Leveraging Artificial Intelligence for Cross-Disciplinary Student Performance Prediction a Framework for Personalized Education and Academic Success

DOI

10.2991/978-2-38476-400-6_94How to use a DOI?

Keywords

Artificial Intelligence (AI); Cross-Disciplinary Education; Personalized Learning; Explainable AI (XAI)

Abstract

In the era of interdisciplinary education, personalized learning has become essential for fostering student success. However, accurately predicting student performance across diverse disciplines remains a significant challenge. This study explores the integration of Artificial Intelligence (AI) to develop a framework for cross-disciplinary student performance prediction, aiming to enhance educational resource allocation and support personalized education. Leveraging open-source datasets, the proposed framework incorporates machine learning models and Explainable AI (XAI) techniques, such as Shapley Additive Explanations (SHAP), to identify key factors influencing academic outcomes. Results demonstrate the framework’s high accuracy in predicting performance and its ability to provide interpretable insights for educators, facilitating tailored interventions. This research highlights the potential of AI-driven approaches to optimize educational practices and promote academic success in interdisciplinary learning environments.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bo Hou
AU  - Cong Zhou
AU  - Yahan Liu
AU  - Wei  Xu
AU  - Junting Zhang
PY  - 2025
DA  - 2025/05/15
TI  - Leveraging Artificial Intelligence for Cross-Disciplinary Student Performance Prediction a Framework for Personalized Education and Academic Success
BT  - Proceedings of the 2nd International Conference on Educational Development and Social Sciences (EDSS 2025)
PB  - Atlantis Press
SP  - 797
EP  - 803
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-400-6_94
DO  - 10.2991/978-2-38476-400-6_94
ID  - Hou2025
ER  -