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REVIEW 2 major objections 4 minor 56 references

Using Generative AI in Software Design Education: An Experience Report

T0 review · 2 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This experience report argues that requiring undergraduate software-design students to use ChatGPT on a team assignment helps them both design and learn the tool's limits.

desk verdict A useful, honest experience report on requiring ChatGPT in a team design assignment; its learning-claim rests on graded reflections and needs a caveat, but it deserves peer review. read the letter →

arxiv 2506.21703 v1 pith:OX5P3OVR submitted 2025-06-26 cs.SE

Victoria Jackson , Susannah Liu , Andre van der Hoek This is my paper · ORCID
classification cs.SE
keywords softwaredesigneducationgenerativeAIChatGPTUMLclassdiagramsengineeringpromptstudentreflectionstudio
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

This experience report argues that requiring undergraduate software-design students to use ChatGPT on a team assignment helps them both design and learn the tool's limits. Based on 179 students in 36 teams, with 360 required conversations and 30 written reflections, the paper reports that students used ChatGPT for ideation, validation, knowledge seeking, and checking consistency, while also finding its outputs unreliable, overcomplicated, or superficial. Students consistently said that human designers must lead the process and critically review every AI response before it enters the design. The authors conclude that GenAI belongs in software design education when introduced after foundational design practices and paired with structured reflection.

What carries the argument

The central machinery is the assignment design: students were required to have at least ten different ChatGPT conversations, each with a distinct purpose, and to submit both the conversation logs and a written reflection. This was paired with expert design practices taught earlier in the course—focusing on the essence, generating alternatives, addressing knowledge deficiencies, and solving simpler problems first—which the paper argues anchored students' use of GenAI and gave them criteria for critiquing its responses. The conversation logs provided behavioral evidence of prompt strategies and usage patterns, while the reflections provided evidence of perceived benefits and limitations.

What would settle it

Repeat the assignment with a course-provided chatbot that automatically records every conversation, then compare the auto-recorded logs with what students submit; any divergence would show the submitted logs are not reliable records, and the observed prompt strategies could be artifacts of curation rather than real behavior.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that students can benefit from using GenAI in software design education: all 36 teams completed the required ChatGPT conversations, and their logs and reflections show it helped generate ideas, provide starting points, validate designs, and speed up the process, while students recognized that its responses required critique and amendment. The paper's strongest claim, stated in the conclusion, is that 'students can benefit from using GenAI in software design education as it helps them design and learn about the strengths and weaknesses of GenAI.' Students overwhelmingly concluded that design remains a human-led activity because ChatGPT lacks contextual understanding, sometimes introduces unnecessary complexity, and cannot be trusted to produce a complete design alone.

Load-bearing premise

The paper's observations of how students used ChatGPT rest on students submitting accurate, complete conversation logs, which the teaching team could not verify because students used their own personal accounts; if teams edited, curated, or omitted parts of their conversations, the reported prompt strategies and usage patterns would not describe what actually happened.

Editorial extensions

If this is right

  • The course provides a repeatable template: teach foundational design practices first, then require a structured GenAI assignment with a reflection, and students will tend to treat ChatGPT as a starting point rather than a final answer.
  • Requiring ten distinct conversations pushes teams to explore multiple purposes, and the observed strategies show GenAI can assist with stakeholders, UML class models, and pseudocode in one design problem.
  • Students' insistence that humans must lead the design process suggests design courses can adopt GenAI without abandoning the human-centered core of design education.
  • The drop in GenAI use when it became optional implies that explicit requirements and grading incentives shape adoption more than perceived benefit alone.
  • The paper's lesson that expert practices helped students critique GenAI responses suggests teaching those practices is a prerequisite for meaningful GenAI use in design.

