Bloom's Taxonomy Framework Guides Responsible GenAI Use in CS Education

A new study published on arXiv (arXiv:2606.27398) by researchers Vahid Garousi, Zafar Jafarov, Aytan Mövsümova, Leyla Memmedova, and Hüseyn Mirzayev investigates how explicit instructional guidance aligned with Bloom's taxonomy can support responsible use of generative AI (GenAI) in software engineering and computer science education. The team, from Queen's University Belfast and Azerbaijan Technical University, developed a Bloom-aligned GenAI framework that specifies appropriate roles for AI at different cognitive levels, from remembering to evaluating. This framework was embedded into course instructions, labs, and assessments across multiple SE/CS courses. Data was collected through anonymous questionnaires and learning artifacts, then analyzed using thematic analysis with Bloom's taxonomy as an analytic lens.

The results show clear patterns: students perceived GenAI as most valuable for higher-order cognitive activities such as analysis, evaluation, and reflection, while finding it less suitable for foundational learning tasks. When given explicit Bloom-level guidance, students used GenAI more reflectively and even intentionally chose not to use it when independent thinking was prioritized—a key insight for academic integrity. Both students and instructors reported pedagogical benefits from the structured approach, but also noted challenges in increased cognitive effort and instructional design workload. The study concludes that GenAI's educational value hinges on intentional alignment between cognitive learning goals, instructional guidance, and learner self-regulation. Bloom's taxonomy offers a scalable, pedagogy-driven alternative to enforcement-focused responses, providing a practical framework for responsible GenAI use in SE/CS education.