrieval of both verbatim and gist memory. However, during late adulthood, there will be a decline in these abilities. Finally, Principle 5 explains that verbatim and gist processing cause vivid remembering. Fuzzy-trace Theory, governed by these 5 principles, has proved useful in explaining false memory and generating new predictions about it.

Epistemic theory

However, not all accounts are so embedded in the neurocognitive aspects of confabulation. Some attribute confabulation to epistemic accounts. In 2009, theories underlying the causation and mechanisms for confabulation were criticized for their focus on neural processes, which are somewhat unclear, as well as their emphasis on the negativity of false remembering. Researchers proposed that an epistemic account of confabulation would be more encompassing of both the advantages and disadvantages of the process.

Presentation

Associated neurological and psychological conditions

Confabulations are often symptoms of various syndromes and psychopathologies in the adult population including: Korsakoff's syndrome, Alzheimer's disease, schizophrenia, and traumatic brain injury.

Wernicke–Korsakoff syndrome is a neurological disorder typically characterized by years of chronic alcohol abuse and a nutritional thiamine deficiency. Confabulation is one salient symptom of this syndrome. A study on confabulation in Korsakoff's patients found that they are subject to provoked confabulation when prompted with questions pertaining to episodic memory, not semantic memory, and when prompted with questions where the appropriate response would be "I don’t know." This suggests that confabulation in these patients is "domain-specific." Korsakoff's patients who confabulate are more likely than healthy adults to falsely recognize distractor words, suggesting that false recognition is a "confabulatory behavior."

Alzheimer's disease is a condition with both neurological and psychological components. It is a form of dementia associated with severe frontal lobe dysfunction. Confabulation in individuals with Alzheimer's is often more spontaneous than it is in other conditions, especially in the advanced stages of the disease. Alzheimer's patients demonstrate comparable abilities to encode information as healthy elderly adults, suggesting that impairments in encoding are not associated with confabulation. However, as seen in Korsakoff's patients, confabulation in Alzheimer's patients is higher when prompted with questions investigating episodic memory. Researchers suggest this is due to damage in the posterior cortical regions of the brain, which is a symptom characteristic of Alzheimer's Disease.

Schizophrenia is a psychological disorder in which confabulation is sometimes observed. Although confabulation is usually coherent in its presentation, confabulations of schizophrenic patients are often delusional Researchers have noted that these patients tend to make up delusions on the spot which are often fantastic and become increasingly elaborate with questioning. Unlike patients with Korsakoff's and Alzheimer's, patients with schizophrenia are more likely to confabulate when prompted with questions regarding their semantic memories, as opposed to episodic memory prompting. In addition, confabulation does not appear to be related to any memory deficit in schizophrenic patients. This is contrary to most forms of confabulation. Also, confabulations made by schizophrenic patients often do not involve the creation of new information, but instead involve an attempt by the patient to reconstruct actual details of a past event.

Traumatic brain injury (TBI) can also result in confabulation. Research has shown that patients with damage to the inferior medial frontal lobe confabulate significantly more than patients with damage to the posterior area and healthy controls. This suggests that this region is key in producing confabulatory responses, and that memory deficit is important but not necessary in confabulation. Additionally, research suggests that confabulation can be seen in patients with frontal lobe syndrome, which involves an insult to the frontal lobe as a result of disease or traumatic brain injury (TBI). Finally, rupture of the anterior or posterior communicating artery, subarachnoid hemorrhage, and encephalitis are also possible causes of confabulation.

Location of brain lesions

Confabulation is believed to be a result of damage to the right frontal lobe of the brain. In particular, damage can be localized to the ventromedial frontal lobes and other structures fed by the anterior communicating artery (ACoA), including the basal forebrain, septum, fornix, cingulate gyrus, cingulum, anterior hypothalamus, and head of the caudate nucleus.

Developmental differences

While some recent literature has suggested that older adults may be more susceptible than their younger counterparts to have false memories, the majority of research on forced confabulation centers around children. Children are particularly susceptible to forced confabulations based on their high suggestibility. When forced to recall confabulated events, children are less likely to remember that they had previously confabulated these situations, and they are more likely than their adult counterparts to come to remember these confabulations as real events that transpired. Research suggests that this inability to distinguish between past confabulatory and real events is centered on developmental differences in source monitoring. Due to underdeveloped encoding and critical reasoning skills, children's ability to distinguish real memories from false memories may be impaired. It may also be that younger children lack the meta-memory processes required to remember confabulated versus non-confabulated events. Children's meta-memory processes may also be influenced by expectancies or biases, in that they believe that highly plausible false scenarios are not confabulated. However, when knowingly being tested for accuracy, children are more likely to respond, "I don’t know" at a rate comparable to adults for unanswerable questions than they are to confabulate. Ultimately, misinformation effects can be minimized by tailoring individual interviews to the specific developmental stage, often based on age, of the participant.

Provoked versus spontaneous confabulations

There is evidence to support different cognitive mechanisms for provoked and spontaneous confabulation. One study suggested that spontaneous confabulation may be a result of an amnesic patient's inability to distinguish the chronological order of events in their memory. In contrast, provoked confabulation may be a compensatory mechanism, in which the patient tries to make up for their memory deficiency by attempting to demonstrate competency in recollection.

Confidence in false memories

Confabulation of events or situations may lead to an eventual acceptance of the confabulated information as true. For instance, people who knowingly lie about a situation may eventually come to believe that their lies are truthful with time. In an interview setting, people are more likely to confabulate in situations in which they are presented false information by another person, as opposed to when they self-generate these falsehoods. Further, people are more likely to accept false information as true when they are interviewed at a later time (after the event in question) than those who are interviewed immediately or soon after the event. Affirmative feedback for confabulated responses is also shown to increase the confabulator's confidence in their response. For instance, in culprit identification, if a witness falsely identifies a member of a line-up, he will be more confident in his identification if the interviewer provides affirmative feedback. This effect of confirmatory feedback appears to last over time, as witnesses will even remember the confabulated information months later.

Among normal subjects

On rare occasions, confabulation can also be seen in normal subjects. It is currently unclear how completely healthy individuals produce confabulations. It is possible that these individuals are in the process of developing some type of organic condition that is causing their confabulation symptoms. It is not uncommon, however, for the general population to display some very mild symptoms of provoked confabulations. Subtle distortions and intrusions in memory are commonly produced by normal subjects when they remember something poorly.

Diagnosis and treatment

Spontaneous confabulations, due to their involuntary nature, cannot be manipulated in a laboratory setting. However, provoked confabulations can be researched in various theoretical contexts. The mechanisms found to underlie provoked confabulations can be applied to spontaneous confabulation mechanisms. The basic premise of researching confabulation comprises finding errors and distortions in memory tests of an individual.

Deese–Roediger–McDermott lists

Confabulations can be detected in the context of the Deese–Roediger–McDermott paradigm by using the Deese–Roediger–McDermott lists. Participants listen to audio recordings of several lists of words centered around a theme, known as the critical word. The participants are later asked to recall the words on their list. If the participant recalls the critical word, which was never explicitly stated in the list, it is considered a confabulation. Participants often have a false memory for the critical word.

Recognition tasks

Confabulations can also be researched by using continuous recognition tasks. These tasks are often used in conjunction with confidence ratings. Generally, in a recognition task, participants are rapidly presented with pictures. Some of these pictures are shown once; others are shown multiple times. 

Participants press a key if they have seen the picture previously. Following a period of time, participants repeat the task. More errors on the second task, versus the first, are indicative of confusion, representing false memories.

Free recall tasks

Confabulations can also be detected using a free recall task, such as a self-narrative task. Participants are asked to recall stories (semantic or autobiographical) that are highly familiar to them. The stories recalled are encoded for errors that could be classified as distortions in memory. Distortions could include falsifying true story elements or including details from a completely different story. Errors such as these would be indicative of confabulations.

Treatment

Treatment for confabulation is somewhat dependent on the cause or source, if identifiable. For example, treatment of Wernicke–Korsakoff syndrome involves large doses of vitamin B in order to reverse the thiamine deficiency. If there is no known physiological cause, more general cognitive techniques may be used to treat confabulation. A case study published in 2000 showed that Self-Monitoring Training (SMT) reduced delusional confabulations. Furthermore, improvements were maintained at a three-month follow-up and were found to generalize to everyday settings. Although this treatment seems promising, more rigorous research is necessary to determine the efficacy of SMT in the general confabulation population.

Research

Although significant gains have been made in the understanding of confabulation in recent years, there is still much to be learned. One group of researchers in particular has laid out several important questions for future study. They suggest more information is needed regarding the neural systems that support the different cognitive processes necessary for normal source monitoring. They also proposed the idea of developing a standard neuro-psychological test battery able to discriminate between the different types of confabulations. And there is a considerable amount of debate regarding the best approach to organizing and combining neuro-imaging, pharmacological, and cognitive/behavioral approaches to understand confabulation.

