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arxiv: 2604.07469 · v2 · submitted 2026-04-08 · 💻 cs.HC

Assessing the Impact and Underlying Pathways of Sequenced AI feedback on Student Learning

Jie Cao , Chloe Qianhui Zhao , Christian Schunn , Elizabeth A.McLaughlin , Jionghao Lin , Kenneth R. Koedinger This is my paper

Pith reviewed 2026-05-10 16:53 UTC · model grok-4.3

classification 💻 cs.HC
keywords AI feedbacksequenced scaffoldinglearning outcomesstudent engagementmediation analysisformative feedbackeducational technologycognitive effort
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The pith

Sequenced AI feedback leads to significantly poorer learning outcomes than direct feedback despite higher engagement.

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

The paper tests whether AI feedback that sequences encouragement and hints before the answer improves learning more than direct explanations. In a randomized study of 199 participants, sequenced feedback raised behavioral engagement and positive perceptions of support but also increased mental effort and the number of repeated submissions. Mediation analysis revealed that any affective benefits were fully offset by behavioral costs, yielding worse performance overall. A reader would care because generative AI is widely promoted for tutoring, yet this shows its layered scaffolding can backfire on actual gains.

Core claim

Sequenced AI feedback, which provides encouragement and hints incrementally before revealing the correct answer, produces significantly poorer learning outcomes than direct non-sequenced feedback. It elicits slightly higher behavioral engagement and is perceived as more encouraging and supportive of independence, yet it induces higher mental effort. The positive affective pathway from perceived encouragement is completely counteracted by a negative behavioral pathway tied to tasks requiring three or more submissions, while the cognitive pathway remains non-significant.

What carries the argument

Mediation analyses tracing affective, cognitive, and behavioral pathways from feedback sequencing to learning performance.

If this is right

  • Direct feedback should be favored over sequenced scaffolding when the goal is maximizing performance gains.
  • Increased submission counts signal inefficiency rather than productive engagement.
  • Positive student perceptions of encouragement do not reliably predict better outcomes.
  • Cognitive load from layered hints can negate motivational benefits in automated systems.
  • Evaluating AI feedback requires joint analysis of performance, effort, and perceptions.

Where Pith is reading between the lines

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

  • Better prompt design or model fine-tuning for educational scaffolding might reduce the observed costs.
  • The trade-off may vary across subject domains that differ in conceptual versus procedural demands.
  • Hybrid feedback that starts direct and adds optional layers could combine strengths of both approaches.
  • Large-scale deployment of AI tutors should prioritize efficiency metrics over engagement scores alone.

Load-bearing premise

The content and quality of the AI-generated feedback remained equivalent across conditions, with sequencing as the sole difference.

What would settle it

A replication in which independent experts rate the feedback content as equivalent in accuracy and relevance, then measure learning gains on a controlled post-test.

Figures

Figures reproduced from arXiv: 2604.07469 by Chloe Qianhui Zhao, Christian Schunn, Elizabeth A.McLaughlin, Jie Cao, Jionghao Lin, Kenneth R. Koedinger.

Figure 1
Figure 1. Figure 1: The design of the layered feedback compared with the non-layered feedback [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Feedback generation process [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Generated feedback example in experimental and control groups [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The research procedure 14 [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Overall learning time of both groups 16 [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of total submissions and the number of tasks with multiple attempts [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Mediation model of how layered feedback influence learning performance com [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗
read the original abstract

