Idiographic artificial intelligence to explain students' self-regulation: Toward precision education

Saqr, Mohammed; Cheng, Rongxin; López-Pernas, Sonsoles; Beck, Emorie D

doi:10.1016/j.lindif.2024.102499

Learning and Individual Differences

2024

DOI: 10.1016/j.lindif.2024.102499

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Idiographic artificial intelligence to explain students' self-regulation: Toward precision education

Mohammed Saqr,

Rongxin Cheng,

Sonsoles López-Pernas

et al.

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2024

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Cited by 2 publications

References 72 publications

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Why explainable AI may not be enough: predictions and mispredictions in decision making in education

Saqr,

López-Pernas

2024

Smart Learn. Environ.

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In learning analytics and in education at large, AI explanations are always computed from aggregate data of all the students to offer the “average” picture. Whereas the average may work for most students, it does not reflect or capture the individual differences or the variability among students. Therefore, instance-level predictions—where explanations for each particular student are presented according to their own data—may help understand how and why predictions were estimated and how a student or teacher may act or make decisions. This study aims to examine the utility of individualized instance-level AI, its value in informing decision-making, and—more importantly—how they can be used to offer personalized feedback. Furthermore, the study examines mispredictions, their explanations and how they offer explanations or affect decision making. Using data from a full course with 126 students, five ML algorithms were implemented with explanatory mechanisms, compared and the best performing algorithm (Random Forest) was therefore selected. The results show that AI explanations, while useful, cannot achieve their full potential without a nuanced human involvement (i.e., hybrid human AI collaboration). Instance-level explainability may allow us to understand individual algorithmic decisions but may not very helpful for personalization or individualized support. In case of mispredictions, the explanations show that algorithms decide based on the “wrong predictors” which underscores the fact that a full data-driven approach cannot be fully trusted with generating plausible recommendations completely on its own and may require human assistance.

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Why explainable AI may not be enough: predictions and mispredictions in decision making in education

Saqr,

López-Pernas

2024

Smart Learn. Environ.

View full text Add to dashboard Cite

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A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda

Mustafa,

Tlili,

Lampropoulos

et al. 2024

Smart Learn. Environ.

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Despite the increased adoption of Artificial Intelligence in Education (AIED), several concerns are still associated with it. This has motivated researchers to conduct (systematic) reviews aiming at synthesizing the AIED findings in the literature. However, these AIED reviews are diversified in terms of focus, stakeholders, educational level and region, and so on. This has made the understanding of the overall landscape of AIED challenging. To address this research gap, this study proceeds one step forward by systematically meta-synthesizing the AIED literature reviews. Specifically, 143 literature reviews were included and analyzed according to the technology-based learning model. It is worth noting that most of the AIED research has been from China and the U.S. Additionally, when discussing AIED, strong focus was on higher education, where less attention is paid to special education. The results also reveal that AI is used mostly to support teachers and students in education with less focus on other educational stakeholders (e.g. school leaders or administrators). The study provides a possible roadmap for future research agenda on AIED, facilitating the implementation of effective and safe AIED.

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Idiographic artificial intelligence to explain students' self-regulation: Toward precision education

Cited by 2 publications

References 72 publications

Why explainable AI may not be enough: predictions and mispredictions in decision making in education

Why explainable AI may not be enough: predictions and mispredictions in decision making in education

A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda

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