In a recent review article, another group of researchers contemplate issues concerning the distinctions between delusions and confabulation. They question whether delusions and confabulation should be considered distinct or overlapping disorders and, if overlapping, to what degree? They also discuss the role of unconscious processes in confabulation. Some researchers suggest that unconscious emotional and motivational processes are potentially just as important as cognitive and memory problems. Finally, they raise the question of where to draw the line between the pathological and the nonpathological. Delusion-like beliefs and confabulation-like fabrications are commonly seen in healthy individuals. What are the important differences between patients with similar etiology who do and do not confabulate? Since the line between pathological and nonpathological is likely blurry, should we take a more dimensional approach to confabulation? Research suggests that confabulation occurs along a continuum of implausibility, bizarreness, content, conviction, preoccupation, and distress, and impact on daily life.

 

False memory

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In psychology, a false memory is a phenomenon where a person recalls something that did not happen or recalls it differently from the way it actually happened. Suggestibility, activation of associated information, the incorporation of misinformation and source misattribution have been suggested to be several mechanisms underlying a variety of types of false memory phenomena.

False memories are a component of False Memory Syndrome (FMS).

Early work

The false memory phenomenon was initially investigated by psychological pioneers Pierre Janet and Sigmund Freud.

Sigmund Freud is known to be the founder of psychoanalysis, a field of psychology that has been deemed by many as dead or dying. One thing that has kept it alive is the emphasis Freud put on his study of memory. Freud was fascinated with memory and all the ways it could be understood, used, and manipulated. Some claim that his studies have been quite influential in contemporary memory research, including the research into the field of false memory. Pierre Janet was a French Neurologist also credited with great contributions into memory research. Pierre contributed to false memory through his ideas on dissociation and memory retrieval through hypnosis.

In 1974, Elizabeth Loftus and John Palmer conducted a study to investigate the effects of language on the development of false memory. The experiment involved two separate studies.

In the first test, 45 participants were randomly assigned to watch different videos of a car accident, in which separate videos had shown collisions at 20 mph (32 km/h), 30 mph (48 km/h) and 40 mph (64 km/h). Afterwards, participants filled out a survey. The survey asked the question, "About how fast were the cars going when they smashed into each other?" The question always asked the same thing, except the verb used to describe the collision varied. Rather than "smashed", other verbs used included "bumped", "collided", "hit", or "contacted". Participants estimated collisions of all speeds to average between 35 mph (56 km/h) to just below 40 mph (64 km/h). If actual speed was the main factor in estimate, it could be assumed that participants would have lower estimates for lower speed collisions. Instead, the word being used to describe the collision seemed to better predict the estimate in speed rather than the speed itself.

The second experiment also showed participants videos of a car accident, but the phrasing of the follow-up questionnaire was critical in participant responses. 150 participants were randomly assigned to three conditions. Those in the first condition were asked the same question as the first study using the verb "smashed". The second group was asked the same question as the first study, replacing "smashed" with "hit". The final group was not asked about the speed of the crashed cars. The researchers then asked the participants if they had seen any broken glass, knowing that there was no broken glass in the video. The responses to this question had shown that the difference between whether broken glass was recalled or not heavily depended on the verb used. A larger sum of participants in the "smashed" group declared that there was broken glass.

In this study, the first point brought up in discussion is that the words used to phrase a question can heavily influence the response given. Second, the study indicates that the phrasing of a question can give expectations to previously ignored details, and therefore, a misconstruction of our memory recall. This indication supports false memory as an existing phenomenon.

Replications in different contexts (such as hockey games instead of car crashes) have shown that different scenarios require different framing effects to produce differing memories.

Manifestations and types

Mandela effect

False memories can sometimes be shared by multiple people. This is sometimes called the Mandela effect. One prominent example comes from a 2010 study that examined people familiar with the clock at Bologna Centrale railway station, which was damaged in the Bologna massacre bombing in August 1980. In the study, 92% of respondents falsely remembered the clock had remained stopped since the bombing when, in fact, the clock was repaired shortly after the attack. Years later the clock was again stopped and set to the time of the bombing in observance and commemoration of the bombing. Other such examples include memories of the Berenstain Bears' name being spelled Berenstein, and of the existence of a 1990s movie entitled Shazaam starring comedian Sinbad as a genie.

The Bologna station clock, subject of a collective false memory

In 2010 this shared false memory phenomenon was dubbed the Mandela effect by self-described "paranormal consultant" Fiona Broome in reference to her false memory of the death of South African anti-Apartheid leader Nelson Mandela in prison in the 1980s (he actually died in 2013, after having served as President of South Africa from 1994-99), which she claimed was shared by "perhaps thousands" of other people.

Scientists suggest that these are examples of false memories shaped by similar cognitive factors affecting multiple people and family, such as social and cognitive reinforcement of incorrect memories or false news reports and misleading photographs that influence the formation of memories based on them. For example, the false memories of Shazaam have been explained as a confabulation of memories of the comedian wearing a genie-like costume during a TV presentation of Sinbad the Sailor movies in 1994, and a similarly named 1996 film Kazaam featuring a genie played by Shaquille O'Neal. In addition, in 1960s, Hanna-Barbera had an animated series about a genie called Shazzan.

Presuppositions and the misinformation effect

A presupposition is an implication through chosen language. If a person is asked, "What shade of blue was the wallet?", the questioner is, in translation, saying, "The wallet was blue. What shade was it?" The question's phrasing provides the respondent with a supposed "fact". This presupposition creates one of two separate effects: true effect and false effect.

• In true effect, the implication was accurate: the wallet really was blue. That makes the respondent's recall stronger, more readily available, and easier to extrapolate from. A respondent is more likely to remember a wallet as blue if the prompt said that it was blue, than if the prompt did not say so.
• In false effect, the implication was actually false: the wallet was not blue even though the question asked what shade of blue it was. This convinces the respondent of its truth (i.e., that the wallet was blue), which affects their memory. It can also alter responses to later questions to keep them consistent with the false implication.

Regardless of the effect being true or false, the respondent is attempting to conform to the supplied information, because they assume it to be true.

Loftus' meta-analysis on language manipulation studies suggested the misinformation effect taking hold on the recall process and products of the human memory. Even the smallest adjustment in a question, such as the article preceding the supposed memory, could alter the responses. For example, having asked someone if they had seen "the" stop sign, rather than "a" stop sign, provided the respondent with a presupposition that there was a stop sign in the scene. This presupposition increased the number of people responding that they had indeed seen the stop sign.

The strength of verbs used in conversation or questioning also has a similar effect on the memory; for example – the words met, bumped, collided, crashed, or smashed would all cause people to remember a car accident at different levels of intensity. The words bumped, hit, grabbed, smacked, or groped would all paint a different picture of a person in the memory of an observer of sexual harassment if questioned about it later. The stronger the word, the more intense the recreation of the experience in the memory is. This in turn could trigger further false memories to better fit the memory created (change how a person looks or how fast a vehicle was moving before an accident).

Word lists

One can trigger false memories by presenting subjects a continuous list of words. When subjects were presented with a second version of the list and asked if the words had appeared on the previous list, they found that the subjects did not recognize the list correctly. When the words on the two lists were semantically related to each other (e.g. sleep/bed), it was more likely that the subjects did not remember the first list correctly and created false memories (Anisfeld & Knapp, 1963).

In 1998 Kathleen McDermott and Henry Roediger III conducted a similar experiment. Their goal was to intentionally trigger false memories through word lists. They presented subjects with lists to study all containing a large number of words that that were semantically related to another word that was not found on the list. For example, if the word that they were trying to trigger was “river” the list would contain words such as flow, current, water, stream, bend, etc. They would then take the lists away and ask the subjects to recall the words on the lists. Almost every time the false memory was triggered and the subjects would end up recalling the target word as part of the list when it was never there. Mc Dermott and Roediger even went as far as informing the subjects of the purpose and details of the experiment, and still the subjects would recall the non listed target word as part of the word list they had studied.

Staged naturalistic events

Subjects were invited into an office and were told to wait there. After this they had to recall the inventory of the visited office. Subjects recognized objects consistent with the “office schema” although they did not appear in the office. (Brewer & Treyens, 1981)

In another study, subjects were presented with a situation where they witnessed a staged robbery. Half of the subjects witnessed the robbery live while the other half watched a video of the robbery as it took place. After the event, they were sat down and asked to recall what had happened during the robbery. The results surprisingly showed that those who watched the video of the robbery actually recalled more information more accurately than those who were live on the scene. Still false memory presented itself in ways such as subjects seeing things that would fit in a crime scene that weren't there, or not recalling things that don't fit the crime scene. This happened with both parties, displaying the idea of staged naturalistic events.

