Bibliographic record
Abstract
The inaccessibility of rulebooks hinders the rule learning experience of boardgame players who are blind or low vision (BLV). We explore the design of conversational agents (CAs) to support players' learning needs and provide companionship by conducting two qualitative studies. In study 1, 14 boardgame players who are BLV first identified their rule learning challenges and co-designed desired social and functional characteristics of CAs to combat these challenges. Based on these findings, we developed a CA using Amazon Alexa and 9 players who are BLV evaluated our CA in study 2. Our findings generated five design principles for CAs to support boardgame rule learning: conciseness, ease of navigation, customization, supplementary features, and social characteristics. These principles guide designers and researchers in exploring the novel design space. Our research also demonstrates the feasibility of our method for conducting accessible remote co-design and evaluation with participants who are BLV.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".