Teaching Requirements Engineering for Human-Centered AI: A Classroom Experience with the RE4HCAI Framework
Abstract
Requirements Engineering for AI-based systems (RE4AI) demands approaches that integrate technical, social, and human-centered dimensions. For instance, when eliciting requirements for AI systems, one must account for data biases and imbalances that may lead to decisions reinforcing stereotypes and excluding certain user profiles without making such effects visible. Despite the topic’s relevance, teaching RE4AI remains nascent and lacks suitable pedagogical methods. To address this gap, this study examines the use of the RE4HCAI framework as a pedagogical aid for teaching requirements elicitation in AI systems, attending to technical, human, and social dimensions. We conducted a classroom activity with 86 students, applying the framework and collecting perceptions of its usefulness. Among participants, 66% reported that using the RE4HCAI framework fostered reflection on themes such as responsibility, bias, and explainability. Despite its formative potential, students noted challenges related to linguistic complexity, the need for prior knowledge, and the clarity of instructions. This work offers preliminary empirical indications and provides structured materials that can support instructors in teaching RE4AI.
Keywords:
Requirements Engineering, Artificial Intelligence Systems, Software Engineering Education, RE4HCAI Framework
References
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Khlood Ahmad, Mohamed Abdelrazek, Chetan Arora, Muneera Bano, and John Grundy. 2023. Requirements practices and gaps when engineering humancentered Artificial Intelligence systems. Applied Soft Computing 143 (2023), 110421.
Khlood Ahmad, Mohamed Abdelrazek, Chetan Arora, John Grundy, and Muneera Bano. 2023. Requirements elicitation and modelling of artificial intelligence systems: An empirical study. arXiv preprint arXiv:2302.06034 (2023).
Mahir Akgun and Hadi Hosseini. 2025. AI Education in a Mirror: Challenges Faced by Academic and Industry Experts. arXiv preprint arXiv:2505.02856 (2025).
Mamdouh Alenezi and Mohammed Akour. 2025. Ai-driven innovations in software engineering: a review of current practices and future directions. Applied Sciences 15, 3 (2025), 1344.
Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. 2019. Software engineering for machine learning: A case study. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP). IEEE, 291–300.
Beatriz Braga Batista, Márcia Sampaio Lima, and Tayana Uchôa Conte. 2024. Teaching Requirements Engineering for AI: A Goal-Oriented Approach in Software Engineering Courses. In Proceedings of the XXIII Brazilian Symposium on Software Quality. 613–623.
Jan Bosch, Helena Holmström Olsson, and Ivica Crnkovic. 2021. Engineering ai systems: A research agenda. Artificial intelligence paradigms for smart cyberphysical systems (2021), 1–19.
Marian Daun, AliciaMGrubb, Viktoria Stenkova, and Bastian Tenbergen. 2023. A systematic literature reviewof requirements engineering education. Requirements Engineering 28, 2 (2023), 145–175.
Vincenzo De Martino and Fabio Palomba. 2025. Classification and challenges of non-functional requirements in ML-enabled systems: A systematic literature review. Information and Software Technology (2025), 107678.
Maria Alice de Souza Macedo, Carla Bezerra, and Emanuel Coutinho. 2024. Uma Pesquisa Qualitativa do Contexto de Ensino em Requisitos de Software no Brasil. In Workshop sobre Educação em Computação (WEI). SBC, 669–679.
Vahid Garousi, Görkem Giray, Eray Tüzün, Cagatay Catal, and Michael Felderer. 2019. Aligning software engineering education with industrial needs: A metaanalysis. Journal of Systems and Software 156 (2019), 65–83.
Umm-E Habiba, Justus Bogner, and Stefan Wagner. 2022. Can requirements engineering support explainable artificial intelligence? towards a user-centric approach for explainability requirements. In 2022 IEEE 30th international requirements engineering conference workshops (REW). IEEE, 162–165.
Umm-e Habiba, Markus Haug, Justus Bogner, and Stefan Wagner. 2024. How mature is requirements engineering for AI-based systems? A systematic mapping study on practices, challenges, and future research directions. Requirements Engineering 29, 4 (2024), 567–600.
Jennifer Horkoff. 2019. Non-functional requirements for machine learning: Challenges and new directions. In 2019 IEEE 27th international requirements engineering conference (RE). IEEE, 386–391.
Silverio Martínez-Fernández, Justus Bogner, Xavier Franch, Marc Oriol, Julien Siebert, Adam Trendowicz, Anna Maria Vollmer, and Stefan Wagner. 2022. Software engineering for AI-based systems: a survey. ACM Transactions on Software Engineering and Methodology (TOSEM) 31, 2 (2022), 1–59.
David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015. Hidden technical debt in machine learning systems. Advances in neural information processing systems 28 (2015).
Anselm Strauss and Juliet Corbin. 1998. Basics of qualitative research techniques. (1998).
Andreas Vogelsang and Markus Borg. 2019. Requirements engineering for machine learning: Perspectives from data scientists. In 2019 IEEE 27th international requirements engineering conference workshops (REW). IEEE, 245–251.
Published
2025-11-04
How to Cite
SANTOS, Bruna; LIMA, Márcia; RIBEIRO, Márcio; CONTE, Tayana.
Teaching Requirements Engineering for Human-Centered AI: A Classroom Experience with the RE4HCAI Framework. In: BRAZILIAN SOFTWARE QUALITY SYMPOSIUM (SBQS), 24. , 2025, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 540-550.
DOI: https://doi.org/10.5753/sbqs.2025.15264.