Relational processing

Memory retrieval has been associated with the brain's relational processing. In associating two events (in reference to false memory, say tying a testimony to a prior event), there are verbatim and gist representations. Verbatim matches to the individual occurrences (e.g., I do not like dogs because when I was five a chihuahua bit me) and gist matches to general inferences (e.g., I do not like dogs because they are mean). Keeping in line with the fuzzy-trace theory, which suggests false memories are stored in gist representations (which retrieves both true and false recall), Storbeck & Clore (2005) wanted to see how change in mood affected the retrieval of false memories. After using the measure of a word association tool called the Deese–Roediger–McDermott paradigm (DRM), the subjects' moods were manipulated. Moods were either oriented towards being more positive, more negative, or were left untouched. Findings suggested that a more negative mood made critical details, stored in gist representation, less accessible. This would imply that false memories are less likely to occur when a subject was in a worse mood.

Theories

Strength hypothesis (situational strength)

The strength hypothesis states that in strong situations (situations where one course of action is encouraged more than any other course of action due to the objective payoff) people are expected to demonstrate rational behavior, basing their behavior on the objective payoff.

Current laws present a great example of this. Most people, no matter how daring, will conform to the laws of the land because the objective payoff means they receive safety and security.

Construction hypothesis

The construction hypothesis says that if a true piece of information being provided can alter a respondent's answer, then so can a false piece of information.

Construction hypothesis has major implications for explanations on the malleability of memory. Upon asking a respondent a question that provides a presupposition, the respondent will provide a recall in accordance with the presupposition (if accepted to exist in the first place). The respondent will recall the object or detail.

Skeleton theory

Loftus developed what some refer to as "the skeleton theory" after having run an experiment involving 150 subjects from the University of Washington. Loftus noticed that when a presupposition was one of false information it could only be explained by the construction hypothesis and not the strength hypothesis. Loftus then stated that a theory needed to be created for complex visual experiences where the construction hypothesis plays a significantly more important role than situational strength. She presented a diagram as a “skeleton” of this theory, which later became referred to by some as the skeleton theory.

The skeleton theory explains the procedure of how a memory is recalled, which is split into two categories: the acquisition processes and the retrieval processes.

The acquisition processes are in three separate steps. First, upon the original encounter, the observer selects a stimulus to focus on. The information that the observer can focus on compared to all of the information occurring in the situation as a whole, is very limited. In other words, a lot is going on around us and we only pick up on a small portion. This forces the observer to begin by selecting a focal point for focus. Second, our visual perception must be translated into statements and descriptions. The statements represent a collection of concepts and objects; they are the link between the event occurrence and the recall. Third, the perceptions are subject to any "external" information being provided before or after the interpretation. This subsequent set of information can reconstruct the memory.

The retrieval processes come in two steps. First, the memory and imagery are regenerated. This perception is subject to what foci the observer has selected, along with the information provided before or after the observation. Second, the linking is initiated by a statement response, "painting a picture" to make sense of what was observed. This retrieval process results in either an accurate memory or a false memory.

Natural factors for the formation of false memories

Individual differences

Greater creative imagination and dissociation are known to relate to false memory formation. Creative imagination may lead to vivid details of imagined events. High dissociation may be associated with habitual use of lax response criteria for source decisions due to frequent interruption of attention or consciousness. Social desirability and false memory have also been examined. Social desirability effects may depend on the level of perceived social pressure.

Individuals who feel under greater social pressure may be more likely to acquiesce. Perceived pressure from an authority figure may lower individuals' criteria for accepting a false event as true. The new individual difference factors include preexisting beliefs about memory, self-evaluation of one's own memory abilities, trauma symptoms, and attachment styles. Regarding the first of these, metamemory beliefs about the malleability of memory, the nature of trauma memory, and the recoverability of lost memory may influence willingness to accept vague impressions or fragmentary images as recovered memories and thus, might affect the likelihood of accepting false memory. For example, if someone believes that memory once encoded is permanent, and that visualization is an effective way to recover memories, the individual may endorse more liberal criteria for accepting a mental image as true memory. Also, individuals who report themselves as having better everyday memories may feel more compelled to come up with a memory when asked to do so. This may lead to more liberal criteria, making these individuals more susceptible to false memory.

There is some research that shows individual differences in false memory susceptibility are not always large (even on variables that have previously shown differences—such as creative imagination or dissociation), that there appears to be no false memory trait, and that even those who have highly superior memory are susceptible to false memories.

Trauma

A history of trauma is relevant to the issue of false memory. It has been proposed that people with a trauma history or trauma symptoms may be particularly vulnerable to memory deficits, including source-monitoring failures.

Possible associations between attachment styles and reports of false childhood memories were also of interest. Adult attachment styles have been related to memories of early childhood events, suggesting that the encoding or retrieval of such memories may activate the attachment system. It is more difficult for avoidant adults to access negative emotional experiences from childhood, whereas ambivalent adults access these kinds of experiences easily. Consistent with attachment theory, adults with avoidant attachment styles, like their child counterparts, may attempt to suppress physiological and emotional reactions to activation of the attachment system. Significant associations between parental attachment and children's suggestibility exist. These data, however, do not directly address the issue of whether adults' or their parents' attachment styles are related to false childhood memories. Such data nevertheless suggest that greater attachment avoidance may be associated with a stronger tendency to form false memories of childhood.

Sleep deprivation

Sleep deprivation can also affect the possibility of falsely encoding a memory. In two experiments, participants studied DRM lists (lists of words [e.g., bed, rest, awake, tired] that are semantically associated with a non-presented word) before a night of either sleep or sleep deprivation; testing took place the following day. One study showed higher rates of false recognition in sleep-deprived participants, compared with rested participants.

Sleep deprivation can increase the risk of developing false memories. Specifically, sleep deprivation increased false memories in a misinformation task when participants in a study were sleep deprived during event encoding, but did not have a significant effect when the deprivation occurred after event encoding.

False memory syndrome

False memory syndrome recognizes false memory as a prevalent part of one's life in which it affects the person's mentality and day-to-day life. False memory syndrome differs from false memory in that the syndrome is heavily influential in the orientation of a person's life, while false memory can occur without this significant effect. The syndrome takes effect because the person believes the influential memory to be true. However, its research is controversial and the syndrome is excluded from identification as a mental disorder and, therefore, is also excluded from the Diagnostic and Statistical Manual of Mental Disorders. False memory is an important part of psychological research because of the ties it has to a large number of mental disorders, such as PTSD. The false memory syndrome is loosely defined, and not a part of the DSM. However, the syndrome suggests that false memory can be declared a syndrome when recall of a false or inaccurate memory takes great effect on a person's life. This false memory can completely alter the orientation of your personality and lifestyle.

Psychiatry

Therapists who subscribe to recovered memory theory point to a wide variety of common problems, ranging from eating disorders to sleeplessness, as evidence of repressed memories of sexual abuse. Psychotherapists tried to reveal “repressed memories” in mental therapy patients through “hypnosis, guided imagery, dream interpretation and narco-analysis”. The reasoning was that if abuse couldn't be remembered, then it needed to be recovered by the therapist. The legal phenomena developed in the 1980s, with civil suits alleging child sexual abuse on the basis of “memories” recovered during psychotherapy. The term “repressed memory therapy” gained momentum and with it social stigma surrounded those accused of abuse. The “therapy” led to other psychological disorders in persons whose memories were recovered.

Memories recovered through therapy have become more difficult to distinguish between simply being repressed or having existed in the first place.

Therapists have used strategies such as hypnotherapy, repeated questioning, and bibliotherapy. These strategies may provoke the recovery of nonexistent events or inaccurate memories. A recent report indicates that similar strategies may have produced false memories in several therapies in the century before the modern controversy on the topic which took place in the 1980s and 1990s.

According to Loftus, there are different possibilities to create false therapy-induced memory. One is the unintentional suggestions of therapists. For example, a therapist might tell their client that, on the basis of their symptoms, it is quite likely that they had been abused as a child. Once this "diagnosis" is made, the therapist sometimes urges the patient to pursue the recalcitrant memories. It is a problem resulting from the fact that people create their own social reality with external information.

The "lost-in-the-mall" technique is another recovery strategy. This is essentially a repeated suggestion pattern. The person whose memory is to be recovered is persistently said to have gone through an experience even if it may have not happened. This strategy can cause the person to recall the event as having occurred, despite its falsehood.

