Teaching Practice of Explainable Feedback in Programming Empowered by Large Language Models

Xinzhi Li; Yixin Zhang; Jingjing Xie; Rui Zhang; Mengmeng Liu; Jing Zhao

doi:10.6918/IJOSSER.202607_9(7).0008

Teaching Practice of Explainable Feedback in Programming Empowered by Large Language Models

Xinzhi Li, Yixin Zhang, Jingjing Xie, Rui Zhang, Mengmeng Liu, Jing Zhao

International Journal of Social Science and Education Research, (2026), Vol.9, No.7, pp.60-67

Published: July 12, 2026

DOI: 10.6918/IJOSSER.202607_9(7).0008

PDF: Download Full Text PDF

Abstract

Against the background of artificial intelligence and large language models, undergraduate natural language processing programming tasks often face delayed feedback, interrupted debugging processes, and insufficient result interpretation. This paper analyzes the scaffolding role of generative artificial intelligence in error diagnosis, step-by-step prompting, and autonomous revision, and proposes an explainable feedback approach characterized by “AI-assisted diagnosis, teacher-guided rule constraints, student-led revision, and process-based reflection”. Taking named entity recognition and sentiment analysis as practical tasks, this paper designs an AI-supported feedback process for programming ability development to improve feedback timeliness, strengthen problem-locating and debugging abilities, and enhance the problem-solving experience in complex programming tasks.

Keywords: Large language models; generative artificial intelligence; programming teaching; explainable feedback; natural language processing; cognitive scaffolding.

APA Citation: Xinzhi Li, Yixin Zhang, Jingjing Xie, Rui Zhang, Mengmeng Liu, Jing Zhao (2026). Teaching Practice of Explainable Feedback in Programming Empowered by Large Language Models. International Journal of Social Science and Education Research, 9(7), 60-67. https://doi.org/10.6918/IJOSSER.202607_9(7).0008

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