Gate sensitive simulated-patient content behind an accumulated-skill threshold with constraint-first prompting¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article's implementation strategy is to release sensitive disclosure topics only as the trainee's accumulated skill crosses fixed thresholds. The prompt is built constraint-first: a disclosure-constraint instruction marked absolute, then level-matched topics, then memory, then dialogue history. Long-term memory is "seeded only from G-level content at session start, so memory retrieval cannot surface sensitive material before the level allows it." At G, H-level facts are not in the prompt so the LLM cannot produce them.

Design Implications¶

Context¶

Requirements¶

• Requires per-level disclosure topic sets and constraint instructions authored for each persona
• Requires a validated disclosure-labeling mechanism to resolve the current level each turn

Constraints¶

• The threshold values (M at 4.5, H at 10.0) and the 3:1 weight ratio are design choices; the article states the optimal ratio likely exceeds 3:1

Target Learners¶

• clinicians and clinical trainees practicing psychotherapy micro-skills

Target Learning Goals¶

• practicing elicitation of progressively deeper patient disclosure

Affordances¶

• Avp State Generation Separation Framework

Related Strategies¶

• Mastery Based Progression

Examples¶

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Key Sources¶

• Angela Chen, Siwei Jin, Catherine Bao, Canwen Wang, Robert E. Kraut, Tongshuang Wu, and Haiyi Zhu. (2026). The Empirically Grounded Adaptive Virtual Patient for Psychotherapy Training: Disclosure That Responds to Therapist Micro-Skills. https://arxiv.org/abs/2606.10051