Hypnosis

Laurence and Perry conducted a study testing the ability to induce memory recall through hypnosis. Subjects were put into a hypnotic state and later woken up. Observers suggested that the subjects were woken up by a loud noise. Nearly half of the subjects being tested concluded that this was true, despite it being false. Although, by therapeutically altering the subject's state, they may have been led to believe that what they were being told was true. Because of this, the respondent has a false recall.

A 1989 study focusing on hypnotizability and false memory separated accurate and inaccurate memories recalled. In open-ended question formation, 11.5% of subjects recalled the false event suggested by observers. In a multiple-choice format, no participants claimed the false event had happened. This result led to the conclusion that hypnotic suggestions produce shifts in focus, awareness, and attention. Despite this, subjects do not mix fantasy up with reality.

Effects on society

Legal cases

Therapy-induced memory recovery has made frequent appearances in legal cases, particularly those regarding sexual abuse. Therapists can often aid in creating a false memory in a victim's mind, intentionally or unintentionally. They will associate a patient's behavior with the fact that they have been a victim of sexual abuse, thus helping the memory occur. They use memory enhancement techniques such as hypnosis dream analysis to extract memories of sexual abuse from victims. According to the FMSF (False Memory Syndrome Foundation), these memories are false and are produced in the very act of searching for and employing them in a life narrative. In Ramona v. Isabella, two therapists wrongly prompted a recall that their patient, Holly Ramona, had been sexually abused by her father. It was suggested that the therapist, Isabella, had implanted the memory in Ramona after use of the hypnotic drug sodium amytal. After a nearly unanimous decision, Isabella had been declared negligent towards Holly Ramona. This 1994 legal issue played a massive role in shedding light on the possibility of false memories' occurrences.

In another legal case where false memories were used, they helped a man to be acquitted of his charges. Joseph Pacely had been accused of breaking into a woman's home with the intent to sexually assault her. The woman had given her description of the assailant to police shortly after the crime had happened. During the trial, memory researcher Elizabeth Loftus testified that memory is fallible and there were many emotions that played a part in the woman's description given to police. Loftus has published many studies consistent with her testimony. These studies suggest that memories can easily be changed around and sometimes eyewitness testimonies are not as reliable as many believe.

Another notable case is Maxine Berry. Maxine grew up in the custody of her mother, who opposed the father having contact with her (Berry & Berry, 2001). When the father expressed his desire to attend his daughter's high school graduation, the mother enrolled Maxine in therapy, ostensibly to deal with the stress of seeing her father. The therapist pressed Maxine to recover memories of sex abuse by her father. Maxine broke down under the pressure and had to be psychiatrically hospitalized. She underwent tubal ligation, so she would not have children and repeat the cycle of abuse. With the support of her husband and primary care physician, Maxine eventually realized that her memories were false and filed a suit for malpractice. The suit brought to light the mother's manipulation of mental health professionals to convince Maxine that she had been sexually abused by her father. In February 1997 Maxine Berry sued her therapists and clinic that treated her from 1992-1995 and, she says, made her falsely believe she had been sexually and physically abused as a child when no such abuse ever occurred. The lawsuit, filed in February 1997 in Minnehaha Co. Circuit Court South Dakota, states that therapist Lynda O'Connor-Davis had an improper relationship with Berry, both during and after her treatment. The suit also names psychologist Vail Williams, psychiatrist Dr. William Fuller and Charter Hospital and Charter Counseling Center as defendants. Berry and her husband settled out of court.

Although there have been many legal cases in which false memory appears to have been a factor, this does not ease the process of distinguishing between false memory and real recall. Sound therapeutic strategy can help this differentiation, by either avoiding known controversial strategies or to disclosing controversy to a subject.

Harold Merskey published a paper on the ethical issues of recovered-memory therapy. He suggests that if a patient had pre-existing severe issues in their life, it is likely that "deterioration" will occur to a relatively severe extent upon memory recall. This deterioration is a physical parallel to the emotional trauma being surfaced. There may be tears, writhing, or many other forms of physical disturbance. The occurrence of physical deterioration in memory recall coming from a patient with relatively minor issues prior to therapy could be an indication of the recalled memory's potential falsehood.

Children

False memory is often considered for trauma victims including those of childhood sexual abuse.

If a child experienced abuse, it is not typical for them to disclose the details of the event when confronted in an open-ended manner. Trying to indirectly prompt a memory recall can lead to the conflict of source attribution, as if repeatedly questioned the child might try to recall a memory to satisfy a question. The stress being put on the child can make recovering an accurate memory more difficult. Some people hypothesise that as the child continuously attempts to remember a memory, they are building a larger file of sources that the memory could be derived from, potentially including sources other than genuine memories. Children that have never been abused but undergo similar response-eliciting techniques can disclose events that never occurred.

One of children's most notable setbacks in memory recall is source misattribution. Source misattribution is the flaw in deciphering between potential origins of a memory. The source could come from an actual occurring perception, or it can come from an induced and imagined event. Younger children, preschoolers in particular, find it more difficult to discriminate between the two. Lindsay & Johnson (1987) concluded that even children approaching adolescence struggle with this, as well as recalling an existent memory as a witness. Children are significantly more likely to confuse a source between being invented or existent.

For example, Shyamalan, Lamb and Sheldrick (1995) partially re-created a study that involved attempted memory implanting in children. The study comprised a series of interviews concerning a medical procedure that the children may have undergone. The data was scored so that if a child made one false affirmation during the interview, the child was classified as inaccurate. When the medical procedure was described in detail, "only 13% of the children answered 'yes' to the question 'Did you ever have this procedure?'". As to the success of implantation with false 'memories', the children "assented to the question for a variety of reasons, a false memory being only one of them. In sum, it is possible that no false memories have been created in children in implanted-memory studies".

Ethics and public opinion

A 2016 study surveyed the public's attitude regarding the ethics of planting false memories as an attempt to influence healthy behavior. People were most concerned with the consequences, with 37% saying it was overly manipulative, potentially harmful or traumatic. Their reasons against are that the ends do not justify the means (32%), potential for abuse (14%), lack of consent (10%), practical doubts (8%), better alternative (7%), and free will (3%). Of those who thought implanting false memories would be acceptable, 36% believed the end justified the means, with other reasons being increasing treatment options (6%), people need support (6%), no harm would be done (6%), and it's no worse than alternatives (5%). An article published in the journal entitled Applied Cognitive Psychology, indicated that the public has mixed sentiments about implanting false memories to improve eating habits, with 41% saying it would be generally unacceptable and 48% saying it would, 25% think it completely unethical while 10% believe the opposite.

Potential benefits

Several possible benefits associated with false memory arrive from fuzzy-trace theory and gist memory. Valerie F. Reyna, who coined the terms as an explanation for the DRM paradigm, explains that her findings indicate that reliance on prior knowledge from gist memory can help individuals make safer, well informed choices in terms of risk taking. Other positive traits associated with false memory indicate that individuals have superior organizational processes, heightened creativity, and prime solutions for insight based problems. All of these things indicate that false memories are adaptive and functional. False memories tied to familiar concepts can also potentially aid in future problem solving in a related topic, especially when related to survival.

 

Intelligent tutoring system

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An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher (e.g., classroom lectures), or no teacher at all (e.g., online homework). ITSs are often designed with the goal of providing access to high quality education to each and every student.

History

Early mechanical systems

Skinner teaching machine 08

The possibility of intelligent machines have been discussed for centuries. Blaise Pascal created the first calculating machine capable of mathematical functions in the 17th century simply called Pascal's Calculator. At this time the mathematician and philosopher Gottfried Wilhelm Leibniz envisioned machines capable of reasoning and applying rules of logic to settle disputes (Buchanan, 2006). These early works contributed to the development of the computer and future applications.

The concept of intelligent machines for instructional use date back as early as 1924, when Sidney Pressey of Ohio State University created a mechanical teaching machine to instruct students without a human teacher. His machine resembled closely a typewriter with several keys and a window that provided the learner with questions. The Pressey Machine allowed user input and provided immediate feedback by recording their score on a counter.

Pressey himself was influenced by Edward L. Thorndike, a learning theorist and educational psychologist at the Columbia University Teacher College of the late 19th and early 20th centuries. Thorndike posited laws for maximizing learning. Thorndike's laws included the law of effect, the law of exercise, and the law of recency. Following later standards, Pressey's teaching and testing machine would not be considered intelligent as it was mechanically run and was based on one question and answer at a time, but it set an early precedent for future projects. By the 1950s and 1960s, new perspectives on learning were emerging. Burrhus Frederic "B.F." Skinner at Harvard University did not agree with Thorndike's learning theory of connectionism or Pressey's teaching machine. Rather, Skinner was a behaviourist who believed that learners should construct their answers and not rely on recognition. He too, constructed a teaching machine structured using an incremental mechanical system that would reward students for correct responses to questions.

