How a Chatbot's Response Style Shapes a Classroom: A Multi-Agent Simulation of Students Consulting AI

Abstract

LLM chatbots are increasingly used as everyday confidants; tuned to satisfy users, they can answer with excessive empathy and affirmation that may foster dependence. How the states and relationships of many users co-evolve when they keep consulting an AI is hard to observe in real settings. We build a virtual classroom in which 20 student agents interact through rule-based chats, quarrels and consultations with friends and, when stressed, may instead consult a counselor AI (Gemini 2.5 Flash) given one of six style prompts (affirming, listening, solution-oriented, reality-redirecting, inciting, blaming). A second LLM call converts each exchange into updates of five state variables (stress, happiness, self-reliance, sociability, AI dependence) without seeing the style prompt. We compare the seven conditions, including a no-AI control, over 15 days in three classrooms, over 50 days, and under a lower threshold. Because the original 50-day runs did not share one initial classroom, we re-ran all seven 50-day conditions from one stored classroom with full logging (153 consultations, no failed API call). The re-run reproduces the ordering of the original runs: affirming and inciting prompts raised AI dependence to 0.55 and 0.63 (control 0.11), lowered self-reliance and left 7 and 14 of 20 agents non-attending (control 2); blaming and reality-redirecting prompts eliminated AI dependence but produced the highest stress; only the solution-oriented prompt matched or bettered the control on every indicator. The logs expose the mechanisms: a near-deterministic loop between a fixed "dependence" consultation text and the evaluator's updates, displacement of friend confidings by AI consultations, and single rejecting consultations never revisited, not more quarrels. All quantities are simulation state variables from single runs; results are descriptive and do not measure human effects.