CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models

Jun Wang; Runze Li; Siyu Wu; Wei Zhang

REVIEW 6 major objections 5 minor 15 references

CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models

T0 review · 6 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A two-LLM framework iteratively refines natural-language student profiles by using the predictor's own correctness as a reward, and reports accuracy gains of up to six points over knowledge-tracing baselines on three education datasets.

desk verdict A new and plausible LLM-based KT loop with a real compute confound in its central ablation; worth refereeing, but the iteration claim needs cleaner evidence. read the letter →

arxiv 2505.17705 v1 pith:VEXEBRPR submitted 2025-05-23 cs.AI cs.LG

Runze Li , Siyu Wu , Jun Wang , Wei Zhang This is my paper · ORCID

classification cs.AIcs.LG

keywords knowledgetracinglargelanguagemodelsstudentprofilingexplainabilityiterativeoptimizationpolicygradientKTOeducationaldatamining

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 claims that knowledge-tracing accuracy and explainability can be improved together by a two-LLM setup in which one model writes a plain-language profile of a student from their answer history and a second model forecasts the next answer from that profile plus the history. The novel piece is the iterative loop: the predictor's hit-or-miss on the next response becomes a binary reward that updates the profile writer with a KTO-style policy-gradient loss, and the predictor is then retrained on the improved profiles. On three public education datasets the framework reports accuracy gains of several points over the strongest deep knowledge-tracing baselines and over direct applications of GPT-4o and Deepseek-R1, with the largest margins on long interaction sequences. A sympathetic reader would care because these results suggest that natural-language, human-readable student models need not come at the price of predictive performance.

What carries the argument

The load-bearing mechanism is the collaborative iterative loop between the Analyst and the Predictor. The pipeline runs in four stages: distillation, in which a teacher LLM (GPT-4o) writes initial profiles that are curated and used to train the Analyst by cross-entropy; profiling, in which the trained Analyst produces profiles for the whole dataset; reasoning, in which the Predictor is trained with binary cross-entropy on the history, profile, and target exercise; and iteration, in which the Predictor's prediction on the next exercise is compared to the ground-truth response to give a binary reward $r_{t+1}\in\{+1,-1\}$ (Eq. 9), and the Analyst is updated by maximizing $\log \pi_{\theta_A}(p_t|x_t)$ weighted by that reward (Eq. 10), after which the Predictor is retrained on the improved profiles (Eq. 11). This is the KTO (Kahneman-Tversky Optimization) scheme, applied here with the downstream predictor's correctness as the 'desirable/undesirable' label.

What would settle it

Run the same CIKT pipeline with the reward in Eq. (9) replaced by a fair coin flip (or by the opposite of the true correctness), and compare held-out accuracy; if the gains persist under random rewards, the paper's credit-assignment assumption is not doing the work reported.

Watch

Extended reading notes

Core claim

The central claim is that student-knowledge profiles written in natural language by an Analyst LLM are not just a byproduct of the system but the load-bearing feature that lets a Predictor LLM forecast next-answer correctness, and that the profiles can be progressively improved by feeding the Predictor's correctness back into the Analyst. Concretely, the Analyst receives a student's chronological interaction sequence and outputs a structured textual profile summarizing mastery, patterns, and areas of difficulty; the Predictor conditions on history, profile, and target exercise to produce the next-response probability. The iteration step treats the binary correctness of that prediction as reward $r_{t+1}$ (Eq. 9), maximizes the log-probability of the profile that led to the correct judgment (Eq. 10), and then retrains the Predictor on profiles from the updated Analyst (Eq. 11). The paper reports that this loop yields significant accuracy improvements on ASSIST2009, ASSIST2012, and Eedi, and the ablations attribute much of the gain to the presence of the profile and to the iterative refinement.

Load-bearing premise

The loop assumes that the predictor's binary correctness on the next answer is a trustworthy credit-assignment signal for improving the written student profile, rather than a noisy target the loop can overfit to training labels.

