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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".