The most engaging AI features are often the ones most likely to harm wellbeing — they hold attention, respond emotionally, and are always available. The AI User Wellbeing Design Checklist evaluates your feature against the design practices that protect users from overuse, dependency, emotional manipulation, and unhealthy attachment.
How it works
You describe the feature and how frequently users interact with it, then tick the protective design practices you have implemented. The checklist spans five risk areas: addiction / overuse risk, parasocial relationship risk, emotional manipulation, autonomy preservation, and dependency reduction.
The tool grades your coverage in each area and surfaces the highest-priority gaps — weighting more heavily for high-frequency features, where the stakes are greatest. The result is a wellbeing score and a prioritised list of practices to add.
What it checks
Examples of protective patterns include: natural stopping points instead of infinite engagement loops, usage-time awareness or gentle break prompts, clearly signalling the AI is not a person, avoiding manufactured urgency or guilt to retain users, making it trivial to disagree with or override the AI, surfacing human alternatives for emotionally serious topics, and steering users toward real-world resources rather than deepening reliance on the product.
Tips and notes
The two areas teams most often neglect are parasocial risk (especially for companion or always-available chat features) and dependency reduction (designing the product to need the user less over time, not more). If your feature serves children or otherwise vulnerable users, treat every area as high priority and seek expert review — a good checklist score is a strong start, not a clinical or legal sign-off. Everything runs in your browser and nothing is recorded.