Date
Publisher
arXiv
Context: Generative AI adoption in software engineering education raises opportunities for learning support alongside concerns about superficial learning and academic integrity.
Objective: This study investigates how explicit instructional guidance aligned with Bloom's taxonomy supports responsible GenAI use in SE/CS education, exploring student and instructor perceptions of GenAI-supported learning.
Method: We designed a Bloom-aligned GenAI framework that articulated appropriate GenAI roles at different cognitive levels. The framework was embedded in course instructions, labs, and assessment across multiple SE/CS courses at Queen's University Belfast and Azerbaijan Technical University. Data were collected via anonymous questionnaires and learning artifacts, analyzed using thematic analysis with Bloom's taxonomy as an analytic lens.
Results: Students perceived GenAI as most valuable for higher-order cognitive activities (analysis, evaluation, reflection) and less suitable for foundational learning. Explicit Bloom-level guidance influenced students to use GenAI reflectively, with delayed or intentional non-use when independent thinking was prioritized. Both students and instructors reported pedagogical benefits alongside challenges in cognitive effort and instructional design workload.
Conclusion: GenAI's educational value lies in intentional alignment between cognitive learning goals, instructional guidance, and learner self-regulation. Bloom's taxonomy provides a scalable, pedagogy-driven framework for responsible GenAI use in SE/CS education, offering a practical alternative to enforcement-focused responses.
Study at a glance
- Question: How can Bloom-aligned instructional guidance be embedded in computing education to guide AI-supported learning?
- Setting and data: Undergraduate computing courses at Queen’s University Belfast (42 students) and Azerbaijan Technical University (11 course instructors).
- Method: Qualitative practice-oriented study using open-ended questionnaires, learning artifacts, and thematic analysis.
Why it matters
Unstructured use of generative AI risks superficial learning and cognitive debt, while restrictive policies provide limited guidance. A pedagogy-driven framework is needed to align generative AI with learning objectives and preserve independent student thinking.
Key findings
- Students perceived generative AI as most valuable for higher-order cognitive tasks like analysis and evaluation, but viewed it as counterproductive for initial foundational concept acquisition.
- Explicit Bloom-level guidance shifted student habits toward delaying AI interaction until after independent problem solving, prompting for critique rather than answers, and practicing intentional non-use.
- Instructors and students reported improved pedagogical dialogue and reflective thinking, alongside challenges including increased instructional preparation workloads and initial student cognitive effort.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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