Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan, Ali; Kalita, Jugal

doi:10.5120/ijca2019918451

Cited by 50 publications

(31 citation statements)

References 93 publications

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“…'AI in education' has existed as a coherent academic research field since at least the 1980s, as marked by the first publication of the International Journal of Artificial Intelligence in Education in 1989 and the formation of the International AI in Education Society (IAIED) in 1993, although it was preceded by development of Intelligent Tutoring Systems Computer Assisted Instruction systems in the 1960s and 70s (Alkhatlan and Kalita 2018;Selwyn 2019). As a field AIed has developed along two complementary strands of activity: the development of AI-based tools for classrooms, and the use of AI to understand, measure and improve learning (Holmes, Bialik, and Fadel 2019).…”

Section: Aied Researchmentioning

confidence: 99%

Historical threads, missing links, and future directions in AI in education

Williamson

Eynon

2020

Learning, Media and Technology

149

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Artificial intelligence has become a routine presence in everyday life. Accessing information over the Web, consuming news and entertainment, the performance of financial markets, the ways surveillance systems identify individuals, how drivers and pedestrians navigate, and how citizens receive welfare payments are among myriad examples of how AI has penetrated into human lives, social institutions, cultural practices, and political and economic processes. The effects of the algorithmic techniques employed to enable AI are far-reaching and have inspired considerable epochal hype and hope, as well as dystopian dread, although they remain largely opaque and weakly understood outside of the social networks of technical experts (Rieder 2020). The profound social and ethical implications of AI, however, are becoming increasingly apparent and the objects of significant critical attention. AI is at the centre of controversies concerning, for example, automation in workplaces and public services; algorithmic forms of bias and discrimination; automated reproduction of inequalities and disadvantage; regimes of data-centred surveillance and algorithmic profiling; disregard of data protections and privacy; political and commercial micro targeting; and the power of technology corporations to control and shape all sectors and spaces they penetrate, from whole cities and citizen populations to specific collectives, individuals or even human bodies (Whittaker et al. 2018). Numerous ethical frameworks and professional codes of conduct have been developed to attempt to mitigate the potential dangers and risks of AI in society, though important debates persist about their concrete effects on companies or the way such frameworks and codes may serve to protect commercial interests (Greene, Hoffman, and Stark 2019). The current instantiation of AI on the Web, on smartphones, in social media, and in spaces via interconnected objects and sensor networks has a much longer history than some recent epochal claims would suggest. Histories of AI stretch back at least as far as the birth of computer science and cybernetics in the 1940s. The term 'artificial intelligence' itself was coined as part of a project and workshop at Dartmouth College in the mid-1950s. From the 1960s to the 90s, punctuated by periods of 'AI winter', AI research and development focused first on encoding principles of human reasoning to simulate human intelligence, and then on 'expert systems' that emulated the procedural decision-making processes of experts based on defined knowledge bases. After 2010, AI gradually returned under a new paradigm, not as simulated human intelligences or programmable expert systems but as data-processing systems that can learn and make predictions from classifying and correlating huge quantities of 'big data'. Computational processes including data analytics, machine learning, neural networks, deep learning and reinforcement learning underpin most contemporary forms of AI. AI is, perhaps, just a new catch-all name for a range of statistica...

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Section: Aied Researchmentioning

confidence: 99%

Historical threads, missing links, and future directions in AI in education

Williamson

Eynon

2020

Learning, Media and Technology

149

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“…Adaptive learning is a core part for ITS [1], which is different from traditional education style (e.g. learning in classroom and presenting same learning materials to all learners).…”

Section: Introductionmentioning

confidence: 99%

Curriculum-Oriented Multi-goal Agent for Adaptive Learning

Zhang

et al. 2021

Communications in Computer and Information Science

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Adaptive learning is an important part of Intelligent Tutoring System (ITS). Given that students have different learning targets and knowledge concepts proficiency, a smart intelligent tutor should be able to provide personalized learning materials to them, and help students master target knowledge and skills with learning materials as less as possible. Reinforcement Learning (RL) algorithms are good at solving sequence decision problems, so they are widely used in learning material recommendation. However, the existing intelligent tutoring systems based on reinforcement learning usually consider only one learning target. Moreover, the agent needs to learn in the case of sparse rewards, resulting in inefficient learning. To this end, we propose a curriculumoriented multi-goal reinforcement learning method, which combines an off-policy RL algorithm with Hindsight Experience Replay (HER) to enable the agent to learn from past failed experiences to alleviate the problem of sparse rewards. Besides, our method is applicable to the case of multi-goal learning, and the agent learns specific strategy for each goal. Additionally, according to different learning stages of the agent, we set different learning pseudo goals adaptively for it to accelerate learning speed.

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“…In the literature, such systems are well known as intelligent tutoring systems (ITSs) and have been an active field of research in recent years. Alkhatlan and Kalita (2018) conduct a comprehensive historical survey for ITSs.…”

Section: Introductionmentioning

confidence: 99%

“…These four elements of a traditional architecture of ITSs could be found with different names, but they used for the same purposes. Specifically, Alkhatlan and Kalita (2018) refer that an ITS consists of four basic components: a domain model, a student model, a tutor model, and a user interface model. The student model contains basic information for the student, and this is the reason that the student model is a basic component of an ITS (Sarrafzadeh et al., 2008).…”

Section: Introductionmentioning

confidence: 99%

“…The student model contains basic information for the student, and this is the reason that the student model is a basic component of an ITS (Sarrafzadeh et al., 2008). The domain model stores the domain knowledge, and the tutor model stores pedagogical knowledge and is responsible to decide when and how to intervene (Alkhatlan & Kalita, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Studying Affective Tutoring Systems for Mathematical Concepts

Mastorodimos

Chatzichristofis

2019

Journal of Educational Technology Systems

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Students face difficulties in learning mathematical processes. As a result, they have negative emotions toward mathematics. The use of technology is employed to change the student’s attitude toward mathematics. Some methods utilize intelligent tutoring systems to recognize student’s emotional state and adapt the learning process accordingly. These systems, known as affective tutoring systems (ATSs), sense the emotional state of a student and then intelligently attempt to suggest appropriate strategies that can guide the learning process and ultimately shift the negative attitude of students toward mathematical learning. This article presents a survey of ATSs, which teach mathematic content, and tries to find common elements among them. It examines the kind and the number of student’s emotions that can be recognized and the strategies and methods that these ATSs use to recognize student’s emotional state. There are findings that agree with other studies about the recognized emotions and the methods that are used.

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Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Cited by 50 publications

References 93 publications

Historical threads, missing links, and future directions in AI in education

Historical threads, missing links, and future directions in AI in education

Curriculum-Oriented Multi-goal Agent for Adaptive Learning

Studying Affective Tutoring Systems for Mathematical Concepts

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