Charting the Uncharted with GUR: How AI Playtesting Can Supplement Expert Evaluation
Bibliographic record
Abstract
Despite the advantages of using expert evaluation as a method within games user research (GUR) (i.e. provides stakeholders low cost, rapid feedback), it does not always accurately reflect the general player’s experience. Testing the game out with real users (also called playtesting) helps bridge this gap by giving game developers an in-depth look into the player experience. However, playtesting is resource intensive and time consuming, making it difficult to implement within the tight time frames of industry game development. AI can help to mitigate some of these issues by providing an automated way to simulate player behaviour and experience. In this paper, we introduce a tool called PathOS+—a playtesting interface which uses AI playtesting data to help enhance expert evaluation. Results from a study conducted with expert participants shows how PathOS+ could contribute to game design and assist developers and researchers in conducting expert evaluations. This is an important contribution as it provides game user researchers and designers with a fast, low-cost and effective game evaluation approach which has the potential to make game evaluation more accessible to indie and smaller game studios.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".