Reading between the lines

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

  • If the submitted logs accurately reflect behavior, the most sophisticated teams used ChatGPT not just as a generator but as a critic and simulator; a follow-up study could measure whether prompting for self-reflection or execution improves design quality.
  • A course-provided chatbot that automatically records conversations would remove the data-reliability problem and could nudge students to apply expert practices such as generating alternatives or addressing knowledge gaps.
  • The paper's own framing suggests a controlled comparison between a section with mandatory structured GenAI use and one with free optional use to separate the effect of the tool from the effect of the assignment structure.
  • The absence of any student reflection on ethics, copyright, or sustainability is itself a signal for educators: if those topics matter, reflection prompts must explicitly invite them.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. This experience report describes the integration of a mandatory ChatGPT assignment into an undergraduate software design course at the University of California, Irvine. In Fall 2023, 179 students in 36 teams were required to use ChatGPT, with each team asked to conduct ten conversations of different purposes and to submit the logs along with a written reflection on their experience. The authors analyzed the 36 logs and 30 reflections using thematic analysis and cross-referencing with design artifacts, and they also assessed whether the expert design practices taught in the course were visible in students' GenAI use. The paper's central claim, stated in the conclusion, is that based on this experience, students can benefit from using GenAI in software design education because it helps them design and learn about the strengths and weaknesses of GenAI. The paper reports that students used ChatGPT for ideation, knowledge seeking, validation, and even as a team member, while also recognizing the need to critique and correct its output, and it concludes with six lessons for educators.

Significance. If the result holds, this paper makes a useful contribution to a sparse literature on teaching software design with generative AI. Its strengths include a transparent description of the data collection (all 36 teams submitted logs, 30 of 36 submitted reflections), a detailed account of the assignment design and training, and appropriately hedged conclusions that are framed as experiential rather than causal. The paper also provides practical, actionable lessons for educators, such as the value of teaching design practices before introducing GenAI and the importance of reflection. However, the central claim about student learning rests on self-reported reflections that were a graded course requirement, and the paper does not provide the reflection prompt or an explicit treatment of demand characteristics. The result is still valuable as an experience report, but the inference that students 'learned about the strengths and weaknesses of GenAI' needs stronger evidentiary support or a more cautious framing.

major comments (2)
  1. [Sections 3.3, 3.4, and 4.4] The central claim that students benefited and learned about GenAI's strengths and weaknesses is inferred primarily from the reflections, which were a mandatory, graded submission written after a lecture that explicitly taught that GenAI output requires critique and that design is human-led (Section 3.4, Figure 1). The paper does not report the exact reflection prompt or grading rubric, so it is impossible to assess whether students were prompted to reproduce the taught narrative. This is load-bearing because the benefit/learning conclusion would collapse if the reflections merely echo the instructor's framing. Please include the reflection prompt and rubric in an appendix, and add a limitations paragraph discussing demand characteristics and the possibility that reflections were tailored to the grading criteria. The presence of some nonconforming reflections (e.g., T146's initial over-trust, T151's initial struggle) provides partial evidence against uniform echo, but the analysis would be stronger if the authors explicitly addressed this issue.
  2. [Section 3.5] The thematic analysis was conducted by one researcher, with no reported second coder, inter-rater reliability check, or member checking. While this is acceptable for an experience report, the paper should either provide more detail on the verification steps or soften the language from 'students learned' to 'students reported learning.' As written, the conclusion in Section 6 states that the approach 'has helped students learn about the strengths and weaknesses of GenAI,' which overstates the evidentiary weight of a single-researcher thematic analysis of self-reports.
minor comments (4)
  1. [Section 3.3] The text 'Marks were given for using GenAI only in Design Studio - Part 1' appears to be a typo for 'Design Studio 2 - Part 1'; please correct it for consistency with the rest of the paper.
  2. [Table 1 and Section 3.5] The expert practices listed in Table 1 come from a book co-authored by one of the authors (reference [47]), and the 'Observed' column reflects the authors' judgment. This creates a mild self-referential risk: the lens used to interpret student behavior is the authors' own framework. Please add a brief note acknowledging this positionality and the possibility of confirmation bias in the analysis.
  3. [Section 3.6] The limitation about log fidelity is clearly acknowledged, but the paper could further qualify the usage statistics (e.g., the statement in Section 5.1 that only 30% of conversations had four or more prompts) by noting that the dataset may be incomplete if students curated or edited their logs before submission.
  4. [Section 5.1] In Lesson Learned #5, the sentence 'It needs to be clarified if the use did indeed drop off' is awkward and grammatically incomplete; consider rephrasing to something like 'It is unclear whether the drop in submitted logs reflects a true decrease in GenAI usage or simply a lack of incentive to submit logs when optional.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benefit-and-learning claim is an empirical interpretation of student reflections and logs, not a quantity defined in terms of its inputs.

