Personality-Adaptive Conversational AI for Emotional Support: A Simulation Study Integrating Big Five Detection with Zurich Model-Inspired Regulation

LLM-based conversational agents generate fluent responses but remain limited in adapting their supportive style to individual personality and emotional needs. We present a Detect–Regulate–Evaluate (D–R–E) architecture that performs turn-by-turn Big Five detection and applies Zurich Model-inspired behavioural regulation, orchestrated with PROMISE. The novelty is this integrated, reproducible detection→regulation→evaluation architecture for controlled simulation—not a claim of clinical effectiveness. In a GPT-4 simulation comparing personality-adapted (regulated) and standard (non-adaptive) assistants, scored by a structured LLM-based evaluator with author review retained as an internal audit, the main finding is the mixed-personality comparison with identical user text (input replay; outcome-scoring protocol not independently archived in full): dimension-level accuracy fell to 58.1%, yet three cross-family LLM judges retained a Personality Needs advantage, locating the extreme-profile result as an upper bound. Under extreme boundary-condition profiles, strict all-five-trait recovery was 21/60 (35.0%) and dimension-level accuracy was 83.3%; regulation adherence was 100%, and the Personality Needs Yes rate rose from 8.3% (5/60) to 100% (60/60) as an upper-bound selective-enhancement check. A two-rater verify-and-revise audit on an n=66 overlap showed substantial agreement on detection labels (mean linear κ=0.731) but covered detection labels only; outcome rubrics remain LLM-rated. Safety behaviours (crisis handling, unsafe advice, hallucination, escalation) were not evaluated. The Zurich mapping is implemented as a design choice rather than validated as psychological theory; the system is not evaluated as a therapeutic or clinical intervention.