Early electronic systems

In the period following the second world war, mechanical binary systems gave way to binary based electronic machines. These machines were considered intelligent when compared to their mechanical counterparts as they had the capacity to make logical decisions. However, the study of defining and recognizing a machine intelligence was still in its infancy.

Alan Turing, a mathematician, logician and computer scientist, linked computing systems to thinking. One of his most notable papers outlined a hypothetical test to assess the intelligence of a machine which came to be known as the Turing test. Essentially, the test would have a person communicate with two other agents, a human and a computer asking questions to both recipients. The computer passes the test if it can respond in such a way that the human posing the questions cannot differentiate between the other human and the computer. The Turing test has been used in its essence for more than two decades as a model for current ITS development. The main ideal for ITS systems is to effectively communicate. As early as the 1950s programs were emerging displaying intelligent features. Turing's work as well as later projects by researchers such as Allen Newell, Clifford Shaw, and Herb Simon showed programs capable of creating logical proofs and theorems. Their program, The Logic Theorist exhibited complex symbol manipulation and even generation of new information without direct human control and is considered by some to be the first AI program. Such breakthroughs would inspire the new field of Artificial Intelligence officially named in 1956 by John McCarthy in 1956 at the Dartmouth Conference. This conference was the first of its kind that was devoted to scientists and research in the field of AI.

The PLATO V CAI terminal in 1981

The latter part of the 1960s and 1970s saw many new CAI (Computer-Assisted instruction) projects that built on advances in computer science. The creation of the ALGOL programming language in 1958 enabled many schools and universities to begin developing Computer Assisted Instruction (CAI) programs. Major computer vendors and federal agencies in the US such as IBM, HP, and the National Science Foundation funded the development of these projects. Early implementations in education focused on programmed instruction (PI), a structure based on a computerized input-output system. Although many supported this form of instruction, there was limited evidence supporting its effectiveness. The programming language LOGO was created in 1967 by Wally Feurzeig, Cynthia Solomon, and Seymour Papert as a language streamlined for education. PLATO, an educational terminal featuring displays, animations, and touch controls that could store and deliver large amounts of course material, was developed by Donald Bitzer in the University of Illinois in the early 1970s. Along with these, many other CAI projects were initiated in many countries including the US, the UK, and Canada.

At the same time that CAI was gaining interest, Jaime Carbonell suggested that computers could act as a teacher rather than just a tool (Carbonell, 1970). A new perspective would emerge that focused on the use of computers to intelligently coach students called Intelligent Computer Assisted Instruction or Intelligent Tutoring Systems (ITS). Where CAI used a behaviourist perspective on learning based on Skinner's theories (Dede & Swigger, 1988), ITS drew from work in cognitive psychology, computer science, and especially artificial intelligence. There was a shift in AI research at this time as systems moved from the logic focus of the previous decade to knowledge based systems—systems could make intelligent decisions based on prior knowledge (Buchanan, 2006). Such a program was created by Seymour Papert and Ira Goldstein who created Dendral, a system that predicted possible chemical structures from existing data. Further work began to showcase analogical reasoning and language processing. These changes with a focus on knowledge had big implications for how computers could be used in instruction. The technical requirements of ITS, however, proved to be higher and more complex than CAI systems and ITS systems would find limited success at this time.

Towards the latter part of the 1970s interest in CAI technologies began to wane. Computers were still expensive and not as available as expected. Developers and instructors were reacting negatively to the high cost of developing CAI programs, the inadequate provision for instructor training, and the lack of resources.

Microcomputers and intelligent systems

The microcomputer revolution in the late 1970s and early 1980s helped to revive CAI development and jumpstart development of ITS systems. Personal computers such as the Apple 2, Commodore PET, and TRS-80 reduced the resources required to own computers and by 1981, 50% of US schools were using computers (Chambers & Sprecher, 1983). Several CAI projects utilized the Apple 2 as a system to deliver CAI programs in high schools and universities including the British Columbia Project and California State University Project in 1981.

The early 1980s would also see Intelligent Computer-Assisted Instruction (ICAI) and ITS goals diverge from their roots in CAI. As CAI became increasingly focused on deeper interactions with content created for a specific area of interest, ITS sought to create systems that focused on knowledge of the task and the ability to generalize that knowledge in non-specific ways (Larkin & Chabay, 1992). The key goals set out for ITS were to be able to teach a task as well as perform it, adapting dynamically to its situation. In the transition from CAI to ICAI systems, the computer would have to distinguish not only between the correct and incorrect response but the type of incorrect response to adjust the type of instruction. Research in Artificial Intelligence and Cognitive Psychology fueled the new principles of ITS. Psychologists considered how a computer could solve problems and perform 'intelligent' activities. An ITS programme would have to be able to represent, store and retrieve knowledge and even search its own database to derive its own new knowledge to respond to learner's questions. Basically, early specifications for ITS or (ICAI) require it to "diagnose errors and tailor remediation based on the diagnosis" (Shute & Psotka, 1994, p. 9). The idea of diagnosis and remediation is still in use today when programming ITS.

A key breakthrough in ITS research was the creation of The LISP Tutor, a program that implemented ITS principles in a practical way and showed promising effects increasing student performance. The LISP Tutor was developed and researched in 1983 as an ITS system for teaching students the LISP programming language (Corbett & Anderson, 1992). The LISP Tutor could identify mistakes and provide constructive feedback to students while they were performing the exercise. The system was found to decrease the time required to complete the exercises while improving student test scores (Corbett & Anderson, 1992). Other ITS systems beginning to develop around this time include TUTOR created by Logica in 1984 as a general instructional tool and PARNASSUS created in Carnegie Mellon University in 1989 for language instruction.

Modern ITS

After the implementation of initial ITS, more researchers created a number of ITS for different students. In the late 20th century, Intelligent Tutoring Tools (ITTs) was developed by the Byzantium project, which involved six universities. The ITTs were general purpose tutoring system builders and many institutions had positive feedback while using them. (Kinshuk, 1996) This builder, ITT, would produce an Intelligent Tutoring Applet (ITA) for different subject areas. Different teachers created the ITAs and built up a large inventory of knowledge that was accessible by others through the Internet. Once an ITS was created, teachers could copy it and modify it for future use. This system was efficient and flexible. However, Kinshuk and Patel believed that the ITS was not designed from an educational point of view and was not developed based on the actual needs of students and teachers (Kinshuk and Patel, 1997).

 Recent work has employed ethnographic and design research methods to examine the ways ITSs are actually used by students and teachers across a range of contexts, often revealing unanticipated needs that they meet, fail to meet, or in some cases, even create.

Modern day ITSs typically try to replicate the role of a teacher or a teaching assistant, and increasingly automate pedagogical functions such as problem generation, problem selection, and feedback generation. However, given a current shift towards blended learning models, recent work on ITSs has begun focusing on ways these systems can effectively leverage the complementary strengths of human-led instruction from a teacher or peer, when used in co-located classrooms or other social contexts.

There were three ITS projects that functioned based on conversational dialogue: AutoTutor, Atlas (Freedman, 1999), and Why2. The idea behind these projects was that since students learn best by constructing knowledge themselves, the programs would begin with leading questions for the students and would give out answers as a last resort. AutoTutor's students focused on answering questions about computer technology, Atlas's students focused on solving quantitative problems, and Why2's students focused on explaining physical systems qualitatively. (Graesser, VanLehn, and others, 2001) Other similar tutoring systems such as Andes (Gertner, Conati, and VanLehn, 1998) tend to provide hints and immediate feedback for students when students have trouble answering the questions. They could guess their answers and have correct answers without deep understanding of the concepts. Research was done with a small group of students using Atlas and Andes respectively. The results showed that students using Atlas made significant improvements compared with students who used Andes. However, since the above systems require analysis of students' dialogues, improvement is yet to be made so that more complicated dialogues can be managed.

Structure

Intelligent tutoring systems (ITSs) consist of four basic components based on a general consensus amongst researchers (Nwana,1990; Freedman, 2000; Nkambou et al., 2010):

1. The Domain model
2. The Student model
3. The Tutoring model, and
4. The User interface model

The domain model (also known as the cognitive model or expert knowledge model) is built on a theory of learning, such as the ACT-R theory which tries to take into account all the possible steps required to solve a problem. More specifically, this model "contains the concepts, rules, and problem-solving strategies of the domain to be learned. It can fulfill several roles: as a source of expert knowledge, a standard for evaluating the student's performance or for detecting errors, etc." (Nkambou et al., 2010, p. 4). Another approach for developing domain models is based on Stellan Ohlsson's Theory of Learning from performance errors, known as constraint-based modelling (CBM). In this case, the domain model is presented as a set of constraints on correct solutions.