Feedback is essential for learning, but its effectiveness relies heavily on how well it engages students in the educational process. Generative AI offers novel opportunities to efficiently produce rich, formative feedback, ranging from direct explanations to incrementally sequenced scaffolding designed to promote learner autonomy. Despite these capabilities, it is still unclear whether sequenced (layered) AI feedback -- which provides encouragement and hints before revealing the correct answer -- genuinely enhances engagement and learning outcomes. To investigate this, we randomly assigned 199 participants to receive either sequenced or non-sequenced AI-generated feedback. We evaluated its impact on learning performance, cognitive and behavioral engagement, and affective perceptions to understand how these factors mediate overall learning outcomes. Results show that sequenced feedback elicited slightly higher behavioral engagement and, as anticipated, was perceived as more encouraging and supportive of student independence. Concurrently, however, it induced a higher level of mental effort. Mediation analyses identified a positive affective pathway driven by perceived encouragement, which was completely counteracted by a negative behavioral pathway associated with the average number of tasks requiring three or more submissions; the cognitive pathway (mental effort) remained non-significant. Overall, sequenced feedback led to significantly poorer learning outcomes when compared to direct, non-sequenced feedback. These findings highlight a crucial trade-off: although sequenced AI scaffolding boosts engagement and positive user perceptions, it can have a detrimental effect on actual learning performance. By integrating analyses of outcomes, perceptions, and underlying mechanisms, this study provides nuanced insights for designing automated, AI-driven feedback systems.

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 reports a randomized experiment assigning 199 participants to either sequenced (layered hints and encouragement before the answer) or direct non-sequenced AI-generated feedback. It finds that sequenced feedback produced higher behavioral engagement and more positive affective perceptions but also higher mental effort, with mediation analyses showing an affective pathway offset by a behavioral pathway (number of tasks needing three or more submissions), ultimately yielding significantly poorer learning outcomes for the sequenced condition.

Significance. If the result holds after verification of content equivalence, the work contributes to HCI and educational technology by identifying a trade-off in AI feedback design: scaffolding can boost engagement and autonomy perceptions yet impair performance. Strengths include random assignment supporting causal interpretation and the use of mediation to unpack affective, behavioral, and cognitive pathways. These elements provide mechanistic insight beyond simple outcome comparisons.

major comments (3)
  1. [Abstract] Abstract: The headline claim that sequenced feedback produced significantly poorer learning outcomes is presented without effect sizes, confidence intervals, or any statistical test details. This omission prevents evaluation of the magnitude and precision of the reported difference, which is central to the paper's contribution.
  2. [Abstract and Methods] Abstract and Methods: The interpretation that poorer outcomes are attributable to sequencing rather than content differences rests on the unverified assumption that AI-generated feedback was equivalent in quality, accuracy, relevance, and length across conditions. No details are supplied on prompt engineering, base content generation, or post-generation matching procedures; any systematic disparity in hint quality could fully explain the performance gap.
  3. [Results] Results: The mediation analysis asserts that the positive affective pathway is 'completely counteracted' by the negative behavioral pathway, yet no path coefficients, standard errors, or variance-explained statistics are referenced. Without these quantities it is impossible to confirm the claimed complete offset or to assess the non-significant cognitive pathway.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'slightly higher behavioral engagement' is used without specifying the exact behavioral measure or the associated statistical test and p-value.
  2. [General] General: A post-hoc power analysis or a priori sample-size justification for N=199 would strengthen the methodological reporting and help readers interpret the non-significant cognitive pathway.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which have helped us identify areas for improvement in our manuscript. We address each major comment below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline claim that sequenced feedback produced significantly poorer learning outcomes is presented without effect sizes, confidence intervals, or any statistical test details. This omission prevents evaluation of the magnitude and precision of the reported difference, which is central to the paper's contribution.

    Authors: We agree with this observation. The revised abstract will include the effect size (Cohen's d), 95% confidence interval, and the p-value for the primary learning outcome comparison to provide a complete statistical picture. revision: yes

  2. Referee: [Abstract and Methods] Abstract and Methods: The interpretation that poorer outcomes are attributable to sequencing rather than content differences rests on the unverified assumption that AI-generated feedback was equivalent in quality, accuracy, relevance, and length across conditions. No details are supplied on prompt engineering, base content generation, or post-generation matching procedures; any systematic disparity in hint quality could fully explain the performance gap.

