AI-Enabled Teaching in University Physics Elective Courses
Abstract
With the rapid development of Artificial Intelligence (AI) technology, the field of education is undergoing unprecedented transformation. In higher education, AI is gradually permeating various aspects, including curriculum design, teaching methods, and learning assessment. This paper focuses on university physics elective courses, exploring how AI can enhance teaching quality and learning outcomes
through intelligent recommendation systems, personalized learning, virtual simulation experiments, and automated evaluation and feedback.
By analyzing real-world cases, the paper examines the current advantages and challenges of AI-enabled teaching and proposes targeted optimization suggestions, aiming to provide theoretical references and practical pathways for the reform of physics education in higher education
institutions.
through intelligent recommendation systems, personalized learning, virtual simulation experiments, and automated evaluation and feedback.
By analyzing real-world cases, the paper examines the current advantages and challenges of AI-enabled teaching and proposes targeted optimization suggestions, aiming to provide theoretical references and practical pathways for the reform of physics education in higher education
institutions.
Keywords
Artificial Intelligence (AI); University Physics; Elective Courses; Intelligent Teaching; Personalized Learning
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DOI: http://dx.doi.org/10.70711/neet.v3i6.7102
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