How ChatGPT can inspire and improve serious board game design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Designing engaging serious board games that effectively address students' diverse and complex needs presents a significant challenge for educators. As a possible solution, Large Language Models (LLMs) such as ChatGPT can assist educators in designing and evaluating game-based learning experiences. This study explores three primary ways ChatGPT can enhance educators' game design process. Firstly, ChatGPT can assist with brainstorming, suggesting game themes and mechanisms aligned with curriculum and learning goals. Secondly, it can provide templates or exemplars of game components, allowing educators to create customized games that offer what their students need. Lastly, ChatGPT can offer valuable feedback on game prototypes, identifying areas for improvement and guidance to enhance the game's efficacy as an educational tool. We attempted to advance the ongoing discourse on the roles of artificial intelligence and board games in education by providing valuable insights into the potential of these tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it