AI-Driven Personalized Learning Profiles to Enhance Student Performance in Basic Physics: A Pilot Study

Artificial intelligence (AI) offers transformative potential in education by enabling personalized learning tailored to individual needs. This study evaluates AI's impact on improving performance in basic physics education at Tecnológico de Monterrey, Campus Cuernavaca (Mexico). The research aimed to address knowledge gaps and boost student confidence by creating customized learning profiles. Using online questionnaires to assess learning styles-self-taught, auditory, visual, or practical-and diagnostic tests on physics topics like kinematics and Newton's laws, AI-generated profiles informed the design of tailored activities, exams, and feedback. In a pilot study with 31 first-semester engineering students (aged 17-23), AI identified weaknesses in vector analysis and energy conservation, enabling targeted interventions. Results showed the experimental group achieved a 17 % higher average in final assessments and a 20 % increase in motivation compared to the control group. Additionally, 75 % of participants expressed high satisfaction, emphasizing personalized feedback's role in enhancing focus and engagement. Examples of implementation included adaptive materials like visual simulations for visual learners and podcasts for auditory learners, demonstrating replicability in diverse educational contexts. This study contributes to knowledge by illustrating AI's capacity to align educational strategies with individual needs, fostering better comprehension and motivation. It also highlights AI's potential to transform traditional teaching methods into scalable, inclusive solutions. Future research will explore the broader applicability of these strategies across disciplines and assess their long-term impact, offering a roadmap for integrating AI into global education systems.