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Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The paper claims that a ChatGPT-based teachable agent—an AI configured as a help-seeking student—improves programming learners' knowledge gains, pseudocode clarity and readability, and self-efficacy, while leaving code correctness…

desk verdict A confounded but readable pilot study of ChatGPT as a teachable agent: the results are directionally plausible, but the design cannot separate teaching from AI assistance. read the letter →

arxiv 2412.15226 v1 pith:LA5K2GZQ submitted 2024-12-05 cs.CY cs.AIstat.AP

Angxuan Chen , Yuang Wei , Huixiao Le , Yan Zhang This is my paper · ORCID
classification cs.CYcs.AIstat.AP
keywords ChatGPTteachableagentprogrammingeducationself-regulatedlearningbyteachinglargelanguagemodelseightqueenspuzzle
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 that a ChatGPT-based teachable agent—an AI prompted to act as a curious, help-seeking student—can support learning-by-teaching in programming education. In a small experiment on the eight-queens backtracking problem, students who taught the ChatGPT agent scored higher on a knowledge test, wrote pseudocode rated higher in clearness and readability, and reported stronger self-efficacy and cognitive strategies than students who learned only from instructional videos and wrote code themselves. Code correctness did not improve, which the authors attribute to the agent generating correct code and so removing chances to practice debugging. If the result holds, ChatGPT offers an inexpensive replacement for hand-built teachable agents and a natural-language route to socialized learning-by-teaching.

What carries the argument

The central object is the 'teachable ChatGPT agent': a GPT-4 session prompted to behave as a naive learner who asks for help in stages, using a five-stage help-seeking process model drawn from the learning sciences. Learners must converse with it in natural language to produce a working C++ solution to the eight-queens puzzle, which an online judging platform verifies. The mechanism is the teaching pressure itself—learners have to articulate the backtracking algorithm, break the task into parts, answer the agent's questions, and take responsibility for the final code. The agent's tendency to answer with correct code is the same machinery used to explain why error-correction skills did not improve: fewer bugs appeared, so learners had fewer opportunities to fix them.

What would settle it

Run the same eight-queens task in three conditions—teachable ChatGPT, ChatGPT as a non-teachable code-writing assistant, and video-only control—with pseudocode scored by raters blind to condition and the analysis pre-registered. If the assistant condition reproduces the teachable condition's gains, the student role is not what matters; if the teachable group still wins, the teaching interaction carries the effect.

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Extended reading notes

Core claim

The central claim is that a ChatGPT model prompted to act as a teachable student can carry the learning-by-teaching effect in programming education. In the paper's pre/post experiment, students who taught the agent about the eight-queens puzzle had higher adjusted post-test scores on backtracking knowledge than the video-and-self-coding control group; their pseudocode was scored higher on clearness and readability but not correctness; and their self-reported self-efficacy and cognitive strategies improved, while test anxiety did not. The authors interpret these patterns as evidence that natural-language teaching produces deeper cognitive processing, that generating correct code on demand robs learners of debugging practice, and that guiding the agent requires planning and task decomposition, which are self-regulated learning strategies. They also chose not to suppress ChatGPT's occasional hallucinations, arguing that beginner-like mistakes give learners something to correct.

Load-bearing premise

The study assumes the only meaningful difference between groups was the teachable agent, yet the experimental group guided ChatGPT to generate code while the control group wrote code alone from scratch, so the reported gains could be due to model-generated code or extra interactivity rather than to teaching.

Editorial extensions

If this is right

  • Natural-language teaching with ChatGPT can serve as a scalable, low-cost version of learning-by-teaching that does not require hand-built tutoring systems.
  • Courses using such agents should expect gains in knowledge and code clarity but plan separate debugging exercises, because correct code from the agent suppresses error-correction practice.
  • Deliberately letting the agent produce occasional wrong code—or using a weaker model—may turn the same setup into a debugging trainer.
  • The self-efficacy and cognitive-strategy gains imply that ChatGPT-based teaching can act as a lightweight intervention for self-regulated learning, not just a content tutor.

