人工智能技术赋能计算机基础教学改革探索

Exploring the Reform of Basic Computer Science Education Empowered by AI Technology

摘要: 目前大学的计算机与人工智能(Artificial Intelligence, AI)基础课程在教学中普遍存在技术迭代快速而内容更新滞后、大班授课难以兼顾个体差异、实践教学与应用脱节、教学评价偏重结果而忽视过程等问题。本研究分析传统教学范式与人工智能技术发展的深层矛盾，提出以AI技术赋能教学的改革思路，探索构建“智能辅助教学–个性化学习–实践能力进阶–过程性智慧评价”四位一体的教学改革框架。通过大语言模型辅助课程内容学习与即时答疑、基于学情数据的个性化学习推荐、虚实结合的智能实践平台及多维度过程性评价等措施，探索AI技术与计算机基础课程教学的深度融合，旨在提升教学质效与人才培养质量，为高校智能化教学转型提供实践参考。

Abstract: Currently, introductory courses in computer science and artificial intelligence (AI) at universities generally face issues such as rapid technological advancements outpacing content updates, large class sizes that make it difficult to address individual differences, a disconnect between practical instruction and real-world applications, and an evaluation system that emphasizes outcomes while neglecting the learning process. This study analyzes the deep-seated contradictions between traditional teaching paradigms and the development of artificial intelligence technology, proposes a reform approach that leverages AI technology to enhance teaching, and explores the construction of a four-pronged teaching reform framework comprising “intelligent teaching assistance - personalized learning - progressive practical skills development - process-oriented intelligent assessment”. Through measures such as using large language models to support course content learning and provide real-time Q&A, personalized learning recommendations based on student performance data, a blended virtual-physical intelligent practical platform, and multidimensional formative assessment, this study explores the deep integration of AI technology with the teaching of foundational computer science courses. The aim is to enhance the quality and efficiency of teaching as well as the quality of talent development, thereby providing practical guidance for the transition to intelligent teaching in higher education institutions.