AIGC-enhanced learning analytics in film education: a decision-making framework for creative pedagogy in Chinese higher education.
Saved in:
| Title: | AIGC-enhanced learning analytics in film education: a decision-making framework for creative pedagogy in Chinese higher education. |
|---|---|
| Authors: | Tang, Chao1 (AUTHOR) tangchao726@jit.edu.cn, Wang, Lili2 (AUTHOR) wanglili@nua.edu.cn |
| Source: | International Journal of Educational Technology in Higher Education. 6/8/2026, Vol. 23 Issue 1, p1-26. 26p. |
| Subject Terms: | *Learning analytics, *Artificial intelligence, *Educational technology, *Ethical problems, *Film schools, *Creative teaching, *Learning management system, Chinese people |
| Abstract: | 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 (/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 =30.6%−35.7%) in learning design effectiveness. Key findings reveal that Ethical Fitness significantly predicts successful AI integration (=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. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: ehh DbLabel: Education Research Complete An: 194358756 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: AIGC-enhanced learning analytics in film education: a decision-making framework for creative pedagogy in Chinese higher education. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tang%2C+Chao%22">Tang, Chao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tangchao726@jit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Lili%22">Wang, Lili</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> wanglili@nua.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Educational+Technology+in+Higher+Education%22">International Journal of Educational Technology in Higher Education</searchLink>. 6/8/2026, Vol. 23 Issue 1, p1-26. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Ethical+problems%22">Ethical problems</searchLink><br />*<searchLink fieldCode="DE" term="%22Film+schools%22">Film schools</searchLink><br />*<searchLink fieldCode="DE" term="%22Creative+teaching%22">Creative teaching</searchLink><br />*<searchLink fieldCode="DE" term="%22Learning+management+system%22">Learning management system</searchLink><br /><searchLink fieldCode="DE" term="%22Chinese+people%22">Chinese people</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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 (/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 =30.6%−35.7%) in learning design effectiveness. Key findings reveal that Ethical Fitness significantly predicts successful AI integration (=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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=194358756 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s41239-026-00593-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1 Subjects: – SubjectFull: Learning analytics Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Educational technology Type: general – SubjectFull: Ethical problems Type: general – SubjectFull: Film schools Type: general – SubjectFull: Creative teaching Type: general – SubjectFull: Learning management system Type: general – SubjectFull: Chinese people Type: general Titles: – TitleFull: AIGC-enhanced learning analytics in film education: a decision-making framework for creative pedagogy in Chinese higher education. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tang, Chao – PersonEntity: Name: NameFull: Wang, Lili IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 06 Text: 6/8/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23659440 Numbering: – Type: volume Value: 23 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Educational Technology in Higher Education Type: main |
| ResultId | 1 |
