Aigc-enhanced learning analytics in film education

A decision-making framework for creative pedagogy in Chinese higher education

Bibliographic Data

ID22434186
AuthorsChao Tang (0000-0003-2701-9425, Jinling Institute of Technology), Lili Wang (0000-0002-0082-8487, Nanjing University of the Arts, corresponding author)
Year2026
Volume23
Issue1
Publication date2026-06-08
Open AccessYes
TypeARTICLE
VenueInternational Journal of Educational Technology in Higher Education (JOURNAL)
Journal identifiersISSN: 2365-9440 • E-ISSN: 2365-9440
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1186/s41239-026-00593-x
OpenAlexW7163808534
LanguageEN
References cited25

The integration of learning analytics with artificial intelligence represents a paradigm shift in educational decision-making, yet systematic frameworks for AI-enhanced learning design remain critically underexplored in creative higher education contexts where ethical considerations are paramount. Despite growing interest in AI-enhanced education, existing approaches lack systematic integration of learning analytics with ethical frameworks for evidence-based educational interventions in arts-based disciplines. This study develops and validates the Learning Analytics-driven Educational Decision-Making (LA-EDM) Framework, a comprehensive approach for AI-enhanced learning design through data-informed educational decision-making in creative education. A sequential mixed-methods design incorporated quantitative analysis of learning analytics data from 508 Chinese film students, qualitative interviews with 10 film educators, and systematic assessment of 10 student films. Structural equation modeling demonstrated strong model fit ( $$\chi ^{2}$$ /df=2.677, CFI=0.949), with mediation analysis revealing significant pathway relationships. The LA-EDM Framework demonstrates robust predictive validity, explaining substantial outcome variance (R $$^{2}$$ =30.6%−35.7%) in learning design effectiveness. Key findings reveal that Ethical Fitness significantly predicts successful AI integration ( $$\beta $$ =0.262 for technical-artistic balance) and indirectly influences Educational Effectiveness through Technical-Artistic Balance, with this pathway accounting for 19.834% of the total effect. Qualitative analysis identifies critical dialectical tensions including empowerment versus deskilling dynamics and efficiency versus creative depth considerations. This research extends learning analytics theory by providing the first empirically validated framework integrating ethical considerations with data-driven educational decision-making in creative disciplines. The findings offer evidence-based guidance for educators implementing AI-enhanced learning design in arts education, demonstrating how learning analytics can inform personalized and ethically-grounded pedagogical interventions.

Analytics · Educational research · Educational technology · Experiential learning · Higher education · Instructional design · Learning analytics · Learning Sciences · Mediation · Explainable Artificial Intelligence (XAI · Innovative Teaching Methodologies in Social Sciences · Online Learning and Analytics

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