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REVIEW 2 major objections 5 minor 40 references

Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023

T0 review · 2 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read By the end of Spring 2023, most computer science students at a U.S.

desk verdict An honest early snapshot of GenAI use in one CS department, but the abstract overstates the evidence from a 12% opt-in sample. read the letter →

arxiv 2411.11166 v1 pith:26JXFE6S submitted 2024-11-17 cs.CY

C. Estelle Smith , Kylee Shiekh , Hayden Cooreman , Sharfi Rahman , Yifei Zhu , Md Kamrul Siam , Michael Ivanitskiy , Ahmed M. Ahmed
show 3 more authors
Michael Hallinan Alexander Grisak Gabe Fierro
This is my paper · ORCID
classification cs.CY
keywords generativeartificialintelligencelargelanguagemodelcodegeneratorimagepolicysurveystudentexperienceAIliteracy
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 paper tries to establish an early empirical baseline: by the end of the Spring 2023 semester, most computer science students at one U.S. university had already tried generative AI tools, mainly large language model chatbots, and used them for writing support, coding help, and self-directed learning. The authors argue that this adoption happened before any institution-wide policy existed, with most classes offering little or no guidance. On the whole, students rated generative AI as more beneficial than harmful to the field of computing, though they were split on whether instruction should encourage, conditionally allow, or discourage use. The paper positions these self-reported use cases and opinions as input for future policy and tool design in computing education.

What carries the argument

The central machinery is a survey instrument followed by directed content analysis, a qualitative method that applies a codebook to free-text responses. The survey asked students to rate their frequency of use of three GenAI categories (LLM chatbots, code generators, image generators), rate on a 1–10 scale how beneficial GenAI will be to computer science, and answer three free-response questions about their use, the appropriate role in education, and workplace concerns. Six human coders iteratively refined codebooks until inter-rater reliability (Krippendorff's alpha) exceeded 0.6, producing a taxonomy of emergent use cases—writing, coding, and learning—and of student opinions on degree of use, rationale, and implementation methods. That taxonomy is what lets the paper move from anecdote to a structured description of how students adopted the tools.

What would settle it

A comparison of self-reported GenAI use against actual usage telemetry (e.g., IDE plugin logs or browser history from a consenting subsample) would settle whether frequency estimates are inflated; if reported 'regular' use substantially exceeds logged use, the adoption baseline is overstated. Alternatively, replicating the survey at several institutions with 40%+ response rates and finding adoption below 30% would undermine the claim that most computing students had adopted GenAI by spring 2023.

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Extended reading notes

Core claim

Surveying all computer science majors at a small engineering-focused R1 university (116 undergraduates and 17 graduate students, 12% and 7.6% of their respective populations), the paper finds that most respondents had tried a large language model chatbot, that fewer had tried code generators, and that almost none had fully turned in AI-generated assignments. Instead, students described using GenAI to draft and debug code, explain concepts, find sources, outline and polish writing, and act as an informal tutor when instructors were unavailable. Asked about the role of GenAI in education, 69 of 126 respondents called for conditional use with instructor-specified boundaries, 41 wanted it encouraged, and 16 wanted it discouraged; students were nearly split on whether it helps or harms learning (44 vs. 40 coded responses), while many viewed AI skills as necessary for future employment. The authors claim these results capture a genuine pre-policy snapshot of early adoption and student attitudes that can inform curricula, policy, and tooling.

Load-bearing premise

The findings rest on students accurately reporting how often they used tools that could look like cheating, from a department where only 12% of undergraduates and 7.6% of graduate students responded.

Editorial extensions

If this is right

  • If most students are already using LLMs before policy exists, then policies written in 2023–2024 were reacting to established behavior rather than preventing it.
  • Because students mostly used GenAI for supported tasks (explaining, debugging, outlining, tutoring) rather than full assignment completion, instructors can target restrictions at specific use cases such as 'no drafting code' without banning the tool outright.
  • The split between students who want conditional use and those who want encouragement suggests that a one-size-fits-all ban would conflict with many students' expressed preferences and career expectations.
  • Students' perception that AI literacy is needed for future jobs implies curricula should include professional AI use, not just academic integrity rules.
  • The finding that students who use LLMs more frequently rate GenAI as more beneficial (p < .0008) suggests familiarity goes with acceptance, so early exposure may shape later attitudes.

