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REVIEW 3 major objections 2 minor 54 references

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

T0 review · 3 major / 2 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read Bloom's taxonomy aligns GenAI use with cognitive levels in software engineering and computer science education.

desk verdict The paper supplies a practical Bloom-aligned framework for GenAI in SE/CS courses at two universities, but the supporting data are thin self-reported perceptions without controls or objective measures. read the letter →

arxiv 2606.27398 v1 pith:RCVB4OQJ submitted 2026-06-24 cs.SE

Vahid Garousi , Zafar Jafarov , Aytan Mövsümova , Leyla Memmedova , Hüseyn Mirzayev This is my paper · ORCID
classification cs.SE
keywords GenAIBloom'staxonomysoftwareengineeringeducationcomputerscienceresponsibleusecognitivelevelsstudentperceptionsinstructionalguidance
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tests whether mapping generative AI roles to Bloom's taxonomy levels helps students and instructors use the tools responsibly in SE and CS courses. Explicit guidance led students to favor AI for analysis, evaluation, and reflection while sometimes skipping it for foundational work to preserve independent thinking. Data came from questionnaires and artifacts across courses at two universities, with thematic analysis showing both benefits and added design effort. A sympathetic reader would care because the approach offers a concrete way to integrate AI without defaulting to bans or unchecked adoption.

What carries the argument

Bloom-aligned GenAI framework that articulates appropriate GenAI roles at different cognitive levels.

What would settle it

A controlled comparison showing no increase in reflective or delayed GenAI use when Bloom-level guidance is added versus standard instructions.

Watch

Extended reading notes

Core claim

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.

Load-bearing premise

Thematic analysis of anonymous questionnaires and learning artifacts accurately reflects genuine changes in student behavior and perceptions rather than social-desirability bias or course-specific effects.

Editorial extensions

If this is right

  • Students perceive GenAI as most valuable for higher-order cognitive activities such as analysis, evaluation, and reflection.
  • Explicit Bloom-level guidance influences students to use GenAI reflectively, with delayed or intentional non-use when independent thinking is prioritized.
  • Both students and instructors report pedagogical benefits alongside challenges in cognitive effort and instructional design workload.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Other fields could map similar cognitive taxonomies to define AI assistance boundaries.
  • Longer-term tracking could check whether reflective habits continue after the guided courses end.
  • Replicating the framework in additional universities would test how much the two-site results depend on local context.
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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The paper describes the design and multi-course deployment of a Bloom's taxonomy-aligned framework for guiding GenAI use in SE/CS education at two universities. It reports thematic analysis of anonymous student/instructor questionnaires and learning artifacts, finding that students perceive GenAI as more valuable for higher-order tasks (analysis, evaluation, reflection), that explicit guidance promotes reflective use and selective non-use, and that both groups note pedagogical benefits alongside increased cognitive and design effort. The central claim is that Bloom's taxonomy supplies a scalable, pedagogy-first alternative to enforcement-focused approaches for responsible GenAI integration.

Significance. If the perception data can be shown to reflect genuine behavioral shifts rather than reporting bias, the work supplies a concrete, replicable instructional framework that links cognitive-level goals to GenAI roles. This is a practical contribution to the growing literature on AI in computing education and could inform curriculum design at other institutions.

major comments (3)
  1. [Methods] Methods: The description of data collection provides no sample sizes, response rates, number of courses or sections involved, number of learning artifacts examined, or inter-rater reliability statistics for the thematic analysis. These omissions make it impossible to evaluate the robustness or generalizability of the reported themes.
  2. [Results] Results/Discussion: Claims that the Bloom-aligned guidance produced changes in student behavior (e.g., "delayed or intentional non-use") rest entirely on post-intervention self-reports without pre-intervention baselines, control sections, or objective outcome measures. This leaves open the possibility that observed perceptions reflect social-desirability bias, instructor expectations, or course-specific factors rather than the framework itself.
  3. [Analysis] Analysis: The thematic analysis uses Bloom's taxonomy as an analytic lens, yet the manuscript does not describe how codes were mapped to taxonomy levels, whether coding was performed blind to the framework, or how disagreements between coders were resolved.
minor comments (2)
  1. [Abstract] The abstract and introduction would benefit from a brief statement of the number of courses and approximate participant numbers to give readers an immediate sense of scale.
  2. [Framework] Figure or table summarizing the Bloom-level GenAI roles would improve clarity and allow readers to assess the framework without reconstructing it from prose.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: [Methods] The description of data collection provides no sample sizes, response rates, number of courses or sections involved, number of learning artifacts examined, or inter-rater reliability statistics for the thematic analysis. These omissions make it impossible to evaluate the robustness or generalizability of the reported themes.

    Authors: We agree that these details are necessary to assess robustness. The revised manuscript will add the number of courses and sections at each institution, total enrolled students, questionnaire response rates, number of learning artifacts examined, and inter-rater reliability statistics (including Cohen's kappa) for the thematic analysis. revision: yes

  2. Referee: [Results] Claims that the Bloom-aligned guidance produced changes in student behavior (e.g., "delayed or intentional non-use") rest entirely on post-intervention self-reports without pre-intervention baselines, control sections, or objective outcome measures. This leaves open the possibility that observed perceptions reflect social-desirability bias, instructor expectations, or course-specific factors rather than the framework itself.

    Authors: We acknowledge the limitation. The study was exploratory and focused on post-implementation perceptions; no pre-intervention baselines or control sections were collected. The revision will add an explicit Limitations section discussing reliance on self-reports, potential social-desirability bias, and the absence of objective behavioral measures. Language will be adjusted to frame findings as student-reported perceptions supported by convergent evidence from artifacts, rather than demonstrated behavioral change. revision: partial

  3. Referee: [Analysis] The thematic analysis uses Bloom's taxonomy as an analytic lens, yet the manuscript does not describe how codes were mapped to taxonomy levels, whether coding was performed blind to the framework, or how disagreements between coders were resolved.

    Authors: We will expand the Methods section to describe the analysis process in detail. This will cover how codes were iteratively mapped to Bloom's levels, that coding was conducted with awareness of the framework (full blinding was not feasible due to the framework's embedding in course materials) while prioritizing data-driven coding, and that coder disagreements were resolved through discussion until consensus was reached. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Empirical perception study with no derivation chain or self-referential predictions

full rationale

The paper reports an empirical intervention study: a Bloom-aligned GenAI framework was designed, embedded in courses at two universities, and evaluated via anonymous questionnaires plus learning artifacts analyzed thematically. No equations, fitted parameters, predictions of new quantities, or mathematical derivations appear. The central claim rests on thematic interpretation of self-reported perceptions rather than any reduction to inputs by construction. Self-citations, if present, are not load-bearing for any uniqueness theorem or ansatz. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The central claim rests on Bloom's taxonomy as a valid and complete lens for cognitive activities in computing education (domain assumption) and on the reliability of self-reported perceptions from anonymous questionnaires (domain assumption). No free parameters or invented entities are introduced.

assumptions (2)
  • domain assumption Bloom's taxonomy accurately partitions cognitive processes relevant to SE/CS learning tasks.
    Invoked throughout the framework design and analysis sections of the abstract.
  • domain assumption Thematic analysis of student and instructor questionnaires can reliably identify effects of the embedded guidance.
    Basis for the results and conclusion statements.

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Cite this review

Pith. "Pith review of Implementing GenAI-Supported Learning in Software Engineering and Computer Science Education using Bloom's Taxonomy." pith.science (2026). https://pith.science/paper/RCVB4OQJ

@misc{pith2026260627398,
  author       = {Pith},
  title        = {Pith review of: Implementing GenAI-Supported Learning in Software Engineering and Computer Science Education using Bloom's Taxonomy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RCVB4OQJ}},
  note         = {Machine review of arXiv:2606.27398}
}
read the original 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.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 2 canonical work pages

  1. [1]

    B. S. Bloom, M. D. Engelhart, E. J. Furst, W. H. Hill, and D. R. Krathwohl, Taxonomy of educational objectives: The classification of educational goals. Handbook 1: Cognitive domain. Longman New York, 1956

    1956

  2. [2]

    A revision of Bloom's taxonomy: An overview,

    D. R. Krathwohl, "A revision of Bloom's taxonomy: An overview," Theory into practice, vol. 41, no. 4, pp. 212–218, 2002

    2002

  3. [3]

