From Intimidation to Innovation: Cross-Continental Multiple Case Studies on How to Harness AI to Elevate Engagement, Comprehension, and Retention
Bibliographic record
Abstract
As generative AI tools become increasingly embedded in education, their role in supporting student learning remains both promising and contested. These cross-continental multiple case studies explore how integrating AI into classroom-based creative projects can move students from intimidation to meaningful engagement, comprehension, and retention of course content. Drawing on data from four international university classrooms—in the USA, UK, Canada, and Australia—this mixed-methods study examines students’ experiences as they collaboratively created comic books using generative AI. Each instructor embedded the assignment within their own pedagogical context, enabling cross-institutional comparison of AI’s educational potential. Findings highlight a shared trajectory: students initially approached AI with uncertainty or overconfidence, but developed nuanced understandings of its capabilities through experimentation, reflection, and collaboration. The process of creating narrative-driven visual outputs required students to synthesize theoretical material, communicate effectively in teams, and creatively solve problems—fostering both cognitive and interpersonal learning. Students reported deeper comprehension of academic content and greater confidence using AI tools critically and ethically. This study concludes that when framed as a collaborative partner rather than a replacement for human thinking, AI can support deeper learning experiences. It also suggests that creative, team-based projects can demystify AI and build essential future-facing skills.
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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 | 0.027 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".