Reading between the lines

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

  • An implication the paper leaves implicit: if early adoption was already this common in spring 2023, later cohorts who have never known a pre-ChatGPT classroom will likely arrive with even higher baseline familiarity, making 'teach responsible use' the more realistic institutional stance.
  • The paper's taxonomy suggests a testable design principle: restrictions keyed to course level (e.g., blocking code drafting in CS1 but allowing it in capstone courses) may preserve learning outcomes better than bans on the tool type itself, a hypothesis an experimental study could check.
  • Interpreting students' 'calculator for coding' framing, assessment may need to shift from judging the produced code to judging the process of verification and design, for instance through oral exams or submitted explanations of AI-assisted changes.
  • Replicating this survey at a large public university or a teaching-focused college would show whether the 12% undergraduate response rate at a single engineering-focused institution yields representative adoption estimates.
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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 / 5 minor

Summary. Smith et al. report a Spring 2023 survey of computer science majors at a single small R1 U.S. university on their use and perceptions of generative AI tools. The study draws on 133 responses, presents frequencies of LLM, code-generator, and image-generator use, applies directed content analysis to open-text responses about writing, coding, and learning, and reports associations between usage frequency and benefit ratings. The central claims are that most students had tried GenAI tools for a variety of writing, coding, and learning use cases, and that students tend to view GenAI as beneficial to computing.

Significance. If limited to the respondent sample, this paper offers a timely and useful qualitative baseline of early GenAI adoption in computing education, with concrete student quotes and a clear coding methodology. Notable strengths are the iterative inter-rater reliability process, the public sharing of codebooks, and the honest acknowledgment of the non-representative sample. The paper's main value is as an exploratory snapshot that can inform later policy-oriented and larger-scale studies, but the population-level claims in the abstract currently outrun the evidence presented.

major comments (2)
  1. [Abstract; Section 3.4] The abstract claims that 'most students have tried GenAI tools' and that 'students tend to view GenAI tools as beneficial,' but Section 3.4 reports that the sample comprises 12% of undergraduates and 7.6% of graduate students at one institution and explicitly acknowledges the sample is not representative. Because the survey topic is GenAI itself, non-response is likely correlated with the outcome: students who have used GenAI or formed opinions may be more likely to respond. Without a non-response analysis (for example, a late-responder comparison, weighting, or benchmarking against institutional data), the headline claims cannot be distinguished from 'most survey respondents.' Please either narrow the abstract and conclusion claims to 'respondents' or supply such an analysis.
  2. [Section 4.1; Abstract] The 'variety of writing, coding, and learning use cases' in the abstract is grounded in the 75 of 133 respondents (56.4%) who answered the optional free-response question about their use of GenAI. The remaining 43.6% of the sample did not provide this information, and the abstract does not disclose this. Furthermore, these 75 respondents are self-selected, so their use-case descriptions may overrepresent students with more extensive or more opinionated GenAI experience. Please add this qualification to the abstract and RQ1 summary, and consider reporting a sensitivity count that treats non-responders to the optional question as missing.
minor comments (5)
  1. [Header; ACM Reference Format] The ACM Reference Format line lists the year as '2014' instead of '2024'.
  2. [Section 4.1] The sentence 'only 36.1% have ever reported trying an image generator' is not supported by the disaggregated counts in the text; please report the image-generator frequency counts shown in Table 1 to make the calculation auditable.
  3. [Section 3.3] The codebook link uses a URL shortener (bit.ly/SIGCSE-GenAI-codebooks); please provide a persistent repository link or DOI to protect against link rot.
  4. [Section 3.2] The full survey instrument is not included; adding it as an appendix would clarify the exact wording of the free-response questions and the frequency-response options, which are central to interpreting the reported percentages.
  5. [Section 4.2; Table 1] The claim that students 'tend to view GenAI as beneficial' is based on means of 6.78 and 7.41 on a 10-point scale with standard deviations around 2.6; please describe the distribution in prose as well, since a wide or bimodal distribution would weaken the interpretation of the mean.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is an empirical survey whose qualitative codes summarize the same data they describe, and it makes no fitted prediction or derivation that reduces to its inputs.