    Authors: We acknowledge that the current manuscript does not provide sufficient details on the feedback generation process. In the revision, we will add a dedicated subsection in the Methods section detailing the prompt engineering strategies, the base content used for generation, and the post-generation review and matching procedures employed to ensure equivalence in quality, accuracy, relevance, and length between the sequenced and direct feedback conditions. revision: yes

  3. Referee: [Results] Results: The mediation analysis asserts that the positive affective pathway is 'completely counteracted' by the negative behavioral pathway, yet no path coefficients, standard errors, or variance-explained statistics are referenced. Without these quantities it is impossible to confirm the claimed complete offset or to assess the non-significant cognitive pathway.

    Authors: We concur that additional statistical details are necessary for transparency. The revised manuscript will include a table presenting the path coefficients, standard errors, p-values, and variance explained (R²) for each pathway in the mediation model, allowing readers to verify the complete offset of the affective pathway by the behavioral pathway and the non-significance of the cognitive pathway. revision: yes

Circularity Check

0 steps flagged

Empirical RCT with direct measurement; no derivations or self-referential reductions

full rationale

The paper reports a randomized controlled trial with 199 participants assigned to sequenced vs. non-sequenced AI feedback conditions. Outcomes (learning performance, engagement, perceptions) are measured directly from participant data. Mediation analyses decompose observed paths but do not redefine variables in terms of themselves or rename fitted parameters as independent predictions. No equations, uniqueness theorems, or ansatzes are invoked that reduce results to inputs by construction. The central claim follows from the experimental contrast and statistical tests on collected data, remaining self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard assumptions of randomized experiments and statistical mediation without introducing new free parameters, axioms beyond domain norms, or invented entities.

axioms (2)
  • domain assumption Randomized assignment balances unobserved confounders between conditions
    Invoked by the statement that participants were randomly assigned to conditions.
  • domain assumption Mediation analysis can validly decompose total effects into specified pathways
    Used to identify the positive affective pathway and negative behavioral pathway.

pith-pipeline@v0.9.0 · 5590 in / 1289 out tokens · 66894 ms · 2026-05-10T16:53:24.608528+00:00 · methodology

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Reference graph

Works this paper leans on

7 extracted references · 7 canonical work pages

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    URL:https://www.learntechlib.org/primary/p/24328/. Baker, R.S., Corbett, A.T., Koedinger, K.R., 2004. Detecting student misuse of intelligent tutoring systems, in: Lester, J.C., Vicari, R.M., Paraguaçu, F. (Eds.), Intelligent Tutoring Systems, Springer Berlin Heidel- berg, Berlin, Heidelberg. pp. 531–540. URL:https://doi.org/10.1007/ 978-3-540-30139-4_50....

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    URL:https://doi.org/10.1145/3657604.3662040. Graham, S., Hebert, M., Harris, K.R., 2015. Formative assessment and writing: A meta-analysis. The elementary school journal 115, 523–547. URL:https://www.jstor.org/stable/10.1086/681947. Hao, Z., Cao, J., Li, R., Yu, J., Liu, Z., Zhang, Y., 2026. Mapping student- AI interaction dynamics in multi-agent learning...

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    Try asking whether this shows appearance or relationships

    Prompt for elaborated feedback with correct answer 1.1 Version for multiple-choice questions You are tasked with generating clear, effective feedback for a student's multiple- choice answer and then formatting it into a structured JSON output. Complete both tasks in sequence. Task 1: Generate Feedback Generate feedback that meets all five criteria: Requir...

    work page 1914

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    the map of Europe in 1914,

    Prompt for Learner-centered feedback 2.1 Version for multiple-choice questions Pr ompt_Appendix 7 You are tasked with generating clear, effective feedback for a student's multiple- choice answer and then formatting it into a structured JSON output. Complete both tasks in sequence. Task 1: Generate Feedback Generate feedback that meets all seven criteria: ...

    work page 1914