From the Spontaneous Use of Generative Artificial Intelligence to the Development of an Intelligent Mock Exam for Enamed
DOI:
https://doi.org/10.14295/idonline.v20i83.4525Keywords:
Self-regulated learning, Co-regulated learning, Generative artificial intelligence, Medical educationAbstract
The growing use of generative artificial intelligence by medical students has transformed study strategies, particularly regarding the search for explanations, summaries, and examples, as well as the resolution of doubts. However, these strategies are often implemented in a fragmented manner, requiring students to switch between mock exam platforms and external AI tools. This study aimed to analyze the AI-supported self-regulated learning strategies used by medical students and, based on the results, propose a conceptual model for an Intelligent Mock Exam designed to prepare students for the National Exam for the Assessment of Medical Education (Enamed). This is an applied, exploratory study with a quantitative approach, conducted by administering a structured questionnaire to medical students at the Federal University of the São Francisco Valley (UNIVASF), grounded in the cyclical model of self-regulated learning. The results revealed a higher prevalence of AI usage in strategies related to the execution and monitoring of learning—such as clarifying doubts, rephrasing explanations, and generating examples and similar exercises—whereas planning and self-reflection strategies were used less frequently. Based on these findings, the architecture for the "CogneMix" Intelligent Mock Exam was proposed; it is designed to integrate AI capabilities directly into the mock exam review workflow, organizing learning across varying levels of depth—ranging from performance analysis by knowledge area to the contextualized review of specific questions. The solution incorporates features such as intelligent summaries, adaptive explanations, clinical cases, educational digital twins, and the generation of similar questions, using the strategies already adopted by students as a starting point to foster planning and self-reflection processes. It is concluded that the contextualized integration of artificial intelligence into the learning workflow has the potential to reduce the fragmentation of study practices and foster the development of self-regulated learning in medical education.
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Copyright (c) 2026 João Carlos Sedraz Silva, João Carlos Sedraz Silva Filho
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