Observational analysis highlights the role of generative AI tools in aesthetic education for students, suggesting the need for effective integration strategies.
Aesthetic education plays a crucial role in higher education by fostering holistic development and creativity. With the advent of Generative AI (GenAI), particularly in accessible and efficient AI painting tools, aesthetic education is undergoing a paradigm shift. Despite increasing adoption, the pedagogical integration and student acceptance of such tools remain underexplored. This study develops an extended Technology Acceptance Model (TAM) tailored to non-design majors in Chinese higher education, incorporating variables such as hedonic motivation, self-efficacy, perceived painting quality, and AI anxiety. Employing covariance-based structural equation modeling (CB-SEM) on data from 317 students, the study reveals that hedonic motivation and perceived painting quality significantly enhance acceptance, while AI anxiety negatively impacts perceived usefulness and intention to use. Furthermore, individual differences (e.g., gender, AI literacy) are shown to moderate several acceptance pathways. These findings advance our understanding of GenAI tools adoption in aesthetic education and offer practical implications for educators and policymakers seeking to design inclusive and effective AI-supported creative learning experiences.
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Zeng et al. (2025) studied this question.
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