Assessing ChatGPT’s Mastery of Bloom’s Taxonomy using psychosomatic medicine exam questions

Introduction Large language models (LLMs) such as GPT-4 are increasingly used in medicine and medical education. However, these models are prone to “hallucinations” – outputs that sound convincing while being factually incorrect. It is currently unknown how these errors by LLMs relate to the different cognitive levels defined in Bloom’s Taxonomy. Methods We used a large dataset of psychosomatic medicine multiple-choice questions (MCQ) (N = 307) with real-world results derived from medical school exams. GPT-4 answered the MCQs using two distinct prompt versions – detailed and short. The answers were analysed using a quantitative and qualitative approach. We focussed on incorrectly answered questions, categorizing reasoning errors according to Bloom’s Taxonomy. Results GPT-4’s performance in answering exam questions yielded a high success rate: 93% (284/307) for the detailed prompt and 91% (278/307) for the short prompt. Questions answered correctly by GPT-4 had a statistically significant higher difficulty compared to questions that GPT-4 answered incorrectly (p=0.002 for the detailed prompt and p<0.001 for the short prompt). Independent of the prompt, GPT-4’s lowest exam performance was 78.9%, always surpassing the pass threshold. Our qualitative analysis of incorrect answers, based on Bloom’s Taxonomy, showed errors mainly in the “remember” (29/68) and “understand” (23/68) cognitive levels. Specific issues arose in recalling details, understanding conceptual relationships, and adhering to standardized guidelines. Discussion GPT-4 displayed a remarkable success rate when confronted with psychosomatic medicine multiple-choice exam questions, aligning with previous findings. When evaluated against Bloom’s hierarchical framework, our data revealed that GPT-4 occasionally ignored specific facts (“remember”), provided illogical reasoning (“understand”), or failed to apply concepts to a new situation (“apply”). These errors, though confidently presented, could be attributed to inherent model biases and the tendency to generate outputs that maximize likelihood. Conclusion While GPT-4 mostly excels at medical exam questions, discerning its occasional cognitive errors is crucial. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Ethics Committee of the Faculty of Medicine at University Hospital Tuebingen approved the study (number 076/2023A). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors