Ludic Pedagogy Meets ChatGPT: An Application of Fun, Play, Playfulness, and Positivity to a Technological Context
Bibliographic record
Abstract
This paper explores how Ludic Pedagogy – the incorporation of fun, play, playfulness, and positivity into learning – can address challenges to student disengagement and academic integrity. We use the case of AI predictive text tool ChatGPT to illustrate how intrinsic motivation can come from students' enjoyment and satisfaction with learning. We make two proposals: first, by using Ludic Pedagogy principles and approaching ChatGPT with a sense of curiosity and experimentation, students can engage more actively with their learning, and may be less likely to “cheat” on academic assignments. Second, designing authentic assessments that are completed with a sense of positivity may negate the usefulness of ChatGPT as a tool for academic dishonesty. Adopting a Ludic Pedagogy has implications for learning environments and assessment whereby educators may turn a technological “threat” into a learning opportunity and students may experience heightened engagement.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".