Editorial extensions

If this is right

If the central claim is right, natural-language student profiles become a load-bearing input rather than a post-hoc explanation, so transparency and accuracy stop trading off.
The iterative recipe—distill from a strong teacher, then reinforce the generator with the predictor's binary correctness—could generalize to other LLM-based prediction tasks that lack explicit preference labels.
The reported advantage on sequences longer than fifteen interactions implies the profile mechanism extracts more value from longer histories than the compared baselines do.
Because the components are separate, the Predictor can be swapped or retrained against a fixed Analyst, enabling incremental system upgrades without restarting profile generation.

Reading between the lines

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

The paper does not test whether profiles refined on training students transfer to a held-out cohort; a transfer experiment would separate genuine profile improvement from overfitting to the training set's label distribution.
A natural control is to replace the reward in Eq. (9) with a random binary signal; if similar accuracy gains appear, the reported improvement would be attributable to the fine-tuning and architecture rather than to the informational content of the reward.
The binary reward discards the predictor's confidence; using the predicted probability as a soft reward could give the Analyst a denser signal and lower the variance of the policy-gradient update.
Because the framework ignores question stem text, it cannot comment on the specific content of misconception; folding in stems would test whether content-aware profiles yield further gains and more usable recommendations.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

I just read CIKT. The core idea—an LLM Analyst writing textual student profiles, a Predictor trained on them, and a loop where the Predictor's binary accuracy rewards refine the Analyst—is genuinely new relative to the KT papers they cite, and it's a reasonable way to get explainability and accuracy into one system. The paper is clearly written, the three public datasets are the right kind, and the w/o Profile ablation (profiles withheld at inference) shows a solid drop, which tells me the profiles carry information. That's a good sign.

But the central claim that the iterative loop drives the gains is not established. The Table 3 ablation compares full CIKT (distillation + three iterations, each with profile regeneration, an RL update, and full Predictor retraining) to 'w/o Iteration' (a single pass). That's an unmatched-compute comparison. The extra 0.9-2.5 ACC could just be more training epochs or more profile diversity, not the specific feedback signal. That's a load-bearing flaw, and it's fixable: match compute, or add a control that does the same number of passes without the reward signal.

Two more issues. Eq. 10 is labeled KTO but is a REINFORCE-style update with a binary reward; it is not the KTO objective from the cited paper. Fix the name or the equation. And the reward is computed on the training set with no validation-based stopping, so the loop could overfit profiles to training idiosyncrasies. They report no error bars or significance tests, so we don't know whether the headline numbers are noise.

There is no released code or data, and scalability is asserted without measurements. The explainability evidence is a single case study—fine as an illustration, not as proof.

None of this is fatal to the idea. The framework is sensible, the writing is honest about limitations, and the empirical footprint is substantial. It deserves a serious referee, but the referee should ask for compute-matched ablations, error bars, a corrected KTO label, and a check for overfitting before saying the iteration mechanism works.

I wouldn't cite it yet, but I'd bring it to reading group; the compute-confound critique is exactly the kind of thing people need to see more often.

Referee Report

6 major / 5 minor

Summary. The paper introduces CIKT, a knowledge tracing framework built around two LLM components: an Analyst that generates textual student profiles from interaction histories, and a Predictor that conditions on these profiles to forecast next-response correctness. Training proceeds in four stages: distillation of the Analyst from teacher-generated curated profiles, batch profiling, Predictor training, and an iterative refinement loop in which the Analyst is updated using a binary reward based on the Predictor's training-set accuracy and the Predictor is retrained. Experiments on ASSIST2009, ASSIST2012, and Eedi report ACC and F1 gains over DLKT and LLM baselines, plus ablations and a qualitative case study.

