Designing Interactive Artifacts for Mental Health Education: A Game-Based Approach using AI as In-game Characters
Bibliographic record
Abstract
Mental Health (MH) is fundamental to overall well-being, yet Mental Health Literacy (MHL) remains low despite growing awareness.Many individuals feel uncomfortable discussing MH due to persistent stigma, leading to misconceptions, discrimination, and reluctance to seek help.Reducing stigma requires education and open dialogue.Games have been widely recognized as effective tools for learning, and digital platforms increasingly play a role in MH interventions by offering engaging, interactive experiences.Similarly, Large Language Models (LLMs), such as ChatGPT, are being adopted across various domains for answering questions and facilitating learning.However, when it comes to sensitive topics like MH, users may hesitate to engage with AI-driven models or be unaware of the proper ways to ask questions to the model.Moreover, the potential of AI-powered game agents in MH educational context remains largely unexplored.My research investigates the design of interventions that integrate game-based learning with AI-driven conversational agents as Non-Player Characters (NPC) to promote awareness of MH, encourage self-reflection, and reduce stigma.Using a Research Through Design approach, I develop and evaluate prototypes where users engage with games and a chatbot to explore MH topics.The findings aim to contribute to the "Artifacts and Systems" domain by providing insights into designing effective and engaging digital MH interventions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".