Ying Gu, Jingyi Shi, Bianqi Sun

Bianqi Sun

November 28, 2025

Teaching AI, Trust, Influencing factors, Technology Acceptance Model, Trust construction, Higher education.

The in-depth integration of artificial intelligence (AI) technology in the education sector is driving profound transformations in the landscape of higher education. As a key vehicle, the effectiveness of teaching AI fundamentally depends on the trust of its users. This study focuses on college students, systematically exploring the multi-dimensional influencing factors of their trust in teaching AI and constructing targeted strategies accordingly. By integrating the Technology Acceptance Model (TAM) and human-computer trust theory, a four-dimensional analytical framework encompassing technological attributes, individual characteristics, environmental contexts, and interaction experiences is established. The study finds that technological reliability, practicality, and transparency at the technological level; AI literacy and technological acceptance at the individual level; institutional support and teacher guidance at the environmental level; and personalization and feedback mechanisms at the interaction level collectively influence trust formation. Based on these findings, this paper proposes systematic trust-building strategies from four dimensions—technological optimization, individual empowerment, environmental construction, and institutional safeguards—providing theoretical references and practical guidance for promoting the sound development of teaching AI.

© 2025, the Authors. Published by ISCCAC

This is an open access article distributed under the CC BY-NC license