This study addresses the pivotal role of university physics courses in the curriculum of non-physics science and engineering majors, proposing a novel deep learning-driven approach to enhance the instructional design and thereby improve learning efficacy and student engagement. The investigation stems from prevalent issues in contemporary physics education, notably the deficiency in practicality, innovation, and applicability, alongside waning student interest and motivation. The core of the research comprises the meticulous design, meticulous implementation, and real-world application of a transformer-based neural network architecture. Transformers, known for their prowess in handling sequential data, enable a sophisticated understanding and processing of complex physical concepts, marking a significant advancement in educational methodology. Findings reveal that the proposed model excels in delivering swift, precise, and interpretable resolutions to physics challenges, demonstrating exceptional generalization capabilities and adaptability surpassing conventional teaching methodologies. Its performance underscores the potential for transforming the learning landscape by fostering a deeper comprehension of physical phenomena. The overarching significance of this work lies in the establishment of a robust framework for university physics course design. By leveraging the model’s capabilities, it facilitates not only the acquisition of fundamental knowledge but also nurtures critical thinking, problem-solving, and innovative application among students. Consequently, this research pioneers a new paradigm for integrating deep learning technologies into physics pedagogy, offering a blueprint for enhancing educational practices and enriching the learning journey for aspiring scientists and engineers.