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arxiv: 2605.21374 · v1 · pith:CYHQFLNSnew · submitted 2026-05-20 · 💻 cs.HC

Combating Harms of Generative AI in CS1 with Code Review Interviews and a Flipped Classroom

Peter Fowles , Erik Falor , Sulove Bhattarai , John Edwards , Seth Poulsen This is my paper

Pith reviewed 2026-05-21 03:29 UTC · model grok-4.3

classification 💻 cs.HC
keywords generative AI in educationCS1 pedagogyoral code reviewsflipped classroomLLM usagestudent learning outcomeskeystroke loggingformative assessment
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The pith

Oral code reviews in a flipped CS1 class preserve student understanding even as LLM usage for assignments rises sharply.

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

The paper examines how to address the problem of students using large language models to complete programming assignments without learning the underlying concepts. It proposes weekly oral code review interviews where students explain their code, paired with a flipped classroom that frees up time for these reviews. Data from three semesters shows that exam scores stayed about the same or slightly higher in the new format, while keystroke logs indicate much more code was pasted from AI sources. Surveys found students generally liked the code reviews, with complaints mainly about scheduling. This suggests the approach lets students use AI tools freely while still building real understanding.

Core claim

Through oral code review assessments and a flipped classroom in CS1, students maintain adequate performance on written exams despite a dramatic increase in the use of generative AI for completing coding assignments. Analysis of exam scores across semesters shows no statistically significant decline, keystroke data confirms higher AI involvement through increased pasting, and end-of-semester surveys indicate positive attitudes toward the interviews.

What carries the argument

Weekly oral code review interviews, in which students must explain and justify the code they submitted for assignments, supported by a flipped classroom model that allocates class time for learning concepts independently and scheduling flexibility for interviews.

If this is right

  • Students retain conceptual understanding as measured by exam performance even when relying more on AI for code production.
  • Formative oral assessments can incentivize metacognitive engagement with code regardless of its source.
  • Positive student feedback supports the feasibility of scaling this model with improvements in scheduling and training.
  • The combination allows experimentation with AI tools without apparent harm to foundational learning in introductory programming.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar oral review methods might transfer to other introductory STEM courses facing AI tools.
  • Institutions could shift from detection and punishment of AI use toward redesigned assessments that reward explanation.
  • Repeated oral reviews may build stronger habits of code comprehension beyond what written exams alone achieve.
  • Data on long-term retention or transfer to new problems would further test the approach.

Load-bearing premise

Oral code review interviews accurately measure and promote deep conceptual understanding instead of students simply preparing rote explanations for the interview.

What would settle it

A follow-up assessment where students must solve new programming problems or explain code modifications without prior preparation would show significantly lower performance if the reviews only encourage superficial knowledge.

Figures

Figures reproduced from arXiv: 2605.21374 by Erik Falor, John Edwards, Peter Fowles, Seth Poulsen, Sulove Bhattarai.

Figure 1
Figure 1. Figure 1: Distribution of exam scores across the Fall 2023, Fall 2024, and CS1-CR semesters. Results on both exams were relatively stable [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Difference in paste events in coding assignments between the Fall 2024 and CS1-CR semesters. (a) Distribution of the total [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) Distribution of number characters pasted in each submission. (b) Distribution of percentage of characters pasted per total [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Results from multiple choice survey questions distributed to CS1-CR students at the end of the CS1-CR semester. [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Results from Likert scale survey questions distributed to CS1-CR students at the end of the CS1-CR semester. These results [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
read the original abstract

Background and Context: Large Language Models (LLMs) are more accessible and accurate than ever before, raising significant concerns for computing educators. One major concern is students using LLMs to bypass the effort needed to understand concepts and metacognitive strategies essential for success in computer science. Objectives: We contribute a unique approach to assessing and building up student understanding through weekly oral code review assessments. These formative assessments incentivize students to understand their submitted code, regardless of whether or not the code was generated by AI tools. We also use a flipped classroom to provide time for students to learn concepts outside of class and provide ample time for students to schedule code review interviews. Methods: For this paper, we collected data from three semesters. We analyze student exam scores, keystroke logs, and surveys to understand how the new course policies affected student learning, behavior, and attitudes. Findings: Pairwise comparison of exam results reveals a statistically insignificant increase in average scores for Fall 2025 compared to previous semesters. Keystroke logs show a significant increase in characters pasted per total characters input into coding assignments in Fall 2025, pointing towards higher AI usage. Survey results show positive student sentiment towards code reviews at the end of Fall 2025, with nearly all negative feedback being addressable through better scheduling and more rigorous TA training. Implications: Oral code reviews with a flipped classroom appear to be effective at mitigating harms of LLM use while providing space for students to freely experiment with these tools. Our work suggests that students in Fall 2025 still show adequate understanding of material covered in written exams, despite dramatic increases in LLM usage for coding assignments.

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 manuscript describes an educational intervention in a CS1 course that combines weekly oral code review interviews with a flipped classroom to mitigate harms from students using LLMs to bypass conceptual understanding and metacognitive strategies. Drawing on data from three semesters, the authors compare exam scores, keystroke logs showing pasted characters, and end-of-semester surveys, reporting a statistically insignificant increase in average exam scores in Fall 2025 alongside a significant rise in pasting (indicating higher LLM usage) and generally positive student feedback on the code reviews.

