Challenges and Optimization Strategies of Artificial Intelligence on Practical Teaching of Software Engineering

软件工程作为理论与实践并重的学科，其教学模式需紧跟技术发展与行业需求。文章分析了人工智能技术对软件工程实践教学带来的多维度挑战，包括学生独立思维能力与编程能力弱化、学术诚信风险增加与评价体系失准、教师角色转型及教学策略适配难题，以及学习动力可能受到抑制等问题，并针对性地提出了教学方法革新、协作学习模式优化、实践驱动的教学内容设计以及评价体系完善等一系列优化策略。通过系统性教学改革，实现技术赋能与教育目标的深度协同，培养适应产业变革的高素质软件工程人才。Software engineering, as a discipline that emphasizes both theory and practice, requires its teaching model to keep pace with technological advancements and industry demands. This paper analyzes the multi-dimensional challenges posed by artificial intelligence (AI) technologies to the practical teaching of software engineering. These challenges include the weakening of students’ independent thinking and programming abilities, increased risks of academic integrity violations and inaccuracies in the evaluation system, difficulties in the transformation of teachers’ roles and adaptation of teaching strategies, as well as potential suppression of students’ learning motivation. In response to these challenges, a series of optimization strategies are proposed, including the innovation of teaching methods, the optimization of collaborative learning models, the design of practice-driven teaching content, and the improvement of the evaluation system. The aim is to achieve a profound synergy between technological empowerment and educational objectives through systematic teaching reforms, thereby cultivating high-quality software engineering talents who are well-equipped to adapt to industrial transformations.