Personalized Learning Through AI-Assisted Tutoring: Exploring the Impact of Intelligent Tutoring on Student Academic Performance Using Educational Data Mining and Explainable Machine Learning

Shuqiao Zhang; Jingchuan Zhu; Xianchao Meng; Yun Zhang; Xiaoxu Wang; Ziqi Liu

doi:10.62177/jetp.v3i1.1267

Authors

Shuqiao Zhang Beijing Gonglian Technology Co., Ltd.
Jingchuan Zhu Beijing Gonglian Technology Co., Ltd.
Xianchao Meng Beijing Gonglian Technology Co., Ltd.
Yun Zhang Beijing Gonglian Technology Co., Ltd.
Xiaoxu Wang Beijing Gonglian Technology Co., Ltd.
Ziqi Liu Shizhen College of Guizhou University of Traditional Chinese Medicine

DOI:

https://doi.org/10.62177/jetp.v3i1.1267

Keywords:

AI-Assisted Tutoring, Personalized Learning, Student Performance Prediction, Shap Interpretability, Educational Data Mining, Machine Learning, Feature Importance Analysis

Abstract

The integration of artificial intelligence into educational settings has emerged as a promising approach to enhancing personalized learning and improving student academic outcomes. However, existing studies on AI-assisted tutoring face several limitations: (1) insufficient quantitative evidence on the causal relationship between tutoring interventions and academic performance, (2) limited understanding of how tutoring interacts with other educational factors such as study habits and parental support, and (3) a lack of interpretable models that can explain the mechanisms through which tutoring influences learning outcomes. To address these challenges, we present a comprehensive analytical framework that combines statistical hypothesis testing, multi-model predictive analysis, and SHAP-based interpretability to investigate the effect of AI-assisted tutoring on student GPA. Our framework incorporates three key contributions: (1) rigorous statistical testing confirming a significant GPA improvement in the tutoring group (Cohen's , ), (2) a comparative evaluation of six machine learning models achieving an of 0.9536 for GPA prediction, and (3) SHAP-based feature attribution revealing that tutoring consistently contributes a positive SHAP value of +0.10 to predicted GPA. Experiments on a dataset of 2,392 students demonstrate that AI-assisted tutoring ranks as the fourth most influential factor in academic performance, and its positive effect is robust across varying levels of study time, absences, and parental support.

Downloads

Download data is not yet available.

References

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252(Part A), 124167. https://doi.org/10.1016/j.eswa.2024.124167

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8

Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/app13095783

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274

Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370

Hasanein, A. M., & Sobaih, A. E. E. (2023). Drivers and consequences of ChatGPT use in higher education: Key stakeholder perspectives. European Journal of Investigation in Health, Psychology and Education, 13(11), 2599–2614. https://doi.org/10.3390/ejihpe13110181

Adel, A., Ahsan, A., & Davison, C. (2024). ChatGPT promises and challenges in education: Computational and ethical perspectives. Education Sciences, 14(8), 814. https://doi.org/10.3390/educsci14080814

Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke, J., & Ali, H. (2024). ChatGPT in teaching and learning: A systematic review. Education Sciences, 14(6), 643. https://doi.org/10.3390/educsci14060643

Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692

Lin, C. C., Huang, A. Y. Q., & Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10(1), 41. https://doi.org/10.1186/s40561-023-00260-y

Yağcı, M. (2022). Educational data mining: Prediction of students' academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 11. https://doi.org/10.1186/s40561-022-00192-z

Batool, S., Rashid, J., & Nisar, M. W. (2023). Educational data mining to predict students' academic performance: A survey study. Education and Information Technologies, 28(8), 905–971. https://doi.org/10.1007/s10639-022-11152-y

Khairy, D., Alharbi, N., & Amasha, M. A. (2024). Prediction of student exam performance using data mining classification algorithms. Education and Information Technologies, 29(12), 21621–21645. https://doi.org/10.1007/s10639-024-12619-w

Asselman, A., Khaldi, M., & Aammou, S. (2023). Enhancing the prediction of student performance based on the machine learning XGBoost algorithm. Interactive Learning Environments, 31(6), 3360–3379. https://doi.org/10.1080/10494820.2021.1928235