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Explainable machine learning for sustainable education: Predicting college students' reliance on generative

Sunyu Tao1, Hongfeng Zhang1, Liwei Ding1

  • 1Faculty of Humanities and Social Sciences, Macao Polytechnic University, 999078, Macao.

Acta Psychologica
|March 17, 2026
PubMed
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Students increasingly use generative artificial intelligence (GenAI) in education, but overreliance is a concern. This study found classroom speaking pressure significantly predicts GenAI dependence, offering insights for responsible AI integration.

Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Human-Computer Interaction

Background:

  • Generative artificial intelligence (GenAI) is increasingly integrated into educational settings.
  • Students utilize GenAI for knowledge acquisition, problem-solving, and creative exploration.
  • Concerns exist regarding potential overreliance on GenAI tools in academic contexts.

Purpose of the Study:

  • To assess and manage student dependence on GenAI for sustainable educational use.
  • To develop and validate machine learning models for predicting GenAI dependence levels.
  • To identify key factors influencing student GenAI dependence.

Main Methods:

  • Collected questionnaire data from university students in China.
  • Applied principal component analysis and numerical binning for data preprocessing.
Keywords:
Education for sustainable developmentExplainable artificial intelligenceGenAI dependenceMachine learningSHAP

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Last Updated: May 5, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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  • Trained and compared six machine learning models, with Random Forest (RF) showing the best performance (F1-score 0.836).
  • Interpreted RF model using SHAP and PDP methods for explainability.

    • Main Results:

    • Classroom speaking pressure was identified as the primary predictor of GenAI dependence, explaining 22.9% of the variance.
    • Higher speaking pressure correlated with increased GenAI dependence, particularly in highly dependent groups.
    • Model robustness was confirmed through ablation studies and multi-dimensional sensitivity analyses.

    Conclusions:

    • Proposed an effective and explainable method for predicting GenAI dependence.
    • Provided empirical evidence to guide students toward responsible GenAI usage.
    • Informed targeted interventions to balance AI support with autonomous learning in education, aligning with UN SDG 4.