Supporting student self-learning using generative AI

With recent advancements in language-generating AI, students increasingly depend on AI for problem-solving in their coursework. However, over-reliance on AI risks depriving students of opportunities for self-learning, which is essential in education. One of the considerable reasons students turn to AI is their inability to solve problems independently due to a lack of understanding. To address this, we propose an interactive system that guides students struggling with problem-solving by giving them hints. When the system gets students’ answers, it assesses the answer by breaking down problems into small parts, then evaluating each part, and finally, explaining the grading rationale. Based on this assessment, the system offers targeted suggestions for improvement generated by language generation AI with pre-prepared templates. These hints guide students without providing direct answers, fostering class understanding and problem-solving skills.

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Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1145/3702163.3702177
Citations
1
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
0.35

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article

Supporting student self-learning using generative AI

Yuki Ito, Qiang Ma
1 citations
Intelligent Tutoring Systems and Adaptive Learning
0.35
article

Supporting student self-learning using generative AI

Yuki Ito, Qiang Ma
article en
1 citations

Abstract

With recent advancements in language-generating AI, students increasingly depend on AI for problem-solving in their coursework. However, over-reliance on AI risks depriving students of opportunities for self-learning, which is essential in education. One of the considerable reasons students turn to AI is their inability to solve problems independently due to a lack of understanding. To address this, we propose an interactive system that guides students struggling with problem-solving by giving them hints. When the system gets students’ answers, it assesses the answer by breaking down problems into small parts, then evaluating each part, and finally, explaining the grading rationale. Based on this assessment, the system offers targeted suggestions for improvement generated by language generation AI with pre-prepared templates. These hints guide students without providing direct answers, fostering class understanding and problem-solving skills.

Kyoto University (JP), Kyoto Institute of Technology (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 30%
Intelligent Tutoring Systems and Adaptive Learning
0.35
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