Implementing GenAI-Supported Learning in Software Engineering and Computer Science Education using Bloom's Taxonomy

• URL: http://arxiv.org/abs/2606.27398v1
• Date: Wed, 24 Jun 2026 10:44:07 GMT
• Title: Implementing GenAI-Supported Learning in Software Engineering and Computer Science Education using Bloom's Taxonomy
• Abstract summary: Generative AI adoption in software engineering education raises opportunities for learning support alongside concerns about superficial learning and academic integrity.<n>This study investigates how explicit instructional guidance aligned with Bloom's taxonomy supports responsible GenAI use in SE/CS education.
• Score: 0.4306143768014156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Is Solving Better Than Evaluating GenAI Solutions? [45.714140455691485]
  This study compares evaluating GenAI-generated solutions with traditional problem solving in a junior-level algorithms course.<n>We found no statistically significant differences between groups in midterm scores, final exam scores, overall course grades, or exam problems structurally aligned with the homework interventions.<n>Students received significantly higher homework scores when evaluating GenAI-generated solutions, but this localized advantage did not translate into downstream gains.
  arXiv  Detail & Related papers  (2026-07-30T02:08:29Z)
• Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course [0.0]
  This research paper examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming (OOP) courses.<n>Survey data were collected from 210 undergraduate students enrolled in a first-year OOP course.
  arXiv  Detail & Related papers  (2026-05-26T07:48:00Z)
• Exploring Student Perception on Gen AI Adoption in Higher Education: A Descriptive Study [1.978884131103313]
  This study examines how students perceive, use, and evaluate Generative Artificial Intelligence (GenAI)<n>Data were collected through a questionnaire administered to 436 postgraduate Computer Science students at the University of Hertfordshire.
  arXiv  Detail & Related papers  (2026-03-29T17:19:39Z)
• On the Role and Impact of GenAI Tools in Software Engineering Education [2.867517731896504]
  generative AI (GenAI) tools like ChatGPT and GitHub Copilot have transformed how software is learned and written.<n>In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning.<n>This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences.
  arXiv  Detail & Related papers  (2025-12-03T20:51:16Z)
• Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications [0.4306143768014156]
  As generative AI (GenAI) tools become pervasive in education, concerns are rising about students using them to complete rather than learn.<n>This paper proposes and empirically applies a causal model to help educators scaffold responsible GenAI use in Software Engineering (SE) education.
  arXiv  Detail & Related papers  (2025-05-31T19:27:40Z)
• From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models [44.99833362998488]
  We investigated the first steps for optimizing content creation for advanced math.<n>We looked at the ability of GenAI to produce high-quality practice problems that are relevant to the course content.
  arXiv  Detail & Related papers  (2025-05-17T08:30:10Z)
• Evaluating the AI-Lab Intervention: Impact on Student Perception and Use of Generative AI in Early Undergraduate Computer Science Courses [0.0]
  Generative AI (GenAI) is rapidly entering computer science education.<n>Concerns about overreliance coexist with a gap in research on structured scaffolding to guide tool use in formal courses.<n>This study examines the impact of a dedicated "AI-Lab" intervention on undergraduate students.
  arXiv  Detail & Related papers  (2025-04-30T18:12:42Z)
• Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023 [38.83649319653387]
  There is limited prior research on computing students' use and perceptions of GenAI. We surveyed all computer science majors in a small engineering-focused R1 university. We discuss the impact of our findings on the emerging conversation around GenAI and education.
  arXiv  Detail & Related papers  (2024-11-17T20:17:47Z)
• Model-based Maintenance and Evolution with GenAI: A Look into the Future [47.93555901495955]
  We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of Model-Based Engineering (MBM&E) We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems.
  arXiv  Detail & Related papers  (2024-07-09T23:13:26Z)
• Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students [35.05435052195561]
  The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes. This paper introduces a co-design study to explore the potential of students' AI usage data as a novel material for assessment.
  arXiv  Detail & Related papers  (2024-01-26T14:49:29Z)
• Toward enriched Cognitive Learning with XAI [44.99833362998488]
  We introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by artificial intelligence (AI) tools. The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle problems to enhance problem-solving skills.
  arXiv  Detail & Related papers  (2023-12-19T16:13:47Z)
• Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption [0.0]
  We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses. By identifying and rectifying GenAI's errors, students enrich their learning process. For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
  arXiv  Detail & Related papers  (2023-08-23T17:20:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.