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<p>Artificial intelligence-generated content (AIGC) has become integral to university students’ learning in higher education, offering potential for improved efficiency and personalized learning while raising concerns over academic integrity, privacy, and other issues. Drawing upon the Technology Acceptance Model (TAM) and Use and Gratification Theory, this study conceptualizes students’ AIGC acceptance as associated with both rational judgment (i.e., AI ethical cognition) and the fulfillment of needs (i.e., AI usage motivation). However, existing studies on AIGC acceptance remain fragmented, lacking integrated analysis of ethical cognition, unified modeling of diverse motivations, and sufficiently representative samples. To address these limitations, this study synthesized relevant literature in accordance with PRISMA guidelines, categorizing key factors into two constructs: AI ethical cognition (i.e., academic norms, privacy risk, algorithmic fairness dimensions) and AI usage motivation (i.e., knowledge-based, instrumental, and social entertainment dimensions). A research framework was constructed with these six factors as independent variables and AIGC acceptance level, measured using a six-item Likert scale, as the dependent variable. Using 1,642 valid responses from Chinese university students, the model was tested via CB-SEM in AMOS and regression analysis in SPSS. Results show that academic norms cognition is negatively associated with AIGC acceptance level, while the other five factors show significant positive relationships with acceptance. This study contributes to expanding the understanding boundaries of ethical factors in technology acceptance research and explaining more complex usage scenarios of generative AI. Practically, the findings provide novel empirical insights into ethical and privacy education initiatives, bias detection practices, learning communities building, interdisciplinary collaborative projects coordination, and the design of teaching activities related to AIGC acceptance in higher education contexts.</p>

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Rethinking AIGC acceptance in higher education: new evidence from a large-scale study of Chinese university students on AI ethical cognition and usage motivation

  • 期刊论文

  • 发表日期：

    2026年4月25日

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  • Zhe Xu

摘要

Artificial intelligence-generated content (AIGC) has become integral to university students’ learning in higher education, offering potential for improved efficiency and personalized learning while raising concerns over academic integrity, privacy, and other issues. Drawing upon the Technology Acceptance Model (TAM) and Use and Gratification Theory, this study conceptualizes students’ AIGC acceptance as associated with both rational judgment (i.e., AI ethical cognition) and the fulfillment of needs (i.e., AI usage motivation). However, existing studies on AIGC acceptance remain fragmented, lacking integrated analysis of ethical cognition, unified modeling of diverse motivations, and sufficiently representative samples. To address these limitations, this study synthesized relevant literature in accordance with PRISMA guidelines, categorizing key factors into two constructs: AI ethical cognition (i.e., academic norms, privacy risk, algorithmic fairness dimensions) and AI usage motivation (i.e., knowledge-based, instrumental, and social entertainment dimensions). A research framework was constructed with these six factors as independent variables and AIGC acceptance level, measured using a six-item Likert scale, as the dependent variable. Using 1,642 valid responses from Chinese university students, the model was tested via CB-SEM in AMOS and regression analysis in SPSS. Results show that academic norms cognition is negatively associated with AIGC acceptance level, while the other five factors show significant positive relationships with acceptance. This study contributes to expanding the understanding boundaries of ethical factors in technology acceptance research and explaining more complex usage scenarios of generative AI. Practically, the findings provide novel empirical insights into ethical and privacy education initiatives, bias detection practices, learning communities building, interdisciplinary collaborative projects coordination, and the design of teaching activities related to AIGC acceptance in higher education contexts.