Right Destination, Wrong Journey: Synthetic Counseling Dialogues Miss Human Interactional Dynamics

LLM-generated counseling dialogues are increasingly used for training and evaluating mental-health systems, yet existing evaluations focus largely on response quality and final outcomes rather than how support unfolds over time. We conduct a trajectory-centered evaluation of process fidelity across nine human, reconstructed, and synthetic counseling datasets. Our results show that synthetic dialogues often reach plausible endpoints but differ from human support in recovery dynamics and turn-by-turn responsiveness. While generation design can shift global properties such as recovery timing and trajectory structure, contingent patient–counselor responsiveness remains consistently lower in fidelity across models and generation paradigms. Our experiments on controlled synthetic datasets show that targeted generation interventions can narrow the gap with real data on several trajectory-level measures, but do not fully recover interactional fidelity, with turn-level responsiveness remaining limited. We release three controlled synthetic datasets comprising 2,050 dialogues, together with annotations and the evaluation framework.