    Bloom’s Taxonomy Revisited – Artificial Intelligence Tools

    Oregon State University. "Bloom’s Taxonomy Revisited – Artificial Intelligence Tools." ecampus.oregonstate.edu/faculty/artificial- intelligence-tools/ blooms-taxonomy-revisited (accessed Jan. 2026)

    2026

  4. [4]

    Early adoption of GenAI in computing education: Emergent student use cases and perspectives,

    C. E. Smith et al., "Early adoption of GenAI in computing education: Emergent student use cases and perspectives," in Proceedings of the on Innovation and Technology in Computer Science Education, 2024, pp. 3–9

    2024

  5. [5]

    From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education,

    I. Reihanian, Y. Hou, and Q. Sun, "From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Education," AI, vol. 7, no. 1, p. 6, 2025

    2025

  6. [6]

    Generative AI in Computing Education: Perspectives of students and instructors,

    C. Zastudil, M. Rogalska, C. Kapp, J. Vaughn, and S. MacNeil, "Generative AI in Computing Education: Perspectives of students and instructors," in IEEE Frontiers in Education Conference, 2023, pp. 1–9

    2023

  7. [7]

    Generative AI in CS Education: Literature Review through a SWOT Lens,

    J. Roberts and A. Mohamed, "Generative AI in CS Education: Literature Review through a SWOT Lens," in Proceedings of the Western Canadian Conference on Computing Education, 2024, pp. 1–6

    2024

  8. [8]

    Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption,

    E. Dickey, A. Bejarano, and C. Garg, "Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption," arXiv preprint 2308.12258, 2023

    work page arXiv 2023

Show all 54 references
  1. [9]

    Risk management strategy for GenAI in computing education: how to handle the strengths, weaknesses, opportunities, and threats?,

    N. Humble, "Risk management strategy for GenAI in computing education: how to handle the strengths, weaknesses, opportunities, and threats?," International Journal of Educational Technology in Higher Education, vol. 21, 61, 2024

    2024

  2. [10]

    Software engineering education in the era of conversational AI: current trends and future directions,

    C. Sengul, R. Neykova, and G. Destefanis, "Software engineering education in the era of conversational AI: current trends and future directions," Frontiers in Artificial Intelligence, vol. 7:1436350, 2024

    2024

  3. [11]

    Software engineering education must adapt and evolve for an LLM environment,

    V. D. Kirova, C. S. Ku, J. R. Laracy, and T. J. Marlowe, "Software engineering education must adapt and evolve for an LLM environment," in Proceedings of the ACM Technical Symposium on Computer Science Education V. 1, 2024, pp. 666–672

    2024

  4. [12]

    Students' Perceptions and Use of GenAI Tools for Programming Across Different Computing Courses,

    H. Keuning et al., "Students' Perceptions and Use of GenAI Tools for Programming Across Different Computing Courses," in Proceedings of the Koli-Calling International Conference on Computing Education Research, 2024, pp. 1–12

    2024

  5. [13]

    Human-Al Collaboration in the STEM Classroom: A Systematic Literature Review of GenAI as a Complement in Higher Education,

    Y. Jia, C. Li, and A. Chan, "Human-Al Collaboration in the STEM Classroom: A Systematic Literature Review of GenAI as a Complement in Higher Education," in International Conference on Technology in Education, 2025, pp. 389–401

    2025

  6. [14]

    Exploring GenAI as a Tutoring Tool: A Case Study in First-Year Computer Programming,

    L. Stienstra, A. Mohamed, and M. Mohamed, "Exploring GenAI as a Tutoring Tool: A Case Study in First-Year Computer Programming," in Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1, 2025, pp. 452– 457

    2025

  7. [15]

    Generative AI as a Learning Buddy and Teaching Assistant: Pre-service Teachers' Uses and Attitudes,

    M. Nyaaba et al., "Generative AI as a Learning Buddy and Teaching Assistant: Pre-service Teachers' Uses and Attitudes," arXiv preprint arXiv:2407.11983, 2024

    2024

  8. [16]

    Leveraging Generative AI for On-Demand Tutoring as a New Paradigm in Education,

    M. Leon, "Leveraging Generative AI for On-Demand Tutoring as a New Paradigm in Education," International Journal on Cybernetics & Informatics, vol. 13, no. 5, pp. 17–29, 2024

    2024

  9. [17]

    Promoting student engagement with GPTutor: An intelligent tutoring system powered by generative AI,

    H. Bai, W. C. Lui, and P. V. Khiatani, "Promoting student engagement with GPTutor: An intelligent tutoring system powered by generative AI," International Journal of Educational Technology in Higher Education, vol. 22, no. 1, p. 77, 2025

    2025

  10. [18]

    The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment,

    M. Perkins, L. Furze, J. Roe, and J. MacVaugh, "The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment," Journal of University Teaching and Learning Practice, vol. 21, no. 6, pp. 49–66, 2024

    2024

  11. [19]

    AI maturity toolkit for tertiary education,

    UK's Joint Information Systems Committee (JISC), "AI maturity toolkit for tertiary education," in www.jisc.ac.uk/ai-maturity-toolkit-for- tertiary-education, Last accessed: Jan. 2026

    2026

  12. [20]

    Understanding AIgiarism in higher education: the lens of general AI attitudes and moral disengagement,

    M. Waqas, A. Hania, and X. U. Chunyan, "Understanding AIgiarism in higher education: the lens of general AI attitudes and moral disengagement," Studies in Higher Education, pp. 1–17, 2025

    2025

  13. [21]

    Students’ perceptions of ‘AI-giarism’: investigating changes in understandings of academic misconduct,

    C. K. Y. Chan, "Students’ perceptions of ‘AI-giarism’: investigating changes in understandings of academic misconduct," Education and Information Technologies, vol. 30, no. 6, pp. 8087–8108, 2025

    2025

  14. [22]

    Bandura and R

    A. Bandura and R. H. Walters, Social learning theory. Prentice hall Englewood Cliffs, NJ, 1977

    1977

  15. [23]

    Artificial intelligence in education: The three paradigms,

    F. Ouyang and P. Jiao, "Artificial intelligence in education: The three paradigms," Computers and Education: Artificial Intelligence, vol. 2, p. 100020, 2021

    2021

  16. [24]

    Learning theories for artificial intelligence promoting learning processes,

    D. Gibson, V. Kovanovic, D. Ifenthaler, S. Dexter, and S. Feng, "Learning theories for artificial intelligence promoting learning processes," British Journal of Educational Technology, vol. 54, no. 5, pp. 1125–1146, 2023

    2023

  17. [25]

    Habit Predicting Higher Education EFL Students’ Intention and Use of AI: A Nexus of UTAUT-2 Model and Metacognition Theory,

    S. Rashid, "Habit Predicting Higher Education EFL Students’ Intention and Use of AI: A Nexus of UTAUT-2 Model and Metacognition Theory," Education Sciences, vol. 15, no. 6, p. 756, 2025

    2025

  18. [26]

    Encouraging Responsible GenAI Use in Software Engineering Education: A Design-Oriented Model,

    V. Garousi, Z. Jafarov, A. Movsumova, A. Namazov, and H. Mirzayev, "Encouraging Responsible GenAI Use in Software Engineering Education: A Design-Oriented Model," In Press, Journal of Systems and Software, 2026

    2026

  19. [27]

    Repository of Software Testing Laboratory Courseware

    V. Garousi. "Repository of Software Testing Laboratory Courseware." https://bit.ly/garousi-openware-software-testing-labs (accessed June 2025

    2025

  20. [28]

    An Open Modern Software Testing Laboratory Courseware: An Experience Report

    V. Garousi, "An Open Modern Software Testing Laboratory Courseware: An Experience Report " in Proceedings of the IEEE Conference on Software Engineering Education and Training, 2010, pp. 177–184

    2010

  21. [29]

    How Productive Is the Productive Struggle? Lessons Learned from a Scoping Review,

    J. R. Young, D. Bevan, and M. Sanders, "How Productive Is the Productive Struggle? Lessons Learned from a Scoping Review," International Journal of Education in Mathematics, Science and Technology, vol. 12, no. 2, pp. 470–495, 2024. 40

    2024

  22. [30]

    Wohlin, P

    C. Wohlin, P. Runeson, M. Höst, M. C. Ohlsson, B. Regnell, and A. Wesslén, Experimentation in Software Engineering: An Introduction. Kluwer Academic Publishers, 2000. APPENDIX A: DESIGN OF THE ANONYMOUS QUESTIONNAIRES A.1 – Bloom-Level Learning Support Questionnaire (Students)...