Reading between the lines

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

  • A testable extension the paper leaves open is a three-arm study: teachable ChatGPT, ChatGPT as a plain code-writing assistant, and video-only control, which would separate the student-role effect from pure exposure to generated code.
  • The error-correction mechanism could be tested directly by injecting intentional bugs into the agent's generated code; the paper's explanation predicts that learners would then gain debugging practice and improve on code correctness.
  • The SRL finding suggests a follow-up that scaffolds metacognitive prompts inside the teaching dialogue—asking learners to set goals and reflect before each teaching turn—and measures whether self-regulated learning gains grow further.
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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

4 major / 5 minor

Summary. The paper reports a quasi-experimental comparison of two instructional conditions for learning to solve the eight-queens problem in C++. Forty-one university students were assigned to an experimental group (EG) that interacted with a ChatGPT-based teachable agent and guided it to generate solution code, or to a control group (CG) that learned from online videos and wrote solution code from scratch. Pre- and post-tests measured knowledge, pseudo-code quality (clearness, correctness, readability), and self-regulated learning (test anxiety, self-efficacy, cognitive strategies). ANCOVA results are reported as showing significantly larger knowledge gains, higher clearness/readability scores, and higher self-efficacy/cognitive-strategy scores for EG, with no significant difference in correctness. The paper interprets these outcomes as evidence that ChatGPT can serve as an effective teachable agent for learning-by-teaching.

Significance. If the causal interpretation were justified, this would be a valuable, low-cost extension of the teachable-agent literature to LLM-based natural-language interaction. The study uses several sensible measurement tools, including an automated judging platform, an experienced-lecturer knowledge test, and two scorers with reported inter-rater agreement for pseudo-code, and it acknowledges an important limitation about missing conversation data. However, the design does not isolate the 'teaching' role from the many other differences between conditions, and the paper's central causal claims go beyond what the data can support. The contribution is better characterized as an exploratory comparison of two instructional conditions, with useful hypotheses for future work.

major comments (4)
  1. [§3.2 and §3.3] The intervention is confounded with AI-assisted code generation and task type. EG participants guided ChatGPT to produce code, while CG participants wrote code from scratch in a local editor. The conditions therefore differ in at least four respects: natural-language interaction with an LLM, exposure to model solutions, amount of hands-on debugging (EG averaged 1.95 submissions versus 2.90 for CG, as reported in §5.2), and the instruction to adopt a teaching role. ANCOVA with pre-test scores as a covariate cannot control for these unmeasured task-related differences. Consequently, the conclusion that 'learners who learning AI knowledge with teaching the ChatGPT teachable agent have better knowledge gains' (§4.1) is not identifiable from this design. The manuscript should either reframe the claims as a comparison of two instructional conditions or add a control condition that isolates the teaching role from mere AI assistance.
  2. [Limitations section] The authors state that the study 'lacks of collecting and analysing students' conversation data on the teaching process.' Without such data, there is no direct evidence that EG participants actually engaged in explanatory teaching (e.g., giving explanations, asking questions, providing feedback) rather than simply issuing code-generation instructions. This is load-bearing for the learning-by-teaching mechanism and for the fidelity of the manipulation. The reported outcome differences cannot be attributed to teaching behavior unless process evidence or a manipulation check is provided.
  3. [Abstract and Conclusion] The abstract claims that interacting with ChatGPT improves programming abilities 'particularly in writing readable and logically sound code,' and the conclusion repeats 'writing readable and logically sound code.' This overstates the results: §4.2 and Table 3 report no significant difference in code correctness (F = 2.98, p > 0.05, η² = 0.19), and the adjusted means are nearly identical. The claims should be restricted to clearness and readability, with correctness reported as non-significant.
  4. [§3.1 and §3.4.2] The manuscript does not describe the assignment procedure (randomization, matching, or self-selection) and does not state whether the two pseudo-code scorers were blind to condition. Non-blind scoring can bias the clearness and readability ratings, which are the main programming-skill findings. The authors should report the assignment method and the blinding status of the scorers, or explicitly acknowledge the risk of scoring bias and interpret the corresponding results with that caveat.
minor comments (5)
  1. [Table 3] The third dimension is labeled 'Clearness' again, but based on the text and the scoring standard in Table 1 it should be 'Readability.'
  2. [§4.1] The sentence 'the three groups were significantly different' should refer to two groups.
  3. [Table 3] The significance stars for the clearness row are inconsistent with the text: the text reports F = 7.39, p < 0.01, but the table marks *** (p < 0.001). Please align the notation.
  4. [§4.3] Levene's test checks equality of variances, not differences in pre-test means; the sentence 'Levene's test ... revealed no significant difference in the pre-test scores between the two groups (F = 2.07, p > 0.05)' is therefore misleading and should be reworded.
  5. [Throughout] There are numerous language issues that should be corrected, for example 'learners who learning AI knowledge,' 'can be ruled towards generating fully correct code,' and 'This finding was echoed previous studies.' A careful proofreading pass is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular reasoning: the paper is an empirical comparison whose outcome measures are independent of the intervention and whose central claims rest on ANCOVA contrasts, not on definitions or self-citations.

