Prompting Against Persona Drift: Comparing Intervention Timing and Content in LLM-Simulated Conversations

Simulating student personas with large language models (LLMs) enables scalable evaluation of educational systems. However, behavioral drift, a progressive decline in persona consistency, can emerge over extended conversations, limiting the validity of such simulations. We evaluate five prompt-level mechanisms using separate monitoring and intervention pipelines. Across 1,200 28-turn conversations spanning four LLMs and two ADHD persona intensities, we varied when to intervene (static vs. adaptive) and what to inject (reinjection vs. reflective reminder), plus a novel adaptive condition in which a monitor generates behavior-specific instructions. Relative to no intervention, reinjection reduced the modeled rate of LLM-rated drift by 35–38%, reflective reminders by 22–27%, and behavior-specific instruction by 87%. None eliminated drift. We found no evidence that adaptive timing outperformed static scheduling. Monitoring therefore appears more useful for deciding what to correct than when to intervene, although behavior-specific instruction requires component-level testing.

Introduction. Human–computer interaction (HCI) research increasingly employs large language models (LLMs) to simulate human behavior. LLM-based agents configured with specific personas can generate synthetic feedback for rapid interface iteration Park et al. (2022); Xiang et al. (2024); Choi et al. (2025), offer lower costs and faster response times than human recruitment Hämäläinen et al. (2023), and enable applications spanning, for example, therapeutic chatbots Hu et al. (2025) and educational AI assistants Kazemitabaar et al. (2024). In educational contexts, LLM-based student simulations offer a scalable alternative to recruiting real learners, enabling cost-effective evaluation of teacher-training systems, adaptive tutoring tools, and conversational educational agents across diverse learner profiles Martynova et al. (2025); Wu et al. (2025); Zhang et al. (2025). LLM-based human simulation is particularly valuable for underrepresented populations, where recruiting sufficient participants at specific symptom intensities is rarely feasible.

Discussion / Conclusion. The results reveal a distinction between two uses of behavioral monitoring. When monitoring only determined when to repeat fixed intervention content, it did not improve the overall trajectory relative to static scheduling. When behavioral measurements shaped what the intervention addressed, the resulting behavior-specific instructions were associated with the largest reduction in drift. Monitoring was therefore not uniformly beneficial; its value depended on how the measurement informed the intervention. At the same time, the continued decline in every condition sets an important boundary on these effects. The clearest content contrast concerns how current behavioral information was used. Behavior-specific instruction reduced the post-knot decline rate by 87% relative to the control and produced a flatter trajectory than either of the other adaptive conditions. This advantage appeared from checkpoint 3 onward and remained present in both intensity strata.