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.First and foremost, I would like to thank my supervisor Dr. Audrey Girouard, who took me under her guidance, taught me to trust my abilities, and gave me an opportunity to work on something I feel strongly passionate about.Without your guidance and trust, I would not be able to come this far.I would also like to thank everyone at the Creative Interactions Lab.I could not have asked for a better team who is always eager to help and contribute.In particular, I would like to thank Dr. Jin Kang for always having my back and supporting me in every way possible.Thank you for guiding me throughout this journey, cheering me up with your words of encouragement
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".