Impact of generative AI interaction and output quality on university students’ learning outcomes: a technology-mediated and motivation-driven approach
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This study investigates the influence of generative artificial intelligence (GAI) on university students' learning outcomes, employing a technology-mediated learning perspective. We developed and empirically tested an integrated model, grounded in interaction theory and technology-mediated learning theory, to examine the relationships between GAI interaction quality, GAI output quality, and learning outcomes. The model incorporates motivational factors (learning motivation, academic self-efficacy, and creative self-efficacy) as mediators and creative thinking as a moderator. Data from 323 Chinese university students, collected through a two-wave longitudinal survey, revealed that both GAI interaction quality and output quality positively influenced learning motivation and creative self-efficacy. Learning motivation significantly mediated the relationship between GAI output quality and learning outcomes. Furthermore, creative thinking moderated several pathways within the model, with some variations observed across the two time points. These findings provide theoretical and practical insights into the effective integration of GAI tools in higher education, highlighting the importance of both interaction and output quality in optimizing student learning experiences.
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Micro-level studies frequently focus on psychological determinants such as technology readiness or anxiety without accounting for macro-institutional environments and meso-level pedagogical interventions.
“Conversely, micro-level studies frequently focus on psychological determinants—such as technology readiness or anxiety—without accounting for the dynamic influence of macro-institutional environments and meso-level pedagogical interventions (Bai and Wang, 2025; Gonsalves, 2025).”
From Multidimensional responses to generative AI in higher education: an integrative framework linking institutional governance, teaching practice, and student psychological adaptation (2026) · cites this paper as Bai and Wang, 2025
The Theoretical background says earlier technology-acceptance frameworks emphasized technological characteristics and user adoption while overlooking educational integration methods. That supports the general contrast, but it does not say micro-level studies frequently focus on technology readiness or anxiety, or that they neglect macro-institutional environments and meso-level pedagogical interventions specifically. The Theoretical implications discuss micro-level interactions and macro-level policy implications, but do not establish that broader characterization of prior studies. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMacro-level studies emphasize institutional governance or ethical standards while overlooking practical pedagogical effectiveness.
“Some emphasize macro-level institutional governance or ethical standards, often overlooking practical pedagogical effectiveness.”
From Multidimensional responses to generative AI in higher education: an integrative framework linking institutional governance, teaching practice, and student psychological adaptation (2026) · cites this paper as Bai and Wang, 2025
The Theoretical background (page unavailable) says earlier educational-technology frameworks overlooked integration methods and practical utility, supporting the claim of a practical gap. It identifies their emphasis as technological characteristics and user adoption, however, not institutional governance or ethical standards. The Discussion recommends institutional policies addressing ethics, but does not describe that as the emphasis of earlier studies. (LLM, conf=0.82)
Experimental — accuracy not yet measuredGenAI can expand the conceptual reach of the research experience by helping transform student interest into researchable inquiry.
“This extends observations that GenAI-generated content aligned with the individual interests of learners can increase motivation, interest, and engagement (Bai et al. 2025; Tasdelen and Bodemer 2025) and suggests that GenAI can expand the conceptual reach of the research experience by helping transform student interest into researchable inquiry.”
From Generative AI Expands the Intellectual Reach of Course Based Undergraduate Research Experiences (CUREs) (2026) · cites this paper as Bai et al. 2025
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“Enables adaptive difficulty and individualized feedback to enhance engagement (Bai & Wang, 2025).”
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Making AI-Generated Feedback Matter: A Large-Scale Study of Feedback Workflows and Student Enactment
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- Authors
- Bai, Y., & Wang, S.
- Affiliations
- Taiyuan Institute of Technology (CN)
- Fuzhou University of International Studies and Trade (CN)
- Corresponding author
- Wang, Shaofeng
- ORCID
- Wang, Shaofeng
- Venue
- Scientific Reports
- Published
- 2025-07-05
- Volume
- 15
- Issue
- 1
- Pages
- 24054-24054
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC12228813
- OpenAlex
- W4412047575
- Cited by
- 86
- References
- 75
- Citations by year
- 2026: 72 · 2025: 14
- FWCI
- 89.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Zhejiang Province Education Science Planning 2023 General Planning Project (2023SCG399)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, learning outcomes, technology-mediated learning, student motivation, academic self-efficacy, creative self-efficacy, creative thinking, higher education, university students, AI integration in education
- MeSH terms
- Adult, Artificial Intelligence, China, Female, Humans, Learning, Longitudinal Studies, Male, Motivation, Students, Universities, Self Efficacy, Young Adult
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