Significance. The potential contribution is substantial if the method can be made sound: CIKT would be one of the first KT frameworks to combine transparent, dynamic student profiles with iterative self-refinement of an LLM-based profiler, and the reported gains of roughly 3 to 6 ACC points over strong DLKT baselines would be practically meaningful. The paper also honestly lists a limitation (no AUC). However, the current manuscript has several load-bearing technical gaps, detailed below, so the significance is conditional on addressing them.

major comments (6)

[Section 3.5, Eq. (9)] The reward is ill-defined because y_hat_{t+1} is a probability in Eq. (5) while y_{t+1} is binary; the equality y_hat_{t+1} = y_{t+1} almost never holds for non-thresholded outputs. Please specify thresholding or define a proper reward (e.g., whether the most likely class matches the ground truth, or a probability-scaled reward).
[Section 3.5, Eq. (10)] Equation (10) is not the KTO objective from Ethayarajh et al. (2024); it is a REINFORCE-style policy gradient with no KL regularization or reference policy, and it operates on a non-stationary training-set reward. Because the paper frames the contribution as KTO-guided optimization, this mislabeling is not merely cosmetic: the missing baseline and KL term are exactly what stabilize the cited algorithm. Please either adopt the actual KTO loss or relabel the method and add variance reduction / KL control.
[Section 4.3, Table 3] The 'w/o Iteration' ablation is not compute-matched. Full CIKT runs the initial single pass plus three iterations, each involving profile regeneration, an Analyst RL update, and a full Predictor retraining (Eq. 11), while the ablated baseline trains both components once. The +0.9 to 2.5 ACC differences could be explained by additional training steps or profile diversity rather than by the collaborative reward. Please add a compute-matched baseline (e.g., a single-pass model trained for the same total number of updates or epochs) to isolate the effect of iteration.
[Sections 3.5 and 4.4] The iterative loop has no validation-based stopping and no overfitting analysis. The reward in Eq. (9) is computed on training instances, and the Predictor is retrained using the same data loops, so the Analyst can overfit to training-set idiosyncrasies; Figure 2 reports scores at 0 to 3 rounds but does not show validation curves or clarify whether the test set was used for iteration selection. Please report per-round validation performance, select the iteration count on validation only, and show train/test gaps to establish generalization.
[Section 3.5 and Section 4.5 / Appendix C] The reward for updating the Analyst comes from the Predictor, which was trained on profiles from the same Analyst, making profile quality tautologically defined as training-set prediction success. The case study in Appendix C is anecdotal and not an independent evaluation. Please include an independent profile quality assessment (e.g., human evaluation or generalization to a downstream task) or validation-grounded selection to break the circularity.
[Abstract and Section 5 vs. Section 4] The claimed 'improved scalability' is asserted without evidence. No runtime, memory, or cost comparisons are given, despite the iterative loop requiring repeated LLM profile generation and fine-tuning on datasets up to 17.8M responses (Table 2). Please provide scalability measurements (e.g., wall-clock time per iteration, GPU memory) or temper the claim to what is actually demonstrated.

minor comments (5)

[Section 4.1.3] The phrase 'average of five times' should report standard deviations or confidence intervals; Table 1 and Table 3 currently present only point estimates.
[Eq. (11)] Equation (11) uses LCE, which was defined for token-level cross-entropy in Eq. (3); for binary outcome prediction the loss should be LPredict from Eq. (6). Please correct the notation.
[Table 1] The improvement row does not indicate the reference baseline; the percentages appear to be relative increases, but the exact baseline should be named (e.g., best baseline in each column) for each metric.
[Sections 3.3 and 3.4] Stage 2 says profiles are generated from the full student sequence Ss, while Stage 3 (Eq. 5) uses prefix-based profiles; clarify that profiles are generated from each prefix to avoid apparent label leakage.
[Section 3.2] The manual curation of teacher profiles is mentioned but no details are given on the curation criteria, the number or fraction of profiles rejected, or the prompts used with the teacher model; this is important for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: CIKT's central test-set accuracy claim is externally evaluated, and the iterative training loop is a self-referential mechanism but not a derivation that reduces to its inputs by construction.

full rationale

The paper's core claim is that CIKT improves held-out prediction accuracy and explainability. This is evaluated on external test splits (Section 4.1.3), not on the training-set reward used inside the loop. The Stage 4 update (Eqs. 8-10) does define a self-referential training signal: the Analyst is rewarded when the current Predictor is correct on the training set, and the Predictor is itself trained on Analyst-generated profiles. However, this is a standard RL/self-training-style optimization procedure, not a case where a prediction is equivalent to its input by construction. The reward is a function of ground-truth labels and the Predictor's output; the final accuracy numbers in Table 1 are measured on held-out data. The paper does not fit a parameter to a subset and then 'predict' that same quantity; the reported gains are not logically forced by the loss definitions. All citations, including the KTO reference, are to external prior work; there is no load-bearing self-citation chain or imported uniqueness theorem. The ablation comparisons may be confounded by unmatched compute or a training-set reward, but those are empirical-validity concerns, not circularity. No quoted step satisfies the standard of exhibiting a derivation that reduces by definition to its own inputs, so the appropriate finding is no significant circularity.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The framework rests on standard supervised losses plus several unverified design choices: the number of iteration rounds and sample size k are hand-picked from sensitivity curves, difficulty is defined as 1 minus pass rate, and the KTO objective is actually a REINFORCE term. The main domain assumptions are that binary (concept, difficulty, correctness) triples are sufficient and that GPT-4o profiles after manual curation are valid training targets. No new physical entities are introduced.

free parameters (6)

Number of iteration rounds = 3 (selected by sensitivity on ASSIST2012)
Figure 2(a) varies rounds from 0 to 3 and performance stabilizes around 3; this choice directly affects the reported gains.
Iteration sample size k = 1000
Selected on ASSIST2009 from {500, 1000, 1500, 2000}; authors adopt k=1000 as a trade-off between efficacy and compute.
Sequence length for segmentation = 50 interactions; sequences under 5 removed
Preprocessing choice in Section 4.1.1; it changes the interaction histories and the ACClen>15 metric.
Question difficulty definition = 1 - pass rate on training set
Ad hoc definition used to encode the target exercise; a different difficulty measure could change profiles and predictions.
LoRA rank and learning rates = rank 8; lr 5.0e-6 (Analyst), 1.0e-4 (Predictor); 10 epochs
Hyperparameters in Table 5; not swept or justified, and they likely affect the reported results.
Teacher model and curation threshold = GPT-4o; manual curation
Section 3.2: only selected high-quality profiles are retained; the selection criteria are subjective and unspecified.

assumptions (5)

standard math Cross-entropy and binary cross-entropy losses correctly train the Analyst and Predictor for their respective objectives.
Used in Eqs. 3 and 6 without proof; standard supervised learning losses.
domain assumption A student's knowledge state relevant to future performance is fully captured by the sequence of (knowledge concept, difficulty, binary correctness) triples, without question text.
Section 4.1.1 and the Limitations section: question stems are excluded due to context window constraints; this limits content-aware modeling.
ad hoc to paper The binary reward r_{t+1} in Eq. 9 is a sufficient training signal for improving profile quality.
Stage 4 assumes prediction success can be attributed to the generated profile and that maximizing log pi(pt|xt) with reward +/-1 improves the profile. No value model, baseline, or KL regularization is used, unlike actual KTO.
domain assumption GPT-4o-generated profiles, after manual curation, are high-quality supervised targets for the Analyst.
Section 3.2 Distillation relies on the teacher's profile quality and curation judgment; no inter-annotator agreement or quality metric is reported.
domain assumption The Predictor can learn from textual profiles paired with structured history to generalize to held-out students.
Section 3.4 assumes the profile representation is compatible with the predictor and provides useful signal; this is tested empirically but not proven.

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Cite this review

Pith. "Pith review of CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models." pith.science (2026). https://pith.science/paper/VEXEBRPR

@misc{pith2026250517705,
  author       = {Pith},
  title        = {Pith review of: CIKT: A Collaborative and Iterative Knowledge Tracing Framework with Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VEXEBRPR}},
  note         = {Machine review of arXiv:2505.17705}
}

read the original abstract

Knowledge Tracing (KT) aims to model a student's learning state over time and predict their future performance. However, traditional KT methods often face challenges in explainability, scalability, and effective modeling of complex knowledge dependencies. While Large Language Models (LLMs) present new avenues for KT, their direct application often struggles with generating structured, explainable student representations and lacks mechanisms for continuous, task-specific refinement. To address these gaps, we propose Collaborative Iterative Knowledge Tracing (CIKT), a framework that harnesses LLMs to enhance both prediction accuracy and explainability. CIKT employs a dual-component architecture: an Analyst generates dynamic, explainable user profiles from student historical responses, and a Predictor utilizes these profiles to forecast future performance. The core of CIKT is a synergistic optimization loop. In this loop, the Analyst is iteratively refined based on the predictive accuracy of the Predictor, which conditions on the generated profiles, and the Predictor is subsequently retrained using these enhanced profiles. Evaluated on multiple educational datasets, CIKT demonstrates significant improvements in prediction accuracy, offers enhanced explainability through its dynamically updated user profiles, and exhibits improved scalability. Our work presents a robust and explainable solution for advancing knowledge tracing systems, effectively bridging the gap between predictive performance and model transparency.

Figures

Figures reproduced from arXiv: 2505.17705 by the authors.

**Figure 1.** The CIKT framework, illustrating the collaborative four-stage process involving the Analyst and Predictor: distillation, profiling, reasoning, and iteration. the curated teacher profiles: LDistill(θA) = X s∈Dtrain LCE(p ∗ s,teacher, ps,analyst) (3) This supervised distillation phase equips the Analyst with a robust initial model for generating meaningful user profiles. 3.3 Stage 2: Profiling Following the initial … view at source ↗

**Figure 2.** Effect of the number of interaction rounds and sample size per iteration on the performance of CIKT. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

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- The difficulty levels of these questions ranged from 0.47 to 0.64, indicating a moderate level of challenge

Making a Table from an Equation: - The student has encountered three questions related to this topic, all answered incorrectly. - The difficulty levels of these questions ranged from 0.47 to 0.64, indicating a moderate level of challenge. - This suggests that the student may not yet have a solid understanding of this concept or is struggling with the appl...

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- The difficulty levels ranged from 0.13 to 0.39

Equivalent Fractions: - The student has answered five questions related to this topic, with a mix of correct and incorrect responses. - The difficulty levels ranged from 0.13 to 0.39. - The student demonstrated a good grasp of easier questions (e.g., those with a lower difficulty, such as 0.13 and 0.26), but struggled with more challenging ones (e.g., 0.3...

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Conversion of Fraction Decimals Percents

Conversion of Fraction Decimals Percents: - The student has attempted two questions related to this topic. - The first question was answered correctly, and the second was answered incorrectly. - Both questions had a difficulty level of 0.17. - The student shows potential understanding of this topic but may need further practice or reinforcement to solidif...

work page

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In Artificial intelligence in education, pages 531–538

Performance factors analysis–a new alternative to knowledge tracing. In Artificial intelligence in education, pages 531–538. Ios Press. Chris Piech, Jonathan Bassen, Jonathan Huang, Surya Ganguli, Mehran Sahami, Leonidas J Guibas, and Jascha Sohl-Dickstein. 2015. Deep knowledge trac- ing. Advances in neural information processing sys- tems, 28. Edward O P...

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Making a Table from an Equation

Recommendations: - The student should focus on reinforcing their understand- ing of "Making a Table from an Equation" and "Equivalent Fractions" through targeted practice and review of fundamen- tal concepts. - For "Conversion of Fraction Decimals Percents," the stu- dent should review the basic principles and practice a variety of problems to build confi...

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Making a Table from an Equation,

Making a Table from an Equation: - The student initially struggled with questions related to "Making a Table from an Equation," as evidenced by incorrect responses to questions with moderate difficulty levels (0.47, 0.64, and 0.61). This suggests a gap in understanding or ap- plication of this concept

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Equiv- alent Fractions,

Equivalent Fractions: - The student demonstrated initial proficiency with "Equiv- alent Fractions," correctly answering questions with low to moderate difficulty levels (0.13 and 0.26). However, there was a decline in performance as the difficulty increased, with incorrect responses to questions at 0.37, 0.32, and 0.39. This indicates that while the stude...

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Conversion of Fraction Decimals Percents

Conversion of Fraction Decimals Percents: - The student showed mixed results with "Conversion of Fraction Decimals Percents." They correctly answered a ques- tion with a low difficulty level (0.17), indicating a basic under- standing, but then responded incorrectly to another question of the same difficulty level (0.17). This inconsistency suggests that t...

work page