IMPROVING THE METHODOLOGY OF TEACHING BIOPHYSICS THROUGH THE CREATION OF AN ARTIFICIAL INTELLIGENCE-BASED ANALYTICAL ENVIRONMENT
Keywords:
biophysics education, artificial intelligence in education, analytical learning environment, adaptive assessment, simulation-based modeling, digital pedagogy, medical education innovation.Abstract
his article explores the theoretical foundations, methodological principles, and pedagogical effectiveness of integrating artificial intelligence (AI) technologies and analytical learning environments into the teaching of biophysics in medical education. The study focuses on creating an AI-driven digital ecosystem that enhances students’ analytical thinking, problem-solving skills, and interdisciplinary integration between physics and medicine. The proposed methodology incorporates intelligent tutoring systems, adaptive assessment tools, data-driven analytics, and simulation-based modeling of biophysical processes.
The research demonstrates that the implementation of AI-supported analytical platforms significantly improves students’ conceptual understanding of complex biophysical phenomena, including bioelectricity, biomechanics, molecular diffusion, and medical imaging physics. The results indicate measurable growth in academic performance, analytical competency, and independent learning skills. The article concludes that the integration of artificial intelligence into biophysics education represents a strategic direction for modernizing medical curricula and preparing future physicians capable of operating in data-intensive healthcare environments.
