Transmitting Traditional Chinese Culture in Educational Psychology Course with AI Empowerment: A Study Based on the ASSURE Model
Bibliographic record
Abstract
This study investigates how artificial intelligence (AI) can enhance the transmission of Traditional Chinese Culture (TCC) within an undergraduate Educational Psychology course through an AI-enhanced ASSURE instructional design framework. Using design-based research with four iterative cycles, the study integrates AI tools-including learner analytics, generative dialogues, and virtual reality cultural scenarios-into each phase of the ASSURE model to support culturally grounded, discipline-embedded learning. A mixed-methods approach collected quantitative data from pre- and post-tests and qualitative data from classroom observations, AI analytics, student artefacts, reflective journals, and interviews with eight representative students. Results show significant improvements in students' TCC knowledge, cultural expression, intercultural competence, and learning motivation. Qualitative findings further reveal that AI-mediated experiences deepened cultural understanding, strengthened links between Confucian and Western psychological theories, and enhanced learner agency and identity expression. The study contributes a replicable model for integrating TCC into disciplinary courses and demonstrates that AI, when aligned with structured instructional design, can meaningfully support cultural transmission, conceptual understanding, and identity development in higher education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".