JOURNAL ARTICLE

Using Peer Assessment Leveraging Large Language Models in Software Engineering Education.

  • Published In: International Journal of Software Engineering & Knowledge Engineering, 2025, v. 35, n. 1. P. 1 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Fiore, Marco; Mongiello, Marina 3 of 3

Abstract

This paper explores the integration of generative AI and large language models into the realm of software engineering education and training, with a specific focus on the transformation of traditional peer assessment methodologies. The motivation stems from the growing demand for innovative educational techniques that can effectively engage and empower learners in mastering Software Engineering principles. The proposed approach involves presenting students with modeling exercises solved by ChatGPT, prompting them to critically evaluate and provide constructive feedback on the generated solutions. By engaging students in a dialogue with the AI model, we aim to foster a dynamic learning environment where learners can articulate their considerations and insights, thereby enhancing their comprehension of software engineering principles, critical thinking and self evaluation skills. Preliminary results from pilot implementations indicate promising outcomes, suggesting that this approach not only enhances the quality of peer feedback but also contributes to a more interactive and engaging educational experience. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Software Engineering & Knowledge Engineering. 2025/01, Vol. 35, Issue 1, p1
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2025
  • ISSN:0218-1940
  • DOI:10.1142/S0218194024500359
  • Accession Number:182210968
  • Copyright Statement:Copyright of International Journal of Software Engineering & Knowledge Engineering is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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