arxiv: 2604.10720 · v2 · submitted 2026-04-12 · 💻 cs.AI · cs.CL· cs.CY

Recognition: no theorem link

Teaching Language Models How to Code Like Learners: Conversational Serialization for Student Simulation

Charles Koutcheme , Arto Hellas , Juho Leinonen

Authors on Pith no claims yet

Pith reviewed 2026-05-14 21:18 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.CY

keywords artificial studentsstudent simulationprogramming educationlanguage model fine-tuningconversational datadebugging behavioreducational AIprocess data

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The pith

Serializing student logs into conversations trains open models to simulate realistic programming learner behavior.

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

The paper introduces a framework to create artificial students by training open-weight language models on real student programming process data. It converts submission logs into a conversational format with code and feedback as alternating turns, then uses supervised fine-tuning and preference optimization to align the model with actual debugging sequences. This is evaluated on Qwen models of 4B and 8B parameters using large datasets from Python assignments. The results indicate that including environment feedback improves the models' ability to match student behavior in terms of functional correctness and code similarity compared to approaches that use only code or rely on prompted large models. Such simulations could help test educational tools at scale while keeping data private and avoiding proprietary dependencies.

Core claim

By representing each student's problem-solving process as a dialogue between the learner and the automated assessment system, where submissions and feedback like test outcomes form alternating turns, models can be trained to replicate authentic student debugging behavior more effectively than prior methods.

What carries the argument

Conversational serialization of temporal student log traces into alternating turns of code submissions and environment feedback.

If this is right

Trained models can replicate iterative debugging processes more accurately when environment feedback is included.
Open-weight models become viable alternatives to prompted proprietary LLMs for student simulation.
Scalable evaluation of tutoring strategies becomes possible using realistic artificial students.
The training pipeline of supervised fine-tuning combined with preference optimization aligns models to real learner patterns.
Privacy and cost concerns are reduced by avoiding large proprietary models and enabling local training.

Where Pith is reading between the lines

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

Similar serialization techniques might apply to simulating learners in other domains like math or writing if process logs are available.
These models could be used to generate synthetic datasets for training better tutoring systems.
Testing on different programming languages or assignment types could reveal how general the approach is.
If the simulations are accurate, they might help identify common student misconceptions automatically.

Load-bearing premise

Converting student logs into conversational turns captures enough of the original context and intent so the model learns real behavior instead of just surface patterns.

What would settle it

Training the models both with and without environment feedback and finding no significant improvement in functional alignment or code similarity on a test set of real student traces would falsify the benefit of including feedback.

Figures

Figures reproduced from arXiv: 2604.10720 by Arto Hellas, Charles Koutcheme, Juho Leinonen.

**Figure 2.** Figure 2: Grade progression across normalized trajectory po [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

read the original abstract

Artificial students -- models that simulate how learners act and respond within educational systems -- are a promising tool for evaluating tutoring strategies and feedback mechanisms at scale. However, most existing approaches rely on prompting large, proprietary language models, limiting adaptability to specific courses and raising concerns around privacy, cost, and dependence. In this work, we propose a framework for training open-weight artificial programming learners directly from authentic student process data. Our approach serializes temporal log traces into a conversational format, representing each student's problem-solving process as a dialogue between the learner and their automated assessment system. Student code submissions and environment feedback, such as test outcomes, grades, and error traces, form alternating conversational turns, enabling models to learn from the iterative debugging process. We additionally introduce a training pipeline combining supervised fine-tuning with preference optimization to align models with authentic student debugging behavior. We evaluate our framework by training Qwen models at 4B and 8B scales on a large-scale dataset of real student submissions to Python programming assignments. Our results show that incorporating environment feedback strengthens models' ability to replicate student debugging behavior, improving over both prior code-only approaches and prompted large language models baselines in functional alignment and code similarity. We release our code to support reproducibility.

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.

Desk Editor's Note private letter to a colleague

Serializing student logs into learner-assessor chat turns plus SFT and preference optimization gives measurable gains on open models for simulating debugging, but the format's contribution over raw feedback tokens is not isolated.

read the letter

The main point is that this paper turns real student submission traces into alternating conversational turns between a learner and an auto-grader, then fine-tunes smaller open-weight models to match actual debugging sequences. On Qwen 4B and 8B it beats both code-only baselines and prompted large models on functional alignment and code similarity, using held-out student data from Python assignments. They also release the code, which is useful for anyone who wants to replicate or extend it. That combination of real data, open models, and reproducibility is the clearest practical step forward here. The approach avoids proprietary APIs and live student experiments, which matters for scaling tutoring evaluations. The weakest part is the missing ablation on the conversational structure itself. The gains are attributed to the alternating-turn format that includes environment feedback, yet there is no direct test of whether the same feedback tokens would produce similar results without the dialogue framing. If multi-turn edits or exploratory dead-ends get collapsed, the model could be learning surface co-occurrences rather than the order of student decisions. The abstract does not report statistical tests or exact data splits, so the strength of the improvement is still moderate until the full evaluation details are checked. This work is aimed at edtech researchers who need controllable student simulators for course design or feedback testing. It is grounded enough in external baselines and real traces to merit a serious referee, even if revisions will be needed on the ablation and analysis of information loss.