full rationale

This paper is an exploratory survey study, not a derivation or prediction pipeline. It reports frequency-of-use counts, benefit ratings, and inductively developed qualitative codebooks from free-response answers. No equation, fitted parameter, or uniqueness theorem is invoked, and no result is claimed to follow from first principles. The only sense in which the codebooks 'describe' the data is the standard relationship of qualitative content analysis to its corpus: codes are derived from responses and then applied back to those responses. That is a descriptive summarization procedure, not a circular prediction, and the paper explicitly frames the quantitative distributions as potentially non-representative (Section 3.4: 'We acknowledge the limitation that this sample is not representative. Therefore we position our results primarily as qualitative and exploratory'). The paper also discloses the main self-report limitation (Section 3.1: 'students may have withheld or misrepresented information about behaviors perceived as cheating') and the low response rates (12% of undergraduates, 7.6% of graduates). These are validity concerns about sampling and self-report, not circularity. There are no self-citations in the reference list that carry a load-bearing premise, and no uniqueness claim is imported from the authors' prior work. The central claims are empirical generalizations from survey responses, which are exactly the paper's input data; reporting them as findings is the normal function of a survey, not a circular reduction. Score 0.

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

The central claims rest on self-report validity, the adequacy of a non-representative sample for qualitative description, and the reliability of the coding process. No numerical parameters are fitted to data, and no new entities are postulated.

assumptions (3)
  • domain assumption Survey respondents answered truthfully and did not systematically conceal GenAI use.
    Used throughout Section 4, especially the claim in RQ1 that no respondents reported using GenAI to fully complete assignments. Section 3.1 acknowledges the risk of withholding or misrepresentation.
  • domain assumption A non-representative sample can still support qualitative description of emergent use cases.
    Section 3.4 acknowledges the sample is not representative and positions results as qualitative and exploratory, but still reports distributions of codes and ratings as descriptive of the respondent group.
  • domain assumption Directed content analysis with Krippendorff's alpha above 0.6 yields meaningful themes.
    Section 3.3 uses this threshold to finalize codebooks and apply codes to all responses; if the threshold is too low or coding unreliable, the code distributions would be affected.

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Cite this review

Pith. "Pith review of Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023." pith.science (2026). https://pith.science/paper/26JXFE6S

@misc{pith2026241111166,
  author       = {Pith},
  title        = {Pith review of: Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/26JXFE6S}},
  note         = {Machine review of arXiv:2411.11166}
}
read the original abstract

Because of the rapid development and increasing public availability of Generative Artificial Intelligence (GenAI) models and tools, educational institutions and educators must immediately reckon with the impact of students using GenAI. There is limited prior research on computing students' use and perceptions of GenAI. In anticipation of future advances and evolutions of GenAI, we capture a snapshot of student attitudes towards and uses of yet emerging GenAI, in a period of time before university policies had reacted to these technologies. We surveyed all computer science majors in a small engineering-focused R1 university in order to: (1) capture a baseline assessment of how GenAI has been immediately adopted by aspiring computer scientists; (2) describe computing students' GenAI-related needs and concerns for their education and careers; and (3) discuss GenAI influences on CS pedagogy, curriculum, culture, and policy. We present an exploratory qualitative analysis of this data and discuss the impact of our findings on the emerging conversation around GenAI and education.

Figures

Figures reproduced from arXiv: 2411.11166 by the authors.

Figure 1
Figure 1. Summary of RQ1 codes applied. these, 𝑁 = 116 (87.2%) are undergraduate students; this equates to 12% of the total undergraduate population in the department at the time of the survey. 𝑁 = 17 (12.8%) are graduate students; this equates to 7.6% of the graduate population at the time of the survey. We acknowledge the limitation that this sample is not represen￾tative. Therefore we position our results primarily as qual… view at source ↗

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