Integrating Artificial Intelligence into the Higher Education of Physics: Theoretical Frameworks and Pedagogical Strategies

Artificial Intelligence (AI) is progressively impacting higher education; yet its incorporation into physics instruction necessitates more than merely adopting new technologies. This article examines the theoretical principles and teaching methodologies that can facilitate the effective integration of AI into university-level physics education. Utilizing constructivism, cognitive load theory, connectivism, and epistemic cognition, the study asserts that AI functions not merely as a technology adjunct but as a cognitive collaborator that can augment conceptual comprehension, representational proficiency, and inquiry-driven learning. Specific educational tactics are examined, including the utilization of AI to enhance active learning, scaffold inquiry, facilitate multimodal reasoning, and deliver formative feedback. The article contends that successful AI integration should maintain the intellectual integrity of physics while enhancing chances for cooperation, reflection, and metacognitive growth, by underscoring congruence with disciplinary epistemologies. The future of AI in physics education is predicated on design-based pedagogical innovation, wherein intelligent technologies are deliberately integrated into teaching methods to enhance scientific engagement and critical awareness among students.