Referee Report

2 major / 3 minor

Summary. The paper claims that serializing real student programming log traces into alternating conversational turns between learner and automated assessment system (incorporating code submissions and environment feedback such as test outcomes and error traces), followed by supervised fine-tuning plus preference optimization on open-weight Qwen 4B/8B models, produces artificial students that more faithfully replicate authentic debugging behavior. This yields measurable gains in functional alignment and code similarity over code-only baselines and prompted-LLM baselines on held-out student data.

Significance. If the central claim holds, the work supplies a reproducible, open-weight route to scalable artificial-student simulators that avoids proprietary-model dependence, improves privacy and cost, and directly leverages authentic process data. The public code release is a concrete strength that enables community verification and extension for tutoring-strategy evaluation.

major comments (2)

[§5] §5 (Evaluation): the reported gains in functional alignment and code similarity are attributed to conversational serialization, yet no ablation isolates the alternating-turn format from the mere presence of feedback tokens. Without this comparison the attribution to the serialization step remains untested and the weakest assumption (preservation of debugging intent) is not directly addressed.
[§4] §4 (Method): the serialization procedure collapses multi-turn edits, pauses, and exploratory dead-ends into single turns, but the manuscript provides neither a quantitative measure of information loss nor an analysis showing that the resulting dialogues retain decision order and implicit intent rather than surface co-occurrences.

minor comments (3)

[§5.1] Exact prompt templates, model versions, and decoding parameters for the prompted-LLM baselines are not specified, hindering direct replication.
[§5.2] Statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals) for the functional-alignment and code-similarity deltas are absent.
[Data section] Precise descriptions of the train/validation/test splits, number of unique students, and assignment distribution are missing from the data section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the insightful comments on our manuscript. We address each major comment below and have updated the paper accordingly to improve clarity and rigor.

read point-by-point responses

Referee: [§5] §5 (Evaluation): the reported gains in functional alignment and code similarity are attributed to conversational serialization, yet no ablation isolates the alternating-turn format from the mere presence of feedback tokens. Without this comparison the attribution to the serialization step remains untested and the weakest assumption (preservation of debugging intent) is not directly addressed.

Authors: We agree that an explicit ablation separating the alternating-turn structure from the inclusion of feedback tokens would strengthen the attribution. Our current code-only baseline removes feedback entirely, and the conversational models include both elements. In the revised version, we will include an additional baseline where feedback is appended without the conversational turn format to isolate the effect of serialization. This addresses the concern about testing the preservation of debugging intent through the format. revision: yes
Referee: [§4] §4 (Method): the serialization procedure collapses multi-turn edits, pauses, and exploratory dead-ends into single turns, but the manuscript provides neither a quantitative measure of information loss nor an analysis showing that the resulting dialogues retain decision order and implicit intent rather than surface co-occurrences.

Authors: We acknowledge that the serialization process involves some aggregation of student actions into turns, which could potentially lose fine-grained temporal information. However, our evaluation demonstrates that models trained on these serialized dialogues better replicate real student debugging sequences compared to baselines, indicating that critical decision points are retained. We will add a new subsection in the revised manuscript providing a qualitative analysis of sample serialized dialogues alongside original logs to illustrate preservation of intent, and discuss the trade-offs of this approach. revision: partial

Circularity Check

0 steps flagged

No circularity; derivation uses held-out data and external baselines

full rationale

The paper serializes real student logs into conversational turns, applies supervised fine-tuning plus preference optimization, and evaluates functional alignment and code similarity on held-out student submissions against independent baselines (code-only models and prompted LLMs). No equations, parameters, or claims reduce by construction to the inputs; the reported gains are measured via external comparison rather than self-referential fitting or self-citation chains. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework relies on standard supervised fine-tuning and preference-optimization assumptions applied to a new data representation; no new free parameters or invented entities are introduced beyond ordinary training hyperparameters.

axioms (1)

domain assumption Student debugging behavior can be adequately captured by alternating code-submission and environment-feedback turns in a conversational format.
This assumption is invoked when the authors serialize temporal logs into dialogue turns and claim the resulting data trains models to replicate authentic learner behavior.

pith-pipeline@v0.9.0 · 5524 in / 1292 out tokens · 41869 ms · 2026-05-14T21:18:06.410987+00:00 · methodology

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discussion (0)

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Works this paper leans on

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INTRODUCTION To better support learners at scale, computing education re- search has long relied on models of students built from the rich log data collected as they solve programming assign- ments [16, 41]. Much of this work focuses on capturing what students know, such as estimating mastery over concepts through knowledge tracing [6, 17, 40], or identif...

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RELATED WORK A complementary line of work focuses onknowledge tracing (KT), whose primary goal is to estimate a student’s mastery of knowledge components across exercises. While early ap- proaches focused exclusively on predicting success on future assignments, more recent methods leverage language-model– based architectures to predict students’ first sub...

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FROM LOGS TO DIALOGS In this section, we introduce our approach for transforming student log data into suitable data for student simulation. Our work assumes that students interact with an automated assessment system returning summative feedback [5]. 3.1 Assumptions We assume access to a deterministic grading function which, for a submitted programato a p...

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Our core pipeline combines supervised fine-tuning with offline preference optimization on a serial- ized datasetD

TRAINING ARTIFICIAL LEARNERS In this section, we present our training pipeline for training a language modelπ θ to simulate how programming students solve assignments. Our core pipeline combines supervised fine-tuning with offline preference optimization on a serial- ized datasetD. We additionally explore online preference optimization as an alternative t...

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