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2025-10-30
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Copyright (c) 2025 Weixuan Huang, Xinyi Ding, Shijia Shao

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How to Cite
A study on the dual mechanisms of AI teaching assistants' impact on college students' learning motivation: From the perspective of social cognitive theory
Weixuan Huang
INTI International University, Faculty of Education and Liberal Arts (FELA), 71800, Nilai
Xinyi Ding
The university of Melbourne, Faculty of Business and Economics, VIC 3010, Australia
Shijia Shao
Northwestern Polytechnical University, school of software, 710129, China
DOI: https://doi.org/10.59429/esp.v10i10.4182
Keywords: AI teaching assistant; autonomous learning motivation; social cognitive theory; empowerment mechanisms; alienation risks; intervention strategies
Abstract
Do AI teaching assistants undermine students' cognitive independence while improving learning outcomes? Based on social cognitive theory, this study systematically addresses this critical question through a 16-week quasi-experimental tracking of 480 college students. The research reveals a dual effect of AI teaching assistants: on one hand, they significantly enhance learning motivation by 22.3% through three major mechanisms—personalized feedback, social presence creation, and adaptive pathways; on the other hand, they lead to a 63% increase in cognitive dependence, a 9.2% decline in critical thinking, and a 54.5% rise in social isolation risk. In response to these alienation risks, the study constructs and validates a three-dimensional intervention model: at the environmental level, implementing blended learning design; at the individual level, conducting metacognitive and critical thinking training; and at the behavioral level, establishing a gradual scaffolding withdrawal mechanism. Intervention experiments show that the comprehensive strategy increases cognitive independence by 37.8%, effectively reversing the dependence trend. The "empowerment-alienation-intervention" theoretical framework constructed by this study provides an actionable risk management solution for AI educational applications and holds significant guiding significance for promoting the healthy development of human-AI collaborative learning ecosystems.
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