The student model can be thought of as an overlay on the domain model. It is considered as the core component of an ITS paying special attention to student's cognitive and affective states and their evolution as the learning process advances. As the student works step-by-step through their problem solving process, an ITS engages in a process called model tracing. Anytime the student model deviates from the domain model, the system identifies, or flags, that an error has occurred. On the other hand, in constraint-based tutors the student model is represented as an overlay on the constraint set. Constraint-based tutors evaluate the student's solution against the constraint set, and identify satisfied and violated constraints. If there are any violated constraints, the student's solution is incorrect, and the ITS provides feedback on those constraints. Constraint-based tutors provide negative feedback (i.e. feedback on errors) and also positive feedback.

The tutor model accepts information from the domain and student models and makes choices about tutoring strategies and actions. At any point in the problem-solving process the learner may request guidance on what to do next, relative to their current location in the model. In addition, the system recognizes when the learner has deviated from the production rules of the model and provides timely feedback for the learner, resulting in a shorter period of time to reach proficiency with the targeted skills. The tutor model may contain several hundred production rules that can be said to exist in one of two states, learned or unlearned. Every time a student successfully applies a rule to a problem, the system updates a probability estimate that the student has learned the rule. The system continues to drill students on exercises that require effective application of a rule until the probability that the rule has been learned reaches at least 95% probability.

Knowledge tracing tracks the learner's progress from problem to problem and builds a profile of strengths and weaknesses relative to the production rules. The cognitive tutoring system developed by John Anderson at Carnegie Mellon University presents information from knowledge tracing as a skillometer, a visual graph of the learner's success in each of the monitored skills related to solving algebra problems. When a learner requests a hint, or an error is flagged, the knowledge tracing data and the skillometer are updated in real-time.

The user interface component "integrates three types of information that are needed in carrying out a dialogue: knowledge about patterns of interpretation (to understand a speaker) and action (to generate utterances) within dialogues; domain knowledge needed for communicating content; and knowledge needed for communicating intent" (Padayachee, 2002, p. 3).

Nkambou et al. (2010) make mention of Nwana's (1990) review of different architectures underlining a strong link between architecture and paradigm (or philosophy). Nwana (1990) declares, "[I]t is almost a rarity to find two ITSs based on the same architecture [which] results from the experimental nature of the work in the area" (p. 258). He further explains that differing tutoring philosophies emphasize different components of the learning process (i.e., domain, student or tutor). The architectural design of an ITS reflects this emphasis, and this leads to a variety of architectures, none of which, individually, can support all tutoring strategies (Nwana, 1990, as cited in Nkambou et al., 2010). Moreover, ITS projects may vary according to the relative level of intelligence of the components. As an example, a project highlighting intelligence in the domain model may generate solutions to complex and novel problems so that students can always have new problems to work on, but it might only have simple methods for teaching those problems, while a system that concentrates on multiple or novel ways of teaching a particular topic might find a less sophisticated representation of that content sufficient.

Design and development methods

Apart from the discrepancy amongst ITS architectures each emphasizing different elements, the development of an ITS is much the same as any instructional design process. Corbett et al. (1997) summarized ITS design and development as consisting of four iterative stages: (1) needs assessment, (2) cognitive task analysis, (3) initial tutor implementation and (4) evaluation.

The first stage known as needs assessment is common to any instructional design process, especially software development. This involves a learner analysis, consultation with subject matter experts and/or the instructor(s). This first step is part of the development of the expert/knowledge and student domain. The goal is to specify learning goals and to outline a general plan for the curriculum; it is imperative not to computerize traditional concepts but develop a new curriculum structure by defining the task in general and understanding learners' possible behaviours dealing with the task and to a lesser degree the tutor's behavior. In doing so, three crucial dimensions need to be dealt with: (1) the probability a student is able to solve problems; (2) the time it takes to reach this performance level and (3) the probability the student will actively use this knowledge in the future. Another important aspect that requires analysis is cost effectiveness of the interface. Moreover, teachers and student entry characteristics such as prior knowledge must be assessed since both groups are going to be system users.^[41]

The second stage, cognitive task analysis, is a detailed approach to expert systems programming with the goal of developing a valid computational model of the required problem solving knowledge. Chief methods for developing a domain model include: (1) interviewing domain experts, (2) conducting "think aloud" protocol studies with domain experts, (3) conducting "think aloud" studies with novices and (4) observation of teaching and learning behavior. Although the first method is most commonly used, experts are usually incapable of reporting cognitive components. The "think aloud" methods, in which the experts is asked to report aloud what s/he is thinking when solving typical problems, can avoid this problem. Observation of actual online interactions between tutors and students provides information related to the processes used in problem-solving, which is useful for building dialogue or interactivity into tutoring systems.

The third stage, initial tutor implementation, involves setting up a problem solving environment to enable and support an authentic learning process. This stage is followed by a series of evaluation activities as the final stage which is again similar to any software development project.

The fourth stage, evaluation includes (1) pilot studies to confirm basic usability and educational impact; (2) formative evaluations of the system under development, including (3) parametric studies that examine the effectiveness of system features and finally, (4) summative evaluations of the final tutor's effect: learning rate and asymptotic achievement levels.

A variety of authoring tools have been developed to support this process and create intelligent tutors, including ASPIRE, the Cognitive Tutor Authoring Tools (CTAT), GIFT, ASSISTments Builder and AutoTutor tools. The goal of most of these authoring tools is to simplify the tutor development process, making it possible for people with less expertise than professional AI programmers to develop Intelligent Tutoring Systems.

Eight principles of ITS design and development

Anderson et al. (1987) outlined eight principles for intelligent tutor design and Corbett et al. (1997) later elaborated on those principles highlighting an all-embracing principle which they believed governed intelligent tutor design, they referred to this principle as:

Principle 0: An intelligent tutor system should enable the student to work to the successful conclusion of problem solving.

1. Represent student competence as a production set.
2. Communicate the goal structure underlying the problem solving.
3. Provide instruction in the problem solving context.
4. Promote an abstract understanding of the problem-solving knowledge.
5. Minimize working memory load.
6. Provide immediate feedback on errors.
7. Adjust the grain size of instruction with learning.
8. Facilitate successive approximations to the target skill.

Use in practice

All this is a substantial amount of work, even if authoring tools have become available to ease the task. This means that building an ITS is an option only in situations in which they, in spite of their relatively high development costs, still reduce the overall costs through reducing the need for human instructors or sufficiently boosting overall productivity. Such situations occur when large groups need to be tutored simultaneously or many replicated tutoring efforts are needed. Cases in point are technical training situations such as training of military recruits and high school mathematics. One specific type of intelligent tutoring system, the Cognitive Tutor, has been incorporated into mathematics curricula in a substantial number of United States high schools, producing improved student learning outcomes on final exams and standardized tests. Intelligent tutoring systems have been constructed to help students learn geography, circuits, medical diagnosis, computer programming, mathematics, physics, genetics, chemistry, etc. Intelligent Language Tutoring Systems (ILTS), e.g. this one, teach natural language to first or second language learners. ILTS requires specialized natural language processing tools such as large dictionaries and morphological and grammatical analyzers with acceptable coverage.

Applications

During the rapid expansion of the web boom, new computer-aided instruction paradigms, such as e-learning and distributed learning, provided an excellent platform for ITS ideas. Areas that have used ITS include natural language processing, machine learning, planning, multi-agent systems, ontologies, semantic Web, and social and emotional computing. In addition, other technologies such as multimedia, object-oriented systems, modeling, simulation, and statistics have also been connected to or combined with ITS. Historically non-technological areas such as the educational sciences and psychology have also been influenced by the success of ITS.

In recent years, ITS has begun to move away from the search-based to include a range of practical applications. ITS have expanded across many critical and complex cognitive domains, and the results have been far reaching. ITS systems have cemented a place within formal education and these systems have found homes in the sphere of corporate training and organizational learning. ITS offers learners several affordances such as individualized learning, just in time feedback, and flexibility in time and space.

While Intelligent tutoring systems evolved from research in cognitive psychology and artificial intelligence, there are now many applications found in education and in organizations. Intelligent tutoring systems can be found in online environments or in a traditional classroom computer lab, and are used in K-12 classrooms as well as in universities. There are a number of programs that target mathematics but applications can be found in health sciences, language acquisition, and other areas of formalized learning.

Reports of improvement in student comprehension, engagement, attitude, motivation, and academic results have all contributed to the ongoing interest in the investment in and research of theses systems. The personalized nature of the intelligent tutoring systems affords educators the opportunity to create individualized programs. Within education there are a plethora of intelligent tutoring systems, an exhaustive list does not exist but several of the more influential programs are listed below.

