技术可供性影响下大学生生成式人工智能的持续使用意愿：基于感知价值与学习投入的中介效应

罗  霞^1,3  计国君^2,3  周  玮⁴

（1.厦门大学 教育研究院，福建 厦门 361005；2.厦门大学 管理学院，福建 厦门 361005；3.厦门大学 教师发展中心，福建 厦门 361005；4.华南师范大学 教育信息技术学院，广东广州 510631）

【摘  要】生成式人工智能作为颠覆性技术，正重塑高等教育生态。基于可供性理论与刺激—机体—反应模型，构建了影响大学生生成式人工智能持续使用意愿的模型。实证分析表明，技术可供性既直接正向影响持续使用意愿，又通过感知价值和学习投入的双重中介路径产生间接影响。研究发现，感知价值在功能、情绪、经济和社会四个维度上起关键作用，学习投入则可通过提升认知参与度和情感投入度强化使用意愿。此外，性别与使用经验在影响路径中并不具有显著的调节效应。基于此提出如下建议：第一，优化技术可供性设计，增强教育适配性；第二，深化教学融合应用，赋能师生协同发展；第三，完善治理评估体系，促进应用规范发展。

【关键词】技术可供性；生成式人工智能；持续使用意愿；感知价值；学习投入

University Students’Continuance Intention to Use Generative Artificial Intelligence under the Influence of Technology Affordances: Based on the Mediating Effects of Perceived Value and Learning Engagement

LUO Xia^1,3，JI Guojun^2,3 and ZHOU Wei⁴

(1. Institute of Education, Xiamen University, Xiamen 361005, China; 2. School of Management, Xiamen University, Xiamen 361005, China; 3. Center for Teaching and Learning Development, Xiamen University, Xiamen 361005, China; 4. School of Educational Information Technology, South China Normal University, Guangzhou 510631, China)

Abstract: Generative Artificial Intelligence (GAI), as a disruptive technology, is reshaping the higher education landscape. Based on affordance theory and the stimulus-organism-response (SOR) framework, this article constructs a model of the influence of GAI technology affordance on university students’ continuance intention. Empirical analyses indicate that technology affordance not only have a direct positive effect on continuance intention but also exert an indirect influence through the dual mediating pathway of perceived value and learning engagement. It is found that perceived value plays a key role across its four dimensions: functional, emotional, economic, and social, while learning engagement strengthens usage intention by enhancing cognitive participation and affective involvement. Furthermore, gender and usage experience do not exhibit significant moderating effects within the influence pathway. Based on these findings, the following suggestions are proposed: first, optimize the design of technology affordances to enhance educational adaptability; second, deepen the integration of GAI into teaching applications to empower the synergistic development of educators and learners; third, improve governance and evaluation system to promote the standardized development of GAI applications.

Keywords: technology affordance; generative artificial intelligence; continuance intention; perceived value; learning engagement

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