full rationale

This paper is an experience report with no equations, fitted parameters, or formal derivation chain, so the central circularity patterns (self-definitional claims, fitted inputs renamed as predictions, uniqueness theorems, ansatz-smuggling) do not apply. The strongest claim, that students can benefit from GenAI in software design education and learn about its strengths and weaknesses, is supported by student reflections, conversation logs, and qualitative analysis. Those data are inputs to the claim, not the claim itself: students were asked to use GenAI and reflect on it, and the paper reports their perceptions and observed prompting behavior. A validity threat exists because the reflections were course-credit-bearing and were written after a lecture that taught the value of human critique, but that is a question of evidence quality and demand characteristics, not of circularity. The paper's own limitations section (Section 3.6) acknowledges reliance on student-provided logs, and the discussion repeatedly notes that benefits were perceived, not measured. Self-citations appear (e.g., the expert-practices book [47] co-authored by van der Hoek, and the earlier course-design paper [5]), but they are not used to force the central conclusion. The expert-practices framework is presented as an interpretive lens for Table 1 and Lesson Learned #3, while the benefit-and-learning claim is independently grounded in the students' written reflections and the authors' observations of the logs. The paper does not rename a known result or invoke a self-citation chain to forbid alternatives. Therefore, no circular step can be exhibited, and the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The report has no free parameters or invented entities. The load-bearing inputs are unverified student self-reports (logs and reflections) and the borrowed expert-practices framework used for Table 1; neither is independently validated, so the ledger is short but the data quality is the main risk.

assumptions (3)
  • domain assumption Submitted ChatGPT conversation logs accurately reflect the actual ChatGPT conversations each team had.
    The authors note students used personal ChatGPT accounts inaccessible to the teaching team, so the study relies on student-provided records; Section 3.6.
  • domain assumption The expert design practices from [47] (Table 1) are a valid framework for categorizing and interpreting student design behavior.
    The authors use these practices as the lens for Table 1 observations and Lesson #3; one co-author is a co-author of the cited book, so the framework is not independently validated in this paper.
  • domain assumption The written reflections from 30 of 36 teams are representative enough to support claims about student experience.
    Six teams did not submit reflections; the paper does not compare responders to non-responders; Section 3.5.

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Cite this review

Pith. "Pith review of Using Generative AI in Software Design Education: An Experience Report." pith.science (2026). https://pith.science/paper/OX5P3OVR

@misc{pith2026250621703,
  author       = {Pith},
  title        = {Pith review of: Using Generative AI in Software Design Education: An Experience Report},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OX5P3OVR}},
  note         = {Machine review of arXiv:2506.21703}
}
read the original abstract

With the rapid adoption of Generative AI (GenAI) tools, software engineering educators have grappled with how best to incorporate them into the classroom. While some research discusses the use of GenAI in the context of learning to code, there is little research that explores the use of GenAI in the classroom for other areas of software development. This paper provides an experience report on introducing GenAI into an undergraduate software design class. Students were required to use GenAI (in the form of ChatGPT) to help complete a team-based assignment. The data collected consisted of the ChatGPT conversation logs and students' reflections on using ChatGPT for the assignment. Subsequently, qualitative analysis was undertaken on the data. Students identified numerous ways ChatGPT helped them in their design process while recognizing the need to critique the response before incorporating it into their design. At the same time, we identified several key lessons for educators in how to deploy GenAI in a software design class effectively. Based on our experience, we believe students can benefit from using GenAI in software design education as it helps them design and learn about the strengths and weaknesses of GenAI.

Figures

Figures reproduced from arXiv: 2506.21703 by the authors.

Figure 1
Figure 1. Guidance for generating a design artifact in GenAI [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

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    User Experience Design Professionals’ Perceptions of Generative Artificial Intelligence. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (New York, NY, USA, 2024-05-11) (CHI ’24). Association for Computing Machinery, 1–18. https://doi.org/10.114...

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.