Education

Algebra Tutor PAT (PUMP Algebra Tutor or Practical Algebra Tutor) developed by the Pittsburgh Advanced Cognitive Tutor Center at Carnegie Mellon University, engages students in anchored learning problems and uses modern algebraic tools in order to engage students in problem solving and in sharing of their results. The aim of PAT is to tap into a students' prior knowledge and everyday experiences with mathematics in order to promote growth. The success of PAT is well documented (ex. Miami-Dade County Public Schools Office of Evaluation and Research) from both a statistical (student results) and emotional (student and instructor feedback) perspective.

SQL-Tutor is the first ever constraint-based tutor developed by the Intelligent Computer Tutoring Group (ICTG) at the University of Canterbury, New Zealand. SQL-Tutor teaches students how to retrieve data from databases using the SQL SELECT statement.

EER-Tutor is a constraint-based tutor (developed by ICTG) that teaches conceptual database design using the Entity Relationship model. An earlier version of EER-Tutor was KERMIT, a stand-alone tutor for ER modelling, whjich was shown to results in significant improvement of student's knowledge after one hour of learning (with the effect size of 0.6).

COLLECT-UML is a constraint-based tutor that supports pairs of students working collaboratively on UML class diagrams. The tutor provides feedback on the domain level as well as on collaboration.

StoichTutor is a web-based intelligent tutor that helps high school students learn chemistry, specifically the sub-area of chemistry known as stoichiometry. It has been used to explore a variety of learning science principles and techniques, such as worked examples and politeness.

Mathematics Tutor The Mathematics Tutor (Beal, Beck & Woolf, 1998) helps students solve word problems using fractions, decimals and percentages. The tutor records the success rates while a student is working on problems while providing subsequent, lever-appropriate problems for the student to work on. The subsequent problems that are selected are based on student ability and a desirable time in is estimated in which the student is to solve the problem.

eTeacher eTeacher (Schiaffino et al., 2008) is an intelligent agent or pedagogical agent, that supports personalized e-learning assistance. It builds student profiles while observing student performance in online courses. eTeacher then uses the information from the student's performance to suggest a personalized courses of action designed to assist their learning process.

ZOSMAT ZOSMAT was designed to address all the needs of a real classroom. It follows and guides a student in different stages of their learning process. This is a student-centered ITS does this by recording the progress in a student's learning and the student program changes based on the student's effort. ZOSMAT can be used for either individual learning or in a real classroom environment alongside the guidance of a human tutor.

REALP REALP was designed to help students enhance their reading comprehension by providing reader-specific lexical practice and offering personalized practice with useful, authentic reading materials gathered from the Web. The system automatically build a user model according to student's performance. After reading, the student is given a series of exercises based on the target vocabulary found in reading.

CIRCSlM-Tutor CIRCSIM_Tutor is an intelligent tutoring system that is used with first year medical students at the Illinois Institute of Technology. It uses natural dialogue based, Socratic language to help students learn about regulating blood pressure.

Why2-Atlas Why2-Atlas is an ITS that analyses students explanations of physics principles. The students input their work in paragraph form and the program converts their words into a proof by making assumptions of student beliefs that are based on their explanations. In doing this, misconceptions and incomplete explanations are highlighted. The system then addresses these issues through a dialogue with the student and asks the student to correct their essay. A number of iterations may take place before the process is complete.

SmartTutor The University of Hong Kong (HKU) developed a SmartTutor to support the needs of continuing education students. Personalized learning was identified as a key need within adult education at HKU and SmartTutor aims to fill that need. SmartTutor provides support for students by combining Internet technology, educational research and artificial intelligence.

AutoTutor AutoTutor assists college students in learning about computer hardware, operating systems and the Internet in an introductory computer literacy course by simulating the discourse patterns and pedagogical strategies of a human tutor. AutoTutor attempts to understand learner's input from the keyboard and then formulate dialog moves with feedback, prompts, correction and hints.

ActiveMath ActiveMath is a web-based, adaptive learning environment for mathematics. This system strives for improving long-distance learning, for complementing traditional classroom teaching, and for supporting individual and lifelong learning.

ESC101-ITS The Indian Institute of Technology, Kanpur, India developed the ESC101-ITS, an intelligent tutoring system for introductory programming problems.

AdaptErrEx is an adaptive intelligent tutor that uses interactive erroneous examples to help students learn decimal arithmetic.

Corporate training and industry

Generalized Intelligent Framework for Tutoring (GIFT) is an educational software designed for creation of computer-based tutoring systems. Developed by the U.S. Army Research Laboratory from 2009 to 2011, GIFT was released for commercial use in May 2012. GIFT is open-source and domain independent, and can be downloaded online for free. The software allows an instructor to design a tutoring program that can cover various disciplines through adjustments to existing courses. It includes coursework tools intended for use by researchers, instructional designers, instructors, and students. GIFT is compatible with other teaching materials, such as PowerPoint presentations, which can be integrated into the program.

SHERLOCK "SHERLOCK" is used to train Air Force technicians to diagnose problems in the electrical systems of F-15 jets. The ITS creates faulty schematic diagrams of systems for the trainee to locate and diagnose. The ITS provides diagnostic readings allowing the trainee to decide whether the fault lies in the circuit being tested or if it lies elsewhere in the system. Feedback and guidance are provided by the system and help is available if requested.

Cardiac Tutor The Cardiac Tutor's aim is to support advanced cardiac support techniques to medical personnel. The tutor presents cardiac problems and, using a variety of steps, students must select various interventions. Cardiac Tutor provides clues, verbal advice, and feedback in order to personalize and optimize the learning. Each simulation, regardless of whether the students were successfully able to help their patients, results in a detailed report which students then review.

CODES Cooperative Music Prototype Design is a Web-based environment for cooperative music prototyping. It was designed to support users, especially those who are not specialists in music, in creating musical pieces in a prototyping manner. The musical examples (prototypes) can be repeatedly tested, played and modified. One of the main aspects of CODES is interaction and cooperation between the music creators and their partners.

Effectiveness

Assessing the effectiveness of ITS programs is problematic. ITS vary greatly in design, implementation, and educational focus. When ITS are used in a classroom, the system is not only used by students, but by teachers as well. This usage can create barriers to effective evaluation for a number of reasons; most notably due to teacher intervention in student learning.

Teachers often have the ability to enter new problems into the system or adjust the curriculum. In addition, teachers and peers often interact with students while they learn with ITSs (e.g., during an individual computer lab session or during classroom lectures falling in between lab sessions) in ways that may influence their learning with the software. Prior work suggests that the vast majority of students' help-seeking behavior in classrooms using ITSs may occur entirely outside of the software - meaning that the nature and quality of peer and teacher feedback in a given class may be an important mediator of student learning in these contexts. In addition, aspects of classroom climate, such as students' overall level of comfort in publicly asking for help, or the degree to which a teacher is physically active in monitoring individual students may add additional sources of variation across evaluation contexts. All of these variables make evaluation of an ITS complex, and may help explain variation in results across evaluation studies.

Despite the inherent complexities, numerous studies have attempted to measure the overall effectiveness of ITS, often by comparisons of ITS to human tutors. Reviews of early ITS systems (1995) showed an effect size of d = 1.0 in comparison to no tutoring, where as human tutors were given an effect size of d = 2.0. Kurt VanLehn's much more recent overview (2011) of modern ITS found that there was no statistical difference in effect size between expert one-on-one human tutors and step-based ITS. Some individual ITS have been evaluated more positively than others. Studies of the Algebra Cognitive Tutor found that the ITS students outperformed students taught by a classroom teacher on standardized test problems and real-world problem solving tasks. Subsequent studies found that these results were particularly pronounced in students from special education, non-native English, and low-income backgrounds.

A more recent meta-analysis suggests that ITSs can exceed the effectiveness of both CAI and human tutors, especially when measured by local (specific) tests as opposed to standardized tests. "Students who received intelligent tutoring outperformed students from conventional classes in 46 (or 92%) of the 50 controlled evaluations, and the improvement in performance was great enough to be considered of substantive importance in 39 (or 78%) of the 50 studies. The median ES in the 50 studies was 0.66, which is considered a moderate-to-large effect for studies in the social sciences. It is roughly equivalent to an improvement in test performance from the 50th to the 75th percentile. This is stronger than typical effects from other forms of tutoring. C.-L. C. Kulik and Kulik’s (1991) meta-analysis, for example, found an average ES of 0.31 in 165 studies of CAI tutoring. ITS gains are about twice as high. The ITS effect is also greater than typical effects from human tutoring. As we have seen, programs of human tutoring typically raise student test scores about 0.4 standard deviations over control levels. Developers of ITSs long ago set out to improve on the success of CAI tutoring and to match the success of human tutoring. Our results suggest that ITS developers have already met both of these goals.... Although effects were moderate to strong in evaluations that measured outcomes on locally developed tests, they were much smaller in evaluations that measured outcomes on standardized tests. Average ES on studies with local tests was 0.73; average ES on studies with standardized tests was 0.13. This discrepancy is not unusual for meta-analyses that include both local and standardized tests... local tests are likely to align well with the objectives of specific instructional programs. Off-the-shelf standardized tests provide a looser fit. ... Our own belief is that both local and standardized tests provide important information about instructional effectiveness, and when possible, both types of tests should be included in evaluation studies."

