人工智能与大学物理课程思政深度融合的探索与实践

Exploration and Practice of Deep Integration of Artificial Intelligence and Ideological and Political Education in University Physics Courses

摘要: 在新时代高等教育全面落实立德树人根本任务的背景下，课程思政已从理念倡导进入系统推进与质量提升阶段。理工科基础课程由于知识体系抽象、理论推导严密、价值元素呈现隐性等特点，在课程思政实施过程中面临融入路径不清、实施效果不显著等现实困境。本文以《大学物理》课程中角动量守恒教学内容为研究对象，在系统梳理课程思政相关理论与理工科课程特点的基础上，探索构建了一种以智能技术支持为工具、以学科核心概念教学为载体的课程思政深度融合教学模式。该研究有助于降低学生对抽象物理概念的理解难度，促进深度学习的发生，不仅增强学生对科学精神、工程伦理与家国责任的认同感，还引导其辩证看待科技发展中的挫折、诚信与社会责任等多维价值问题，实现知识传授、能力培养与价值引领的协同推进。本文的研究为理工科基础课程在人工智能背景下推进课程思政内涵式发展提供了可借鉴的实践范式。

Abstract: In the new era, with the fundamental task of fostering virtue through education being fully implemented in higher education, curriculum-based ideological and political education has progressed from conceptual advocacy to systematic advancement and quality enhancement. Foundational science and engineering courses, characterized by their abstract knowledge systems, rigorous theoretical derivations, and implicit value elements, often face practical challenges such as unclear integration pathways and limited effectiveness during implementation. Taking the teaching of angular momentum conservation in the University Physics course as a case study, and based on a systematic review of relevant theories in ideological and political education and the features of science and engineering curricula, this paper explores and constructs a deeply integrated teaching model. This model employs intelligent technology as a supportive tool and centers on the instruction of core disciplinary concepts. The research helps reduce students’ difficulty in understanding abstract physical concepts, promotes deeper learning, and not only strengthens their identification with scientific spirit, engineering ethics, and national responsibility but also guides them to dialectically examine multidimensional values in technological development, including resilience, integrity, and social responsibility. It thereby achieves the coordinated advancement of knowledge transmission, ability cultivation, and value guidance. This study provides a practical reference for promoting the substantive development of ideological-political education in foundational science and engineering courses within the context of artificial intelligence.