The adaptive virtual patient produces a steadily climbing disclosure trajectory across a session while a prompt-only baseline with the same LLM and persona stays flat¶

Claim · All claims
Evidence · 1 study · causal r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-17 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? In a within-subjects human study, AVP disclosure climbed from guarded toward medium over the session while the static prompt-only VP remained nearly flat. → Angela Chen 2026

Evidence¶

Angela Chen 2026¶

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 doi:10.48550/arxiv.2606.10051

q2 · i? · causal · r2

Within-subjects evaluation (20 clinicians and trainees, 80 sessions, 1,033 turns) comparing AVP and a static prompt-only VP; decile-averaged evaluator-coded disclosure showed "the A VP climbs from ≈1.0 (Guarded)" while the SVP stayed near its starting level. No standardized effect size is printed for this trajectory contrast.

"the A VP climbs from ≈1.0 (Guarded) at the start of a session to ≈2.0 (Medium Disclosure) by the 80% mark, while the SVP starts already at≈1.7 and remains nearly flat."

Discussion¶

Related Claims¶

• Higher-skill trainees elicit faster-climbing AVP disclosure trajectories, directionally supporting skill sensitivity, though the formal interaction test is underpowered — related
• Post-session subjective ratings favored the adaptive system for the expressive persona but showed a reversed adaptivity disadvantage for the guarded persona — related
• The AVP's generated text matches its intended internal disclosure level about twice as often as the static baseline (0.651 vs. 0.311 agreement) — related
• In the AVP, per-turn disclosure change responds to trainee exploration but not measurably to trainee empathy, and the static baseline shows no significant joint response — related
• The deployed empirically motivated weight vector outperforms equal, empathy-only, and exploration-only weightings in evaluator agreement, with exploration carrying most of the adaptive signal — related
• AURA's within-session reinforcement learning improved composite response quality over non-adaptive baselines (p = 0.044, d = 0.66), with fewer specification prompts and more validation behavior — related
• Standalone LLM tutors replicate Phase II's profile: strong single-interaction intelligence without systematic control over the learning trajectory — related
• Expert evaluation found the virtual patient realistic and immediate ACT feedback increased therapists' awareness of intervention choices — related
• LLM coders replicate many human discursive moves but lack questioning, adaptive responsiveness, and emotional expressiveness