Some recognized strengths of ITS are their ability to provide immediate yes/no feedback, individual task selection, on-demand hints, and support mastery learning.

Limitations

Intelligent tutoring systems are expensive both to develop and implement. The research phase paves the way for the development of systems that are commercially viable. However, the research phase is often expensive; it requires the cooperation and input of subject matter experts, the cooperation and support of individuals across both organizations and organizational levels. Another limitation in the development phase is the conceptualization and the development of software within both budget and time constraints. There are also factors that limit the incorporation of intelligent tutors into the real world, including the long timeframe required for development and the high cost of the creation of the system components. A high portion of that cost is a result of content component building. For instance, surveys revealed that encoding an hour of online instruction time took 300 hours of development time for tutoring content. Similarly, building the Cognitive Tutor took a ratio of development time to instruction time of at least 200:1 hours. The high cost of development often eclipses replicating the efforts for real world application. Intelligent tutoring systems are not, in general, commercially feasible for real-world applications.

A criticism of Intelligent Tutoring Systems currently in use, is the pedagogy of immediate feedback and hint sequences that are built in to make the system "intelligent". This pedagogy is criticized for its failure to develop deep learning in students. When students are given control over the ability to receive hints, the learning response created is negative. Some students immediately turn to the hints before attempting to solve the problem or complete the task. When it is possible to do so, some students bottom out the hints – receiving as many hints as possible as fast as possible – in order to complete the task faster. If students fail to reflect on the tutoring system's feedback or hints, and instead increase guessing until positive feedback is garnered, the student is, in effect, learning to do the right thing for the wrong reasons. Most tutoring systems are currently unable to detect shallow learning, or to distinguish between productive versus unproductive struggle (though see, e.g.). For these and many other reasons (e.g., overfitting of underlying models to particular user populations), the effectiveness of these systems may differ significantly across users.

Another criticism of intelligent tutoring systems is the failure of the system to ask questions of the students to explain their actions. If the student is not learning the domain language than it becomes more difficult to gain a deeper understanding, to work collaboratively in groups, and to transfer the domain language to writing. For example, if the student is not "talking science" than it is argued that they are not being immersed in the culture of science, making it difficult to undertake scientific writing or participate in collaborative team efforts. Intelligent tutoring systems have been criticized for being too "instructivist" and removing intrinsic motivation, social learning contexts, and context realism from learning.

Practical concerns, in terms of the inclination of the sponsors/authorities and the users to adapt intelligent tutoring systems, should be taken into account. First, someone must have a willingness to implement the ITS. Additionally an authority must recognize the necessity to integrate an intelligent tutoring software into current curriculum and finally, the sponsor or authority must offer the needed support through the stages of the system development until it is completed and implemented.

Evaluation of an intelligent tutoring system is an important phase; however, it is often difficult, costly, and time consuming.hough there are various evaluation techniques presented in the literature, there are no guiding principles for the selection of appropriate evaluation method(s) to be used in a particular context. Careful inspection should be undertaken to ensure that a complex system does what it claims to do. This assessment may occur during the design and early development of the system to identify problems and to guide modifications (i.e. formative evaluation). In contrast, the evaluation may occur after the completion of the system to support formal claims about the construction, behaviour of, or outcomes associated with a completed system (i.e. summative evaluation). The great challenge introduced by the lack of evaluation standards resulted in neglecting the evaluation stage in several existing ITS'.

Improvements

Intelligent tutoring systems are less capable than human tutors in the areas of dialogue and feedback. For example, human tutors are able to interpret the affective state of the student, and potentially adapt instruction in response to these perceptions. Recent work is exploring potential strategies for overcoming these limitations of ITSs, to make them more effective.

Dialogue

Human tutors have the ability to understand a person's tone and inflection within a dialogue and interpret this to provide continual feedback through an ongoing dialogue. Intelligent tutoring systems are now being developed to attempt to simulate natural conversations. To get the full experience of dialogue there are many different areas in which a computer must be programmed; including being able to understand tone, inflection, body language, and facial expression and then to respond to these. Dialogue in an ITS can be used to ask specific questions to help guide students and elicit information while allowing students to construct their own knowledge. The development of more sophisticated dialogue within an ITS has been a focus in some current research partially to address the limitations and create a more constructivist approach to ITS. In addition, some current research has focused on modeling the nature and effects of various social cues commonly employed within a dialogue by human tutors and tutees, in order to build trust and rapport (which have been shown to have positive impacts on student learning).

Emotional affect

A growing body of work is considering the role of affect on learning, with the objective of developing intelligent tutoring systems that can interpret and adapt to the different emotional states. Humans do not just use cognitive processes in learning but the affective processes they go through also plays an important role. For example, learners learn better when they have a certain level of disequilibrium (frustration), but not enough to make the learner feel completely overwhelmed. This has motivated affective computing to begin to produce and research creating intelligent tutoring systems that can interpret the affective process of an individual. An ITS can be developed to read an individual's expressions and other signs of affect in an attempt to find and tutor to the optimal affective state for learning. There are many complications in doing this since affect is not expressed in just one way but in multiple ways so that for an ITS to be effective in interpreting affective states it may require a multimodal approach (tone, facial expression, etc...). These ideas have created a new field within ITS, that of Affective Tutoring Systems (ATS). One example of an ITS that addresses affect is Gaze Tutor which was developed to track students eye movements and determine whether they are bored or distracted and then the system attempts to reengage the student.

Rapport Building

To date, most ITSs have focused purely on the cognitive aspects of tutoring and not on the social relationship between the tutoring system and the student. As demonstrated by the Computers are social actors paradigm humans often project social heuristics onto computers. For example in observations of young children interacting with Sam the CastleMate, a collaborative story telling agent, children interacted with this simulated child in much the same manner as they would a human child. It has been suggested that to effectively design an ITS that builds rapport with students, the ITS should mimic strategies of instructional immediacy, behaviors which bridge the apparent social distance between students and teachers such as smiling and addressing students by name. With regard to teenagers, Ogan et. al draw from observations of close friends tutoring each other to argue that in order for an ITS to build rapport as a peer to a student, a more involved process of trust building is likely necessary which may ultimately require that the tutoring system possess the capability to effectively respond to and even produce seemingly rude behavior in order to mediate motivational and affective student factors through playful joking and taunting.

Teachable Agents

Traditionally ITSs take on the role of autonomous tutors, however they can also take on the role of tutees for the purpose of learning by teaching exercises. Evidence suggests that learning by teaching can be an effective strategy for mediating self-explanation, improving feelings of self-efficacy, and boosting educational outcomes and retention. In order to replicate this effect the roles of the student and ITS can be switched. This can be achieved by designing the ITS to have the appearance of being taught as is the case in the Teachable Agent Arithmetic Game  and Betty's Brain. Another approach is to have students teach a machine learning agent which can learn to solve problems by demonstration and correctness feedback as is the case in the APLUS system built with SimStudent.  In order to replicate the educational effects of learning by teaching teachable agents generally have a social agent built on top of them which poses questions or conveys confusion. For example Betty from Betty's Brain will prompt the student to ask her questions to make sure that she understands the material, and Stacy from APLUS will prompt the user for explanations of the feedback provided by the student.

Related conferences

Several conferences regularly consider papers on intelligent tutoring systems. The oldest is The International Conference on Intelligent Tutoring Systems, which started in 1988 and is now held every other year. The International Artificial Intelligence in Education (AIED) Society publishes The International Journal of Artificial Intelligence in Education (IJAIED) and organizes the annual International Conference on Artificial Intelligence in Education (http://iaied.org/conf/1/) started in 1989. Many papers on intelligent tutoring systems also appear at International Conference on User Modeling, Adaptation, and Personalization, and International Conference on Educational Data Mining. The American Association of Artificial Intelligence (AAAI) will sometimes have symposia and papers related to intelligent tutoring systems. A number of books have been written on ITS including three published by Lawrence Erlbaum Associates.