Title:
Responsible Integration of Generative AI in Software Engineering Education [Download]Authors:
Miguel Matey-Sanz, Carlos Granell, Ramón A. Mollineda
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Abstract:
Generative AI (GenAI) is rapidly transforming higher education, requiring a consequent shift in teaching methodologies. This article describes a two-year teaching experience (2024/2025 and 2025/2026) integrating the free, responsible, and critical use of GenAI into a coordinated, complex software development project for fourth-year Computer Science students. The study aimed to explore GenAI’s potential as a learning support tool across the software lifecycle and analyze student interaction patterns. The methodology evolved over two academic years: an initial exploratory phase (2024/2025) was followed by a revised implementation (2025/2026) that incorporated active training seminars focused on prompt engineering and critical analysis. Results show that GenAI is highly effective as a “companion” for automating low-level tasks such as code implementation, debugging, and test scenario specification, allowing students to focus on higher-level design and decision making. Initial findings revealed student frustration when GenAI struggled to reason about advanced project contexts, often due to a lack of prompt expertise. However, the corrective training actions successfully mitigated this frustration, significantly improving the critical interpretation of GenAI-generated outputs and the control of “hallucinations”. Usage patterns were asymmetric, with general-purpose tools dominating early stages and specialized tools gaining relevance as project complexity increased. This experience confirms the value of GenAI in promoting critical and responsible use within software engineering education, supporting a shift from a basic skill focus to one centered on analysis, integration, and high-level problem-solving.
DOI:
How to cite:
Miguel Matey-Sanz, Carlos Granell, Ramón A. Mollineda, "Responsible Integration of Generative AI in Software Engineering Education", IEEE-RITA, vol. 21, no. 1, pp. 375-384, Jan. 2026. doi: 10.1109/RITA.2026.3694480