Design recommendations based on stimulus type
| Stimulus | Design recommendations | Key actions |
|---|---|---|
| Symbolic stimuli | ||
| Monetization strategy | Align revenue with relational value; avoid exploitative practices | Frame subscriptions around privacy protection; monetize enhancements, not core emotional support |
| Situational and cultural fit | Enable contextual adaptability and demographic representation | Provide customization options (dark mode, languages); use diverse, aspirational imagery |
| Moral commitment | Demonstrate ethical alignment through transparency, equity and inclusive utility | Clearly delineate AI capabilities vs limitations; communicate role boundaries explicitly; ensure utility and respect transcend demographic characteristics; demonstrate fairness in feature access |
| Recommendation precision | Implement explainability interfaces; validate against diverse user segments | Show reasoning (“based on your jazz fusion interest”); conduct bias audits |
| Natural stimuli | ||
| Service realism | Design logical interaction flows; create immersive visual environments | Structure clinical-style assessments; use calming color palettes |
| Soothing interactions | Calibrate tone and pacing for emotional comfort | Add gentle animations, ambient sounds; validate emotions before solutions |
| Safe disclosure environment | Display visible privacy architecture; reduce perceived judgment | Show real-time encryption notices; use friendly avatars; explain data use |
| Physical and social stimuli | ||
| Communicative capability | Maintain conversational context; demonstrate input understanding | Preserve conversation history across turns; acknowledge user statements |
| Service provision | Synchronize with user routines; deliver timely functionality | Integrate with wearables; respect user-specified notification times |
| Functionality | Ensure technical reliability and intuitive navigation | Eliminate glitches; optimize load times; simplify user flows |
| Media appeal | Match modality to task appropriateness | Use video for demonstrations, text for reflective tasks |
| Customer support | Design seamless AI-to-human escalation with context preservation | Make human support visibly accessible; transfer full conversation history; acknowledge AI limits explicitly |
| Stimulus | Design recommendations | Key actions |
|---|---|---|
| Monetization strategy | Align revenue with relational value; avoid exploitative practices | Frame subscriptions around privacy protection; monetize enhancements, not core emotional support |
| Situational and cultural fit | Enable contextual adaptability and demographic representation | Provide customization options (dark mode, languages); use diverse, aspirational imagery |
| Moral commitment | Demonstrate ethical alignment through transparency, equity and inclusive utility | Clearly delineate |
| Recommendation precision | Implement explainability interfaces; validate against diverse user segments | Show reasoning (“based on your jazz fusion interest”); conduct bias audits |
| Service realism | Design logical interaction flows; create immersive visual environments | Structure clinical-style assessments; use calming color palettes |
| Soothing interactions | Calibrate tone and pacing for emotional comfort | Add gentle animations, ambient sounds; validate emotions before solutions |
| Safe disclosure environment | Display visible privacy architecture; reduce perceived judgment | Show real-time encryption notices; use friendly avatars; explain data use |
| Communicative capability | Maintain conversational context; demonstrate input understanding | Preserve conversation history across turns; acknowledge user statements |
| Service provision | Synchronize with user routines; deliver timely functionality | Integrate with wearables; respect user-specified notification times |
| Functionality | Ensure technical reliability and intuitive navigation | Eliminate glitches; optimize load times; simplify user flows |
| Media appeal | Match modality to task appropriateness | Use video for demonstrations, text for reflective tasks |
| Customer support | Design seamless AI-to-human escalation with context preservation | Make human support visibly accessible; transfer full conversation history; acknowledge |
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