Should the AI Speak First? Evaluating Proactive vs. Reactive Facilitation in Mixed-Reality Medical Training

As AI support tools become more common in immersive medical training, designers face a critical interaction-design question: When should an AI facilitator take initiative, and when should it wait for the learner? To investigate this design tension, we compared two versions of an AI facilitator in a mixed-reality (XR) lumbar puncture simulator training conditions: one in which the AI proactively initiated guidance and encouragement, and another in which the AI responded only when prompted. Using a mixed-methods approach, we examined how medical students (n=22) engaged with, interpreted, and reacted to these two facilitation styles. We found no significant differences in learning outcomes, interaction frequency, or overall experience ratings. However, interviews and behavioral analyses revealed nuanced differences in how learners perceived AI interventions across distinct task phases. AI-initiated support was seen as helpful in some moments and disruptive in others, depending on task phase, cognitive load, and personal preferences. Based on these findings, we contribute a boundary framework which offers actionable design guidance for calibrating AI proactivity in immersive training systems, and extends HCI research on proactive agents and human–AI collaboration within high-cognitive-load environments.