Harnessing generative AI in exercise and sports science education: enhancing real-world learning and overcoming traditional barriers in data analysis
Lewis A. Fazackerley, Dimitri Perrin, Geoffrey M. Minett
Abstract
Generative AI (GenAI) offers transformative potential for exercise and sports science education, addressing traditional data analysis and visualization barriers while promoting real-world learning. This Perspectives article explores how integrating GenAI into exercise and sports science degrees can enhance students' ability to interpret complex physiological data, improve their analytical skills, and foster creativity in problem-solving. By automating routine technical tasks such as data cleaning and visualization, GenAI allows students to focus on critical interpretation, inquiry-based learning, and evidence-based application. An example lesson plan is provided, incorporating GenAI tools to simulate real-world data analysis tasks, helping students develop hands-on data interpretation and decision-making skills. Additionally, the article discusses strategies for responsible implementation, ensuring that ethical considerations and foundational learning are prioritized.