AI智能体链式工作流驱动下高校课程教学环节效能提升的策略构建与实践

陈 培芳
吉利学院　艺术设计学院

摘要


在教育数字化转型背景下，本文探索AI智能体链式工作流驱动高校课程教学效能提升的策略与实践。分析该工作流在教学全流程的技术特性，包括环节连贯性、自动化与智能化、个性化支持及可追溯性等，及其在减轻教师负担、优化学习体验等方面的教育价值。同时指出其存在技术落地难、数据伦理风险、学科适配性差异及评估不足等问题，进而从技术优化、数据规范、融合推广及评估机制等方面提出对策，为高校教学创新提供理论与实践参考。

关键词


AI智能体；链式工作流；路由工作流；教学

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参考


[1]罗杨洋.基于学生在线行为的混合课程学习成绩预测研究[M].北京：清华大学出版社，2023.

[2]Neumann M. AI education matters: a first introduction to modeling and learning using the data science workflow [J]. AI Matters, 2019, 5(3): 21-24.

[3]Lutfiani N, Wijono S, Rahardja U, et al. A bibliometric study: Recommendation based on artificial intelligence for ilearning education [J]. Aptisi Transactions on Technopreneurship, 2023, 5(2): 109-117.

[4]Mehdaoui A. Unveiling Barriers and Challenges of AI Technology Integration in Education: Assessing Teachers’ Perceptions, Readiness and Anticipated Resistance [J]. Futurity Education, 2024, 4(4): 95-108.

[5]Yu H. The application and challenges of ChatGPT in educational transformation: New demands for teachers’ roles [J]. Heliyon, 2024, 10(2).

[6]Chan C K Y. A comprehensive AI policy education framework for university teaching and learning [J]. International Journal of Educational Technology in Higher Education, 2023, 20(1): 38.

[7]Mhlanga D. Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning [M]//MHLANGA D. FinTech and Artificial Intelligence for Sustainable Development: The Role of Smart Technologies in Achieving Development Goals. Cham; Springer Nature Switzerland. 2023: 387-409.


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