METHODOLOGICAL ASPECTS OF USING ARTIFICIAL INTELLIGENCE IN THE DEVELOPMENT OF PRACTICAL MATERIALS IN MEDICAL CHEMISTRY: EXPERIENCE IN CREATING A WORKBOOK

Abstract

The article examines the methodological aspects of using artificial intelligence (AI) as a tool for developing practical educational materials in medical chemistry. Based on the authors’ experience in creating a workbook for master’s students in the specialty General Medicine, the process of integrating AI (using the Grok model as an example) into the preparation of various types of tasks is analyzed, including experimental protocols, calculation problems, modeling of biochemical processes, and clinically oriented case studies The sequence of workflow stages is described: defining the workbook’s structure and competency-based objectives, formulating precise prompts for content generation, initial material generation, expert evaluation by chemistry and biology instructors, and systematic editing to ensure scientific accuracy, laboratory safety, compliance with the educational program, and methodological appropriateness for future physicians. Key methodological advantages of using AI were identified: significant reduction in the time required to create diverse tasks, increased variability and individualization of exercises, rapid adaptation of materials to current scientific data and clinical contexts, and generation of creative experimental ideas that are rarely found in traditional manuals. At the same time, typical limitations of AI-generated content are discussed, including terminological inaccuracies, excessive or insufficient complexity, and the need for mandatory verification of information accuracy. Effective strategies to overcome these limitations through multi-level review and refinement are also presented. Concrete examples of tasks are provided, from the initial AI-generated version to the final edited version, with explanations of the changes made and their pedagogical justification. The article demonstrates how such tasks contribute to the development of clinical thinking, critical analysis skills, and the application of chemical knowledge in evidencebased medical practice. The conclusions formulate practical recommendations for instructors of natural science disciplines in medical higher education institutions regarding the optimal use of AI: principles of effective prompt engineering, criteria for expert evaluation, and the rational combination of AI-generated content with traditional material development methods. The prospects for further development of these approaches in creating interactive and adaptive educational resources are emphasized