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Vladimir Shapiro, Northeastern University, United StatesAbstract
This study leverages Artificial Intelligence (AI) methodologies to enhance Human Learning (HL) outcomes. Such AI concepts as weak and strong learners, learnability, and boosting are examined. Weak learners are rudimentary Machine Learning (ML) models that predict only slightly better than random chance, whereas strong learners achieve high performance after undergoing iterative training. Similarly, students with limited prior knowledge (weak learners) can be transformed into strong learners through ongoing structured feedback delivered throughout an educational program. The significance of feedback in both AI and human learning is emphasized. Additionally, motivation is recognized as another crucial factor in achieving learning success. The study advocates AI-inspired performance assessment methods that reward rapid progress, helping initially weaker students catch up with their initially more knowledgeable peers. The findings suggest that AI-inspired strategies can foster more effective, fair, and rewarding learning environments.
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Conference: PCE2025Stream: Design
This paper is part of the PCE2025 Conference Proceedings (View)
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To cite this article:
Shapiro V. (2025) An AI-Inspired Approach to Student Performance Assessment ISSN: 2758-0962 The Paris Conference on Education 2025: Official Conference Proceedings (pp. 241-256) https://doi.org/10.22492/issn.2758-0962.2025.19
To link to this article: https://doi.org/10.22492/issn.2758-0962.2025.19
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