What Platform Scores Miss: Multidimensional Evaluation of AI Teaching Agents in Medical Education

Background Large language model (LLM)–based AI teaching agents are increasingly used in medical education, yet their pedagogical quality is typically judged by platform-generated scores whose scoring criteria are undisclosed and may not reflect the teaching quality of the agent. Objective This study aimed to develop and validate a multidimensional rubric for evaluating AI teaching agents and to examine the correspondence between platform scores and rubric-based teaching quality. Methods Eight AI teaching agents covering an endocrinology curriculum were deployed across 4 role-play paradigms (patient, student, expert, and family). Twenty-two fourth-year medical students generated 167 dialogues, which were scored both by the platform and by an independently applied 8-dimension rubric (100 points, covering knowledge accuracy, pedagogical guidance, knowledge coverage, role-play quality, difficulty calibration, medical safety, student engagement, and feedback quality). Each dialogue was scored 4 times by a primary evaluator (Claude Opus 4.8; mean within-model SD 0.36), with 2 additional LLMs as robustness checks; 40 dialogues spanning all agents were rescored by a medical-education expert for validation. Results Platform and rubric rankings diverged for most agents: the agent ranked third by the platform ranked last on rubric-based quality, and the platform’s fourth-ranked agent ranked first. Agents differed most on knowledge-related dimensions (knowledge coverage coefficient of variation=27.3%) and least on role-play quality (coefficient of variation=5.7%), while difficulty calibration was a shared weakness. In a case-level observation, one agent revised specifically to strengthen empathy attained high role-play quality yet the lowest knowledge coverage of all agents. AI scores agreed with expert ratings at the total-score level (intraclass correlation coefficient=0.51) and on cognitive-process dimensions, but agreement was low for the more subjective dimensions. Student gender showed no detectable effect, though this analysis was underpowered. Conclusions In this exploratory study, platform-generated scores reflected a construct different from agent teaching quality and should be used as a complement rather than as the sole quality indicator. The 8-dimension rubric provides a transparent, standardized alternative that reveals differences missed by platform scores, including a lack of association between empathy and knowledge coverage that warrants attention in future agent design.