Abstract:

The rapid development of generative AI (GenAI) in higher education has introduced new opportunities and challenges for requirements engineering (RE). While previous research has largely focused on students’ experiences with GenAI, the teacher perspective has received less attention. This study aims to explore how RE teachers perceive, evaluate and adapt their teaching in response to GenAI. To investigate this, semi-structured interviews and surveys were conducted with a selection of university teachers active in the field.

The study is grounded in the theoretical framework of Technology Affordances and Constraints (TAC). By identifying the affordances and constraints that GenAI introduces to educational practice, the study examines how the technology can enhance or hinder learning processes and teaching strategies.

Preliminary findings suggest that teachers see GenAI as a useful tool for quickly generating requirements examples, introducing variety in teaching activities, and sparking student interest. At the same time, concerns are raised regarding the reliability of these tools, the challenge of supporting students in formulating effective prompts, and the risk of overuse. In addition, some teachers express concerns that GenAI may reduce students' own creativity and critical thinking if used without sufficient pedagogical guidance. A recurring issue is also how to determine what is AI-generated and what is the student's own work, particularly in assessment contexts.

This study offers insights into how teachers are engaging with GenAI in practice and the considerations that arise in course design. The focus is especially on issues related to curriculum planning, the teacher's role, and assessment. In particular, the findings show that teachers are experimenting with new ways to incorporate GenAI meaningfully, yet remain vigilant about potential effects on student learning outcomes and academic integrity. By sharing these insights, the study aims to support other teachers and contribute to the continued development of pedagogical approaches in Requirements Engineering.

Keywords:

Generative AI, Requirements Engineering, Higher Education, Mixed methods, Affordances and Constraints.