full rationale

The paper reports a quasi-experiment comparing a ChatGPT teachable-agent condition with an online-video control on knowledge gains, pseudocode quality, and self-reported SRL. The key comparisons (Tables 2-4) are computed from pre/post test scores, rater-scored pseudocode, and an adapted MSLQ questionnaire; none of these instruments is defined in terms of the intervention effect, and no fitted parameter is later relabeled as a prediction. The knowledge test and SRL questionnaire are external measurement instruments with reported reliability, and the pseudocode rubric is an independent scoring standard. The central claims are empirical contrasts, not derivations that reduce to their inputs. The design does conflate the teachable-agent role with LLM interaction and AI-generated code, and the Limitations section explicitly acknowledges the absence of conversation-data analysis and the small sample size; however, these are threats to internal and construct validity, not circularity. No load-bearing self-citation chain appears: prior teachable-agent work is cited as external background, and no uniqueness theorem or prior result by the present authors is invoked to force the conclusion. Therefore the circularity score is 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The study introduces no new entities, parameters fitted to data, or mathematical constructs. Its load-bearing assumptions are the validity of the measurement instruments and the adequacy of the ANCOVA to control for group differences in a non-randomized, confounded design.

assumptions (5)
  • domain assumption The 15-item knowledge test designed by a lecturer validly measures knowledge of the backtracking algorithm and the Eight Queens puzzle.
    Section 3.4.1 states the test 'has been used for over 5 years' and is 'proven to be valid', but provides no reliability statistics or validation details.
  • domain assumption The pseudocode scoring rubric (clearness, correctness, readability) captures programming ability, and the two scorers' judgments are consistent.
    Section 3.4.2 reports Spearman correlations of 0.833-0.935 between raters, but no evidence that raters were blind to group assignment.
  • domain assumption The adapted 20-item MSLQ measures self-regulated learning abilities as intended.
    Section 3.4.3 reports Cronbach's alpha between 0.791 and 0.912, but the adaptation from 44 to 20 items is not fully documented.
  • domain assumption Participants' self-reported 'fundamental coding ability in C++' is accurate.
    Section 3.1 uses this self-report as an inclusion criterion without an objective verification.
  • standard math ANCOVA assumptions of normality and homogeneity of regression hold, and the pre-test covariate adequately controls for baseline differences between non-randomized groups.
    Sections 4.1-4.3 report homogeneity tests, but these tests do not compensate for the lack of random assignment and the confounded task difference between conditions.

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Cite this review

Pith. "Pith review of Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education." pith.science (2026). https://pith.science/paper/LA5K2GZQ

@misc{pith2026241215226,
  author       = {Pith},
  title        = {Pith review of: Learning-by-teaching with ChatGPT: The effect of teachable ChatGPT agent on programming education},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LA5K2GZQ}},
  note         = {Machine review of arXiv:2412.15226}
}
read the original abstract

This study investigates the potential of using ChatGPT as a teachable agent to support students' learning by teaching process, specifically in programming education. While learning by teaching is an effective pedagogical strategy for promoting active learning, traditional teachable agents have limitations, particularly in facilitating natural language dialogue. Our research explored whether ChatGPT, with its ability to engage learners in natural conversations, can support this process. The findings reveal that interacting with ChatGPT improves students' knowledge gains and programming abilities, particularly in writing readable and logically sound code. However, it had limited impact on developing learners' error-correction skills, likely because ChatGPT tends to generate correct code, reducing opportunities for students to practice debugging. Additionally, students' self-regulated learning (SRL) abilities improved, suggesting that teaching ChatGPT fosters learners' higher self-efficacy and better implementation of SRL strategies. This study discussed the role of natural dialogue in fostering socialized learning by teaching, and explored ChatGPT's specific contributions in supporting students' SRL through the learning by teaching process. Overall, the study highlights ChatGPT's potential as a teachable agent, offering insights for future research on ChatGPT-supported education.

Figures

Figures reproduced from arXiv: 2412.15226 by the authors.

Figure 1
Figure 1. A sample solution of “eight queens" puzzle that no two queens in the same row, column, or diagonal [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. Figure.3. First, all the students were required to finished a pre-test online questionnaire including knowledge test and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 2
Figure 2. An example of learners’ teaching in solving the “eight queens" puzzle [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figures from the paper (1 more)
Figure 3
Figure 3. Figure 3: Procedure of the experiment [PITH_FULL_IMAGE:figures/full_fig_p007_3.png]

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Forward citations

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.