下一代教育：增强人工智能以支持微学习
Next-Gen Education: Enhancing AI for Microlearning
AI总结：
本文研究如何利用ChatGPT等AI工具自动生成微学习材料，以减轻教师负担并提升计算机科学教育中的学生参与度和学习效果。
摘要：
本文探讨将微学习策略融入大学课程，尤其是计算机科学教育，以应对新冠疫情后美国大学课堂出勤率和参与度下降的问题。随着学生越来越多地选择远程学习和录播课程，传统教育方法难以维持参与度和教学效果。微学习将复杂学科拆分为易于管理的单元，旨在应对注意力持续时间缩短的问题并提升教育成果。它采用视频、测验、闪卡和基于情境的练习等互动形式，对于需要深入理解和持续实践的算法与编程逻辑等主题尤其有益。微学习的采用往往受限于制作此类材料所需的工作量。本文提出利用人工智能工具，特别是ChatGPT，通过自动生成补充材料来减轻教育者的工作负担。虽然人工智能可以自动完成某些任务，但教育者在引导和塑造学习过程中仍然不可或缺。这种人工智能增强的方法确保课程内容紧跟最新研究和技术，同时由教育者提供背景和见解。通过考察人工智能在微学习中的能力，本研究表明其具有变革计算机科学教育实践和成果的潜力，为将先进技术与既有教学方法相结合提供了一个实用模型。
英文摘要：
This paper explores integrating microlearning strategies into university curricula, particularly in computer science education, to counteract the decline in class attendance and engagement in US universities after COVID. As students increasingly opt for remote learning and recorded lectures, traditional educational approaches struggle to maintain engagement and effectiveness. Microlearning, which breaks complex subjects into manageable units, is proposed to address shorter attention spans and enhance educational outcomes. It uses interactive formats such as videos, quizzes, flashcards, and scenario-based exercises, which are especially beneficial for topics like algorithms and programming logic requiring deep understanding and ongoing practice. Adoption of microlearning is often limited by the effort needed to create such materials. This paper proposes leveraging AI tools, specifically ChatGPT, to reduce the workload for educators by automating the creation of supplementary materials. While AI can automate certain tasks, educators remain essential in guiding and shaping the learning process. This AI-enhanced approach ensures course content is kept current with the latest research and technology, with educators providing context and insights. By examining AI capabilities in microlearning, this study shows the potential to transform educational practices and outcomes in computer science, offering a practical model for combining advanced technology with established teaching methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
