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Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM

T0 review · 2 major / 2 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read A knowledge-grounded LLM framework delivers just-in-time feedback that improves student performance by over 80 percent in a large course.

desk verdict The 80% performance claim rests on an uncontrolled semester comparison with no details on measurement or cohort adjustments. read the letter →

arxiv 2605.26405 v1 pith:Z6XXMM7Q submitted 2026-05-26 cs.CL

Younghun Lee , Amir Bralin , Nobel Sanjay Rebello , Dan Goldwasser This is my paper · ORCID
classification cs.CL
keywords just-in-timefeedbackadaptiveLLMineducationstudentmisconceptionsknowledgegroundinglearningtrajectorieseducationalintervention
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

The paper develops a framework that supplies adaptive feedback by grounding large language models in domain-specific expert knowledge. Students submit written strategy essays that reveal their reasoning; the system identifies likely error types from that content and returns non-intrusive clarifications at the moment they are needed. When run in a university course with more than 1000 students, the intervention produced performance gains exceeding 80 percent relative to earlier semesters. Analysis of successive student exchanges shows that repeated interactions help replace misconceptions with accurate understanding. A reader would care because the work demonstrates a concrete method for scaling personalized guidance without requiring constant human oversight.

What carries the argument

The knowledge-grounded LLM that analyzes student strategy essays to detect error types and generate targeted clarifying feedback.

What would settle it

A controlled within-semester experiment that assigns the feedback to one group of students and withholds it from an otherwise identical group, then finds no performance difference, would falsify the causal claim.

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Extended reading notes

Core claim

The authors claim that grounding LLMs with domain-specific expert knowledge enables collection of strategy essays, detection of error types in the reasoning content, and delivery of non-intrusive feedback; this process raised student performance by over 80 percent compared with previous semesters in a course enrolling more than 1000 students, and iterative conversations with the model shift misconceptions toward correct understanding.

Load-bearing premise

The observed performance increase is caused by the LLM feedback rather than uncontrolled differences in instruction, student population, or assessment between semesters.

Editorial extensions

If this is right

  • Student performance on course assessments rises substantially compared with prior semesters.
  • Learning trajectories recorded in successive exchanges demonstrate movement from misconceptions to correct concepts.
  • The approach operates at the scale of courses enrolling more than 1000 students.
  • Non-intrusive feedback clarifies missing or incorrect concepts without disrupting the learning flow.

Reading between the lines

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

  • The same grounding technique could be tested in other subjects if expert error patterns can be expressed as analyzable rules.
  • Long-term retention of the corrected concepts after the course ends is not measured in the reported data.
  • Reducing manual grading demands might allow instructors to allocate time to other instructional activities.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The manuscript presents a framework for just-in-time adaptive feedback in education that grounds LLMs with domain-specific expert knowledge. It collects students' written strategy essays, identifies potential error types from the reasoning content, and generates non-intrusive feedback to address misconceptions. The framework was deployed in a large university course (N > 1000), where it is claimed to have improved student performance by over 80% relative to previous semesters; learning trajectories from iterative LLM conversations are analyzed to illustrate shifts from misconceptions to correct understanding.

Significance. If the performance gains can be causally attributed to the intervention, the work would demonstrate a practical method for scaling personalized feedback in large courses using student-generated artifacts to constrain LLM outputs. The deployment scale (N > 1000) and the focus on iterative misconception correction provide a useful empirical case study for AI in education. The grounding approach via strategy essays is a concrete strength that could be extended to other domains.

major comments (2)
  1. [Abstract] Abstract: The headline claim that the framework 'improved student performance by over 80% compared to previous semesters' is presented without any description of the performance metric, how the 80% figure was computed, whether assessments or grading standards were held constant across semesters, or any adjustment for cohort, instructional, or selection differences. This uncontrolled semester-to-semester comparison is the central empirical result and cannot support the attribution of gains to the LLM feedback without additional controls or statistical detail.
  2. [Learning trajectories analysis] Learning trajectories section: The analysis shows individual shifts in misconceptions through iterative conversations but supplies no aggregate statistical tests, control conditions, or comparison to non-intervention trajectories that would isolate the effect of the feedback system from other semester-level factors.
minor comments (2)
  1. The methods description of how error types are extracted from strategy essays and mapped to feedback could be expanded with pseudocode or a concrete example to improve reproducibility.
  2. No discussion of potential LLM hallucination risks or human oversight mechanisms in the feedback generation pipeline is provided.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments on the empirical presentation. We address each major comment below with plans for revision where feasible.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim that the framework 'improved student performance by over 80% compared to previous semesters' is presented without any description of the performance metric, how the 80% figure was computed, whether assessments or grading standards were held constant across semesters, or any adjustment for cohort, instructional, or selection differences. This uncontrolled semester-to-semester comparison is the central empirical result and cannot support the attribution of gains to the LLM feedback without additional controls or statistical detail.

    Authors: We agree the abstract requires additional detail for transparency. In revision we will specify the performance metric (mean score on the primary course assessment), describe the relative improvement calculation, confirm that the assessment rubric and course structure were unchanged, and explicitly note the observational nature of the semester comparison along with potential unmeasured cohort or instructional differences. This will qualify the claim appropriately while retaining the reported observation. revision: yes

  2. Referee: [Learning trajectories analysis] Learning trajectories section: The analysis shows individual shifts in misconceptions through iterative conversations but supplies no aggregate statistical tests, control conditions, or comparison to non-intervention trajectories that would isolate the effect of the feedback system from other semester-level factors.

    Authors: The trajectories section is designed to illustrate the mechanism via representative examples of misconception correction. We will revise to include aggregate descriptive statistics across the student sample (e.g., proportion of trajectories showing correction and average iterations to correction). We acknowledge that no control condition was present in the live-course deployment, which precludes isolating the intervention effect, and will add this limitation to the discussion. revision: partial

standing simulated objections not resolved
  • Absence of a control group or matched non-intervention cohort, preventing causal attribution of performance gains to the LLM feedback system.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical deployment with no derivation chain

full rationale

The paper reports an empirical deployment of an LLM feedback framework in a university course (N>1000) and claims an 80% performance improvement relative to prior semesters, along with qualitative analysis of learning trajectories. No equations, fitted parameters, predictions, or mathematical derivations are described anywhere in the provided text. The central result is an observational outcome from real-world use rather than a self-referential model or ansatz that reduces to its inputs by construction. Self-citations, if present, are not load-bearing for any derivation. This is a standard non-circular empirical study.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

The paper rests on the domain assumption that LLMs can reliably map student reasoning text to expert-defined error categories and that the resulting feedback produces learning gains independent of other course changes.

assumptions (1)
  • domain assumption LLMs grounded with domain-specific expert knowledge can accurately identify missing or incorrect concepts in student strategy essays.
    Invoked in the description of the framework that analyzes reasoning logic and delivers feedback.

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Cite this review

Pith. "Pith review of Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM." pith.science (2026). https://pith.science/paper/Z6XXMM7Q

@misc{pith2026260526405,
  author       = {Pith},
  title        = {Pith review of: Towards Just-in-Time Adaptive Feedback: Enhancing Student Learning via Knowledge-Grounded LLM},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z6XXMM7Q}},
  note         = {Machine review of arXiv:2605.26405}
}
read the original abstract

Educational interventions are effective tools for enhancing student learning. While Large Language Models (LLMs) allow for generating adaptive feedback at scale, current studies lack clear methodologies for providing Just-in-Time (JiT) feedback in authentic instructional settings. In this paper, we present a framework that provides adaptive feedback by grounding LLMs with domain-specific expert knowledge. Our approach collects written reasoning logic (strategy essays) from students, analyzes potential error types based on the content of that reasoning, and delivers non-intrusive feedback designed to clarify missing or incorrect concepts. We deploy this framework in a large-scale university course (N > 1000), where it improved student performance by over 80% compared to previous semesters. Lastly, we validate the framework's pedagogical utility by analyzing the learning trajectories; we demonstrate how iterative conversations with LLM facilitate shifting one's misconception to correct understanding.

Figures

Figures reproduced from arXiv: 2605.26405 by the authors.

Figure 1
Figure 1. Overall framework of our Just-in-Time adaptive feedback LLM. In the first phase, we obtain domain knowledge from human experts regarding the appropriate feedback for students’ strategy essays and the types of errors they made. At deployment, we ground the LLM with this knowledge and provide adaptive feedback to the students based on their written strategy essays. answer, the feedback directs the student’s atten￾tion… view at source ↗
Figure 2
Figure 2. Survey results regarding preference be￾tween novice and advanced feedback in two quizzes. Regardless of their level of knowledge, students prefer LLM feedback that is targeted to the novice group. students with different knowledge levels show a contrasting preference for the complexity of LLM feedback. We provide post-hoc LLM feedback to the stu￾dents for two quizzes in the Fall 2025 semester. Students are asked to … view at source ↗
Figure 4
Figure 4. Example of a strategy essay and annotated [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (9 more)
Figure 5
Figure 5. Figure 5: Student’s performance in the same quiz question over four semesters. When the students solved the [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Survey results regarding why LLM’s feedback [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: shows an example of conversation be￾tween a student and LLM. As the student receives LLM’s feedback that tackles missing or incorrect points in the strategy essay, they add these compo￾nents to their strategy essay. By continuing conver￾sations, students incrementally …
Figure 8
Figure 8. Figure 8: Learning Trajectories for students starting with Direction error. Each trace represents a sequence of essay error types as predicted by the feedback-LLM at each conversation turn. Terminal markers are labeled with the specific Path Sequence and the Path Outcome. For in…
Figure 9
Figure 9. Figure 9: Correlation between word count differ￾ences and essay correctness. We compute the corre￾lation between the word count differences in strategy essays between each conversation utterances and the correctness of essay. When the students make more changes to their essays, …
Figure 10
Figure 10. Figure 10: Survey webpage. Students answer two ques [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: The full system prompt used for generating post-hoc feedback [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]
Figure 12
Figure 12. Figure 12: The full system prompt used for generating JiT feedback [PITH_FULL_IMAGE:figures/full_fig_p014_12.png]
Figure 13
Figure 13. Figure 13: Overall flow of JiT feedback. The feedback snippet is placed below the quiz question. Students type in [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Using LLMs to Detect Growth in Computational Thinking in Introductory Physics

    physics.ed-ph 2026-08 conditional novelty 4.0 of 10

    An LLM scored students' written computational thinking responses in an introductory physics course with human-level agreement on well-defined practices and reproduced pre-post growth trends at scale.

Reference graph

Works this paper leans on

12 extracted references · 1 canonical work pages · cited by 1 Pith paper

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    Wei Dai, Jionghao Lin, Hua Jin, Tongguang Li, Yi- Shan Tsai, Dragan Gaševi ´c, and Guanliang Chen

    Categorization and representation of physics problems by experts and novices.Cognitive science, 5(2):121–152. Wei Dai, Jionghao Lin, Hua Jin, Tongguang Li, Yi- Shan Tsai, Dragan Gaševi ´c, and Guanliang Chen

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    In2023 IEEE international conference on advanced learning tech- nologies (ICALT), pages 323–325

    Can large language models provide feedback to students? a case study on chatgpt. In2023 IEEE international conference on advanced learning tech- nologies (ICALT), pages 323–325. IEEE. Jennifer L Docktor, Natalie E Strand, José P Mestre, and Brian H Ross. 2010. A conceptual approach to physics problem solving. InAIP Conference Pro- ceedings, volume 1289, p...

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    In Physics Education Research Conference 2025, PER Conference, pages 184–189, Washington, DC

    Analyzing undergraduate problem-solving in physics through interaction with an ai chatbot. In Physics Education Research Conference 2025, PER Conference, pages 184–189, Washington, DC. John Hattie and Shirley Clarke. 2018.Visible learning: feedback. Routledge. John Hattie and Helen Timperley. 2007. The power of feedback.Review of educational research, 77(...

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    In International conference of the Australian Associa- tion for Research in Education, pages 1–11

    Written feedback doesn’t make sense’: En- hancing assessment feedback using technologies. In International conference of the Australian Associa- tion for Research in Education, pages 1–11. Chandralekha Singh and David Rosengrant. 2003. Multiple-choice test of energy and momentum con- cepts.Am. J. Phys., 71(6):607–617. Karan Taneja, Pratyusha Maiti, Sandee...

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    Thomas Wolsey

    The power of feedback revisited: A meta- analysis of educational feedback research.Frontiers in psychology, 10:487662. Thomas Wolsey. 2008. Efficacy of instructor feedback on written work in an online program. InInterna- tional Journal on E-learning, volume 7, pages 311–

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  6. [6]

    Helps me better under- stand the concept

    Association for the Advancement of Computing in Education (AACE). Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, and 1 others. 2025. Simulating classroom education with llm-empowered agents. InProceedings of the 2025 Conference of the Nations of the Americas Chapter of th...

    work page arXiv 2025

  7. [7]

    The Quiz Problem: The specific question the student attempted to solve

  8. [8]

    Student’s Strategy Essay: The student’s written explanation of how they approached the problem

Show all 12 references
  1. [9]

    NOVICE”: – Tone: Highly encouraging, supportive, and patient. Use the “Sandwich Method

    Expert’s Strategy Essay: A sample strategy essay written by an expert, that can serve as a rubrics for evaluating student’s essay Instructions for Adaptation: • If Knowledge Level is “NOVICE”: – Tone: Highly encouraging, supportive, and patient. Use the “Sandwich Method” (Prai...

  2. [10]

    Look for specific keywords or logical steps

    Analyze the student’s essay. Look for specific keywords or logical steps

  3. [11]

    Identify if they are applying correct principles/concepts or falling into misconceptions

  4. [12]

    Student Essay

    Output your final reasoning and label as strictly valid JSON. Label Categories & Definitions • ‘correct’: The reasoning and final answer are physically sound. • ‘direction’: The student makes an error where their answer is in the exact opposite direction of the correct vector ...

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