Game-Based Platforms for Studying Virtual Agent Believability
Bibliographic record
Abstract
Believable virtual agents are central to many interactive systems, yet current methods for evaluating user trust and perception often rely on limited, decontextualized tools. My research investigates how narrative-driven, game-based environments can be used to study users’ perceptions of virtual agent believability, particularly in relation to gender presentation and role-based behavior. I am developing an interactive platform where users engage with male, female, and androgynous agents embedded in branching scenarios that simulate trust-based decision-making. The platform incorporates adaptive storytelling, real-time analytics, and machine learning to capture both behavioral and attitudinal responses. By gamifying the evaluation process, the research aims to produce richer, more ecologically valid data than traditional surveys. This work draws from human-computer interaction, affective computing, and AI ethics to explore how design choices, such as agent role, gender, and interactivity, affect user judgments of reliability and integrity. As an early-stage PhD student, I am currently refining the research questions, platform design, and experimental methods. Feedback from the AIIDE community will help sharpen the research focus, improve methodological choices, and ensure broader relevance across virtual agent applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".