Psychological adaptation to scaffolded generative AI use in higher education: SEM and thematic evidence from a private university's new media marketing course

This study examines how students psychologically adapt to scaffolded generative artificial intelligence (GenAI) use in higher education. Drawing on the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), we investigated how acceptance-related beliefs and behavioral intention (BI) were associated with learning engagement (ENG) and student-perceived instructional innovation (PPI) in a New Media Marketing course at a private Thai university. Using an explanatory sequential mixed-methods design, quantitative survey data from 207 students were analyzed using covariance-based structural equation modeling (SEM), and qualitative data from semi-structured interviews with 10 students and the course instructor, complemented by classroom artifacts, were examined thematically. The SEM results indicated that BI was positively associated with ENG and PPI, with ENG carrying much of the association between BI and PPI. Upstream beliefs, including digital self-efficacy, perceived ease of use, perceived usefulness, and general attitudes toward AI, were linked to PPI primarily through a serial sequence involving attitudes, intention, and engagement. Qualitative findings identified an explain-verify-revise routine, supported by prompt templates, verification checklists, and process-visible rubrics, as a key mechanism through which scaffolded GenAI use was associated with evaluative judgment, cautious trust, and more deliberate engagement. Taken together, the findings suggest that positive appraisal of scaffolded GenAI in this context depended less on technological familiarity alone than on structured use. Implications are discussed for AI-supported learning design and faculty development, particularly in relation to supporting students' judgment, agency, and accountability when working with GenAI tools.