Designing an Academic Software Factory for AI-Based Systems Development from Professors' Perspectives
DOI:
https://doi.org/10.5753/rbie.2025.5647Keywords:
Academic Software Factory, AI-Based Systems, Software Engineering, SurveyAbstract
The increasing complexity of AI-based systems poses significant challenges in software engineering education, requiring an integrated approach that combines AI techniques with software development methodologies. Traditional educational models often fail to bridge this gap, limiting students' ability to apply theoretical knowledge to real-world AI-driven software solutions. This paper proposes the design of an Academic Software Factory (ASF) tailored for AI-based systems development within undergraduate software engineering education. The proposed ASF integrates Competency-Based Learning, Bloom’s Taxonomy, and Agile Scrum methodologies to provide a structured and industry-aligned learning experience. The ASF framework is developed through a four-phase methodology: (i) identifying characteristics and roles in AI-based system development, (ii) designing the ASF curriculum, (iii) defining a teaching methodology aligned with AI-specific software engineering challenges, and (iv) evaluating the ASF design through expert validation. A survey was conducted with ten professors specializing in software engineering and AI for preliminary validation. The feedback highlighted the ASF's potential to enhance educational outcomes, suggesting refinements such as assigning specialist roles to faculty members, reducing redundancy in pre-existing content, and emphasizing developing and presenting a minimum viable product as a key learning outcome.
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Copyright (c) 2025 Alinne C. Corrêa Souza, Francisco Carlos Monteiro Souza, Thiago Damasceno Cordeiro
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