Significance. If the methodological gaps are addressed, the work could provide a practical, replicable model for CS educators seeking to accommodate AI tools while using formative oral assessments to promote genuine comprehension. The multi-method approach (logs + exams + surveys) offers a useful template for studying behavioral changes in AI-augmented programming courses.

major comments (3)
  1. [Methods] Methods: The description of data collection and analysis lacks sample sizes per semester, the exact statistical tests and p-values for the pairwise exam-score comparisons, effect sizes, and any controls or covariates for semester-to-semester differences in student population, exam difficulty, or course content.
  2. [Findings] Findings and Implications: The claim that stable exam scores demonstrate 'adequate understanding' and mitigation of LLM harms assumes written exams assess the explanatory and debugging skills practiced in oral reviews, yet no example exam items, alignment analysis, or evidence ruling out superficial interview preparation is provided.
  3. [Findings] Findings: The significant increase in pasted characters is presented as evidence of higher AI usage, but without baseline pasting rates from prior semesters or validation that pasting correlates with LLM rather than other copy-paste behaviors, the link to the intervention's effectiveness remains indirect.
minor comments (2)
  1. [Abstract] Abstract: The abstract refers to 'three semesters' and 'Fall 2025' without naming the comparison semesters or clarifying whether the prior data were collected under identical course policies.
  2. [Findings] Survey results: Positive sentiment is reported, but response rate, number of respondents, and breakdown of negative feedback categories should be included to evaluate how representative the views are.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which has helped us identify areas to strengthen the manuscript. We address each major comment below and have revised the paper accordingly to improve methodological transparency and the presentation of findings. We believe these changes enhance the work without altering its core contributions.

read point-by-point responses

  1. Referee: [Methods] Methods: The description of data collection and analysis lacks sample sizes per semester, the exact statistical tests and p-values for the pairwise exam-score comparisons, effect sizes, and any controls or covariates for semester-to-semester differences in student population, exam difficulty, or course content.

    Authors: We agree that these details were insufficiently reported in the original submission. In the revised manuscript, we have added the per-semester sample sizes, specified that pairwise comparisons used independent-samples t-tests, reported exact p-values along with Cohen's d effect sizes, and included a discussion of potential covariates (e.g., student demographics and minor variations in exam content). We also explicitly note the limitations of retrospective data collection in fully controlling for all semester-to-semester differences. These additions directly address the transparency concerns. revision: yes

  2. Referee: [Findings] Findings and Implications: The claim that stable exam scores demonstrate 'adequate understanding' and mitigation of LLM harms assumes written exams assess the explanatory and debugging skills practiced in oral reviews, yet no example exam items, alignment analysis, or evidence ruling out superficial interview preparation is provided.

    Authors: This is a fair critique of the linkage between assessment types. While the manuscript presents stable exam scores as evidence of maintained understanding, we acknowledge the absence of explicit examples and formal alignment. In revision we will add representative exam items that require explanation and debugging, include a short description of how oral review skills map to exam performance, and discuss why weekly interviews covering all assignments make superficial preparation unlikely. We maintain that the multi-method data (exams + logs + surveys) supports our interpretation but will strengthen this section with the requested details. revision: partial

  3. Referee: [Findings] Findings: The significant increase in pasted characters is presented as evidence of higher AI usage, but without baseline pasting rates from prior semesters or validation that pasting correlates with LLM rather than other copy-paste behaviors, the link to the intervention's effectiveness remains indirect.

    Authors: The manuscript does report a statistically significant increase in pasted characters for Fall 2025 relative to prior semesters, which implies baseline data were analyzed. However, we agree that explicit baseline rates and stronger validation of the pasting metric were not detailed enough. In the revision we will report the actual prior-semester pasting percentages for direct comparison and add discussion of why we interpret elevated pasting as primarily LLM-related (e.g., log timing patterns and assignment context), while acknowledging that other copy-paste sources could contribute. This will make the behavioral evidence more explicit. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical comparisons with no derivations or self-referential reductions

full rationale

This is an empirical education research paper that reports data collection from three semesters, including exam score comparisons, keystroke log analysis for paste rates, and end-of-semester surveys. The central findings rest on direct statistical pairwise comparisons and descriptive survey results rather than any mathematical derivation, fitted model, uniqueness theorem, or ansatz that could reduce to its own inputs by construction. No equations, predictions, or load-bearing self-citations appear in the provided text; the argument is self-contained against the collected external benchmarks (prior semesters and student responses).

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work is an empirical education study that relies on standard statistical comparison methods and domain assumptions about what exam scores and interview performance indicate; no new free parameters, invented entities, or ad-hoc axioms are introduced.

axioms (2)
  • domain assumption Pairwise semester comparisons of exam scores are valid when course content and grading standards remain comparable across terms
    Invoked when reporting insignificant score changes between Fall 2025 and prior semesters.
  • domain assumption Increased ratio of pasted characters to total keystrokes serves as a proxy for higher LLM usage
    Used to interpret the significant rise in pasted characters as evidence of greater AI assistance.

pith-pipeline@v0.9.0 · 5848 in / 1408 out tokens · 63728 ms · 2026-05-21T03:29:09.090037+00:00 · methodology

discussion (0)

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Reference graph

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