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The Agency Gap in AI-Supported Writing: How Reactive and Proactive Agent Designs Shape Multimodal Reasoning

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper argues that a learner's generative-AI literacy translates into independent writing quality only when the AI agent leaves initiative to the student: a randomised study of 79 medical and nursing students found literacy predicted…

desk verdict Useful empirical study with a real moderation claim that the authors never actually test; the cautious abstract is right and the conclusion overstates. read the letter →

arxiv 2507.04398 v3 pith:CFFGOYEJ submitted 2025-07-06 cs.HC cs.CY

Yueqiao Jin , Kaixun Yang , Roberto Martinez-Maldonado , Dragan Gašević , Lixiang Yan This is my paper
classification cs.HCcs.CY
keywords generativeAIliteracyacademicwritingchatbotinteractiondesignagencygapmultimodalreasoningeducationalscaffoldinghuman-AIcollaborationlearninganalytics
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

Generative AI tools are entering academic writing, but it is unclear whether they build durable skill or only prop up performance while available. This paper argues that the answer depends on interaction design: it introduces the 'agency gap' — the degree to which a learner's generative-AI literacy predicts independent writing when the learner must self-initiate engagement. In a randomised study with 79 medical and nursing students, literacy predicted writing quality after AI support was removed only in the reactive-chatbot condition; under a proactive agent that posed sequenced questions, low- and high-literacy students performed alike. The authors conclude that cultivating GenAI literacy and designing proactive scaffolds are two complementary levers for equitable AI-supported learning.

What carries the argument

The paper's central object is the 'agency gap' — the extent to which generative-AI literacy predicts independent academic writing when learners must self-initiate and regulate their engagement with AI. It is operationalised by an experiment contrasting two chatbot designs built on a shared agentic architecture, namely retrieval-augmented generation over a knowledge base of the healthcare-simulation data synthesised by a multimodal large language model: a reactive agent that answers only student-initiated queries, and a proactive agent that initiates sequenced domain-expert questions and iterative task- and process-level feedback. The instruments carrying the argument are the 20-item Generative AI Literacy Assessment Test (GLAT) with a correction-for-guessing score, the 12-item mini-VLAT visualisation-literacy test as the domain-knowledge control, a five-criterion writing rubric scored by two blinded coders, and a two-phase design in which each student writes once with AI support and once immediately after it is withdrawn.

What would settle it

Run an adequately powered replication that tests the literacy-by-condition interaction directly: if the GLAT-by-chatbot-condition interaction on the post-support composite writing score is not significant while literacy predicts writing in both arms, the agency-gap claim fails, because the paper's own design cannot distinguish equalisation from an underpowered null.

Watch

Extended reading notes

Core claim

Seventy-nine medical and nursing students wrote two multimodal analytical essays interpreting healthcare-simulation visualisations, one with chatbot support and one immediately after support was withdrawn. When the chatbot was reactive — responding only to student-initiated queries — students' generative-AI literacy, measured by the guessing-corrected 20-item GLAT, significantly predicted independent writing quality on insightfulness, visual-data integration, organisation, linguistic quality, critical thinking, and composite score. When the chatbot proactively scaffolded the task with sequenced domain-expert questions and feedback, no such association appeared. Ordinal regression confirmed that literacy predicted independent performance on visual-data integration, critical thinking, and overall quality even after controlling for visualisation literacy, chatbot type, and AI-supported performance; mediation analysis found no indirect path through in-task performance, which the authors read as evidence that GenAI literacy is a direct, task-general competence, not a proxy for domain knowledge. The authors frame the condition-specific pattern as the 'agency gap' and are explicit that the proactive-condition result is consistent with smaller literacy-related differences but does not establish a compensatory causal effect.

Load-bearing premise

The claim that proactive scaffolding equalises outcomes rests on a null result in a 40-participant proactive arm split into subgroups of 17 and 23, and the paper never reports a literacy-by-condition interaction test, so the absence of an effect does not by itself prove that proactive design removes the literacy effect.

Editorial extensions

If this is right

  • If interaction design determines when GenAI literacy matters, then simply equipping students with a chatbot is not enough: the same underlying competence produces different outcomes depending on whether the agent waits for the learner or leads the learner.
  • Proactive scaffolding can act as an equity lever, letting lower-literacy students reach writing quality comparable to higher-literacy peers while support is present, which shrinks the practical significance of literacy gaps during supported work.
  • Because literacy predicts independent writing without operating through AI-supported performance, educators can treat GenAI literacy as a transferable strategic competence worth teaching in its own right, not merely as a skill for using the tool.
  • Instructional sequencing follows: phase AI support so that proactive scaffolds equalise access early and are then faded, forcing students to exercise the self-initiation and self-regulation that make GenAI literacy visible.
  • The authors are explicit that the proactive-condition pattern is consistent with smaller literacy-related differences but does not establish a compensatory causal effect, so claims of equalisation should be read as suggestive rather than proven.

Reading between the lines

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

  • An extension the paper leaves implicit: the agency-gap pattern should replicate outside writing — in programming, data interpretation, or problem-solving tasks — whenever the tool's initiative structure determines whether learners must self-regulate, which yields a concrete cross-domain prediction.
  • If GLAT's predictive power is direct rather than mediated, short literacy batteries could serve as low-cost screening instruments for who needs stronger scaffolds, a practical use the authors do not discuss.
  • A testable longitudinal prediction follows from the authors' own observation that proactive scaffolding may deprive students of prompting practice: literacy growth should be slower in the proactive arm than in the reactive arm over repeated sessions.
  • The scaffolded questioning sequence in the appendix implies a mechanism testable from interaction logs: if low-literacy students who skip or rush the proactive questions still match high-literacy peers, the equalisation is not explained by engagement with the scaffolded dialogue.
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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

3 major / 6 minor

Summary. The manuscript reports a randomized experiment with 79 medical and nursing students who completed multimodal analytical writing tasks with either a reactive GenAI chatbot (responding only when prompted) or a proactive GenAI chatbot (providing sequenced questions and feedback), followed by a parallel writing task without AI support. Using the authors' Generative AI Literacy Assessment Test (GLAT) and the mini-VLAT as a domain-knowledge control, the paper reports that GenAI literacy was significantly correlated with independent writing performance in the reactive condition but not in the proactive condition; that ordinal regression controlling for VLAT, chatbot type, and AI-assisted scores found GLAT predictive of visual data integration, critical thinking, and composite score at the AI-removal stage; and that bootstrapped mediation found no significant indirect effect via AI-supported performance in either condition. The authors introduce the 'agency gap' to describe the condition-dependent relevance of GenAI literacy and argue that proactive scaffolding equalises outcomes across literacy levels. The central claim is that interaction design determines whether GenAI literacy translates into independent performance after support is removed.

Significance. If the central moderation claim were fully supported, the paper would make a valuable contribution to human-AI interaction and learning-analytics design: it would identify a concrete mechanism (proactive scaffolding) for reducing literