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arxiv: 2606.27398 · v1 · pith:RCVB4OQJnew · submitted 2026-06-24 · 💻 cs.SE

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

Vahid Garousi , Zafar Jafarov , Aytan M\"ovs\"umova , Leyla Memmedova , H\"useyn Mirzayev This is my paper

Pith reviewed 2026-06-29 01:50 UTC · model grok-4.3

classification 💻 cs.SE
keywords GenAIBloom's taxonomysoftware engineering educationcomputer science educationresponsible AI usecognitive levelsstudent perceptionsinstructional guidance
0
0 comments X

The pith

Bloom's taxonomy aligns GenAI use with cognitive levels in software engineering and computer science education.

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

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.

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.

What carries the argument

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

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.

Where Pith is reading between the lines

These are 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.

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.

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.

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

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

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.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 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.

axioms (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.

pith-pipeline@v0.9.1-grok · 5789 in / 1419 out tokens · 31346 ms · 2026-06-29T01:50:43.795380+00:00 · methodology

discussion (0)

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Reference graph

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54 extracted references · 2 canonical work pages

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

  32. [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

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

  34. [34]

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

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

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    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

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    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 practices. Question Response Type

  38. [38]

    Free-text

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

  39. [39]

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

  40. [40]

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

  41. [41]

    Free-text

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

  42. [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 GenAI approach. Question Response Type 41

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

  44. [44]

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

  45. [45]

    Free-text

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

  46. [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 limitations. Question Response Type

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

  48. [48]

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

  49. [49]

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

  50. [50]

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

  51. [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

  52. [52]

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

  53. [53]

    Free-text

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

  54. [54]

    Free-text

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