%0 Journal Article
%T AI赋能下的理论力学课程教学改革探索
Exploration of AI-Enabled Teaching Reform in Theoretical Mechanics Course
%A 闫高明
%A 梁小燕
%J Advances in Education
%P 1376-1381
%@ 2160-7303
%D 2025
%I Hans Publishing
%R 10.12677/ae.2025.1561144
%X 理论力学作为工程类专业的核心基础课程，具有高度抽象性与推理性，长期存在“难教、难学、难评”等教学痛点。传统教学模式难以满足新时代工科教育对学生创新能力与工程实践能力的培养需求。本文基于“新工科”建设背景，系统分析理论力学课程的教学特点与改革必要性，提出以人工智能(AI)技术赋能课程教学改革的新路径。通过构建“教–学–评”一体化智能教学体系，从AI辅助教学设计、AI个性化学习、AI驱动智能评价三大核心环节入手，开展深入探讨研究。研究表明，AI技术在提升教学效率、增强学习体验、优化评价机制方面具有显著成效，为理论力学教学的数字化转型与高质量发展提供了新思路与新支撑，对新时代工程人才培养具有重要意义。
Theoretical Mechanics, as a fundamental course in engineering education, is known for its high level of abstraction and rigorous logical structure. It has long been associated with persistent challenges in instruction, learning, and assessment. Traditional pedagogical approaches often fall short in cultivating the innovation capacity and practical problem-solving skills required by contemporary engineering students. In response to the demands of the “New Engineering” education reform, this paper examines the inherent characteristics and pressing need for transformation in Theoretical Mechanics teaching. It proposes a novel, AI-driven reform framework centered on an integrated “teaching-learning-assessment” system. This study conducts an in-depth investigation into the construction of an integrated intelligent teaching system that unifies teaching, learning, and assessment, focusing on three core dimensions: AI-assisted instructional design, AI-enabled personalized learning, and AI-driven intelligent evaluation. The application of AI technologies demonstrates significant potential in enhancing instructional effectiveness, enriching learner engagement, and establishing data-informed, adaptive evaluation models. This approach provides a forward-looking pathway for the digital transformation of Theoretical Mechanics education and offers valuable insights into the training of high-level, interdisciplinary engineering talent in the new era.
%K 理论力学，
%K 人工智能，
%K 智能教学体系，
%K 个性化学习，
%K 智能评价
Theoretical Mechanics
%K Artificial Intelligence
%K Intelligent Teaching System
%K Personalized Learning
%K Intelligent Assessment
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=118690