    2000

  23. [31]

    In what ways, if any, did GenAI support your learning at the Remember level (e.g., recalling concepts or terminology)? Free-text

  24. [32]

    In what ways, if any, did GenAI support your learning at the Understand level (e.g., explaining concepts in your own words)? Free-text

  25. [33]

    In what ways, if any, did GenAI support your learning at the Apply level (e.g., applying known methods or techniques)? Free-text

  26. [34]

    In what ways, if any, did GenAI support your learning at the Analyze level (e.g., identifying weaknesses, assumptions, or alternatives)? Free-text

  27. [35]

    In what ways, if any, did GenAI support your learning at the Evaluate level (e.g., comparing solutions or judging quality)? Free-text

  28. [36]

    In what ways, if any, did GenAI support your learning at the Create level (e.g., generating ideas based on your own inputs)? Free-text

  29. [37]

    Free-text A.2 – GenAI Usage Practices Questionnaire (Students): Addressed RQ2 Purpose: To understand how explicit Bloom-level guidance influenced students’ GenAI usage practices

    Please briefly describe how GenAI supported (or did not support) your learning across these cognitive levels. Free-text A.2 – GenAI Usage Practices Questionnaire (Students): Addressed RQ2 Purpose: To understand how explicit Bloom-level guidance influenced students’ GenAI usage...

  30. [38]

    Free-text

    Describe when you typically used GenAI during learning activities (e.g., before, during, or after working on a task). Free-text

  31. [39]

    How did Bloom-level guidance influence when you decided to use GenAI? Free-text

  32. [40]

    How did Bloom-level guidance influence how you interacted with GenAI? Free-text

  33. [41]

    Free-text

    Describe a situation where Bloom-level guidance changed the way you would normally have used GenAI. Free-text

  34. [42]

    Question Response Type 41

    Were there situations where you intentionally avoided using GenAI? If so, why? Free-text A.3 – Perceived Benefits and Drawbacks Questionnaire (Students): Addressed RQ3 – student perspective Purpose: To elicit students’ perceived benefits and limitations of the Bloom-aligned Ge...

  35. [43]

    What aspects of the Bloom-aligned GenAI guidance did you find most beneficial for your learning? Free-text

  36. [44]

    What challenges or limitations did you experience when using GenAI under this approach? Free-text

  37. [45]

    Free-text

    Did this approach change how you think about using GenAI for learning? Please explain. Free-text

  38. [46]

    Question Response Type

    Would you like this type of GenAI guidance to be used in other courses? Why or why not? Free-text A.4 – Instructor Reflection Questionnaire (Instructors): Addressed RQ3 – instructor perspective Purpose: To capture instructors’ reflections on pedagogical value, feasibility, and...

  39. [47]

    How did explicit Bloom-level GenAI guidance influence students’ learning practices and engagement? Free-text

  40. [48]

    What pedagogical benefits of this approach did you observe? Free-text

  41. [49]

    What challenges or drawbacks did you encounter when implementing this approach? Free-text

  42. [50]

    How did this approach affect your own teaching practices or assessment design? Free-text

  43. [51]

    Free-text A.5 – Student Background and GenAI Familiarity Questionnaire Purpose: To contextualize student responses by capturing prior GenAI experience

    Would you adopt or adapt this approach in future courses? Please explain. Free-text A.5 – Student Background and GenAI Familiarity Questionnaire Purpose: To contextualize student responses by capturing prior GenAI experience. Question Response Type

  44. [52]

    Before this course, how had you used GenAI tools for learning, if at all? Free-text

  45. [53]

    Free-text

    How confident did you feel using GenAI tools at the start of the course? Please explain. Free-text

  46. [54]

    Free-text

    Had you previously received any guidance on responsible or structured GenAI use? If so, describe it briefly. Free-text

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Reviewed June 29, 2026 · model on record in the stance chip above.