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The rapid evolution of generative artificial intelligence (GAI) has transformed its educational role from a supplementary tool to an active collaborative partner. Based on the Expectation-Confirmation Model (ECM) and Shared Mental Models (SMMs) theory, this study examines the psychological and behavioral drivers of college students’ sustained use of GAI teammates in collaborative learning contexts. Survey data from 867 valid responses were analyzed using structural equation modeling (SEM). Results demonstrate that expectation confirmation significantly enhances perceived usefulness and satisfaction, both of which positively predict continuance intention. Critical SMMs components—communication, trust, and explainable transparency—also exhibit substantial positive effects on continuance intention. These findings offer a theoretical understanding of human-AI collaboration dynamics and practical guidance for optimizing GAI applications in education.

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Determinants of College Students’ Continuance Intention Toward Generative AI Teammates: An Integrated Expectation-Confirmation and Shared Mental Model Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Zhanling Guo,
  • Yunzhen Liang,
  • Yao Liu

摘要

The rapid evolution of generative artificial intelligence (GAI) has transformed its educational role from a supplementary tool to an active collaborative partner. Based on the Expectation-Confirmation Model (ECM) and Shared Mental Models (SMMs) theory, this study examines the psychological and behavioral drivers of college students’ sustained use of GAI teammates in collaborative learning contexts. Survey data from 867 valid responses were analyzed using structural equation modeling (SEM). Results demonstrate that expectation confirmation significantly enhances perceived usefulness and satisfaction, both of which positively predict continuance intention. Critical SMMs components—communication, trust, and explainable transparency—also exhibit substantial positive effects on continuance intention. These findings offer a theoretical understanding of human-AI collaboration dynamics and practical guidance for optimizing GAI applications in education.