Turning <scp>AI</scp> hallucinations into scaffolds for morphologic reasoning: A pilot randomized trial of prompt‐guided virtual microscopy learning during a focused histology module

AI is a medical education tool, yet its potential to foster morphologic reasoning remains underexplored. Histology students often struggle to move beyond pattern recognition toward evidence-based justification. This pilot trial evaluated a prompt-guided "Prompt Loop" workflow for improving morphologic reasoning and verification-oriented appraisal. A parallel-design randomized controlled trial was conducted with 55 first-year medical students. Participants were assigned to Standard Virtual Microscopy (VM) learning (n = 28) or VM Prompt Loop (n = 27). The intervention used a multimodal AI assistant (GPT-4o) within a four-stage cycle-Observation, Reasoning, Verification, and Consolidation-designed to treat AI inaccuracies as verification scaffolds. Outcomes included a supervised VM morphologic reasoning assessment (Identification vs. Reasoning) and a domain-level perception questionnaire. The VM Prompt Loop group achieved higher total scores (78.0 ± 13.5 vs. 67.1 ± 12.0; p = 0.003; Cohen's d = 0.85). This gain was mainly driven by the morphologic reasoning (38.4 ± 10.2 vs. 31.0 ± 8.2; p = 0.005; d = 0.80), whereas identification scores showed no significant difference (p = 0.073). Survey data revealed higher ratings for verification and inquiry behaviors (p < 0.001). Process documentation showed high fidelity, with 93% of Prompt Loop students submitting required AI-interaction screenshots and a mean of 2.1 ± 1.5 error-correction entries. A prompt-guided AI workflow improved morphologic reasoning in histology. When AI outputs were treated as provisional claims and routed through a structured verification process, the Prompt Loop may provide a practical model for supporting verification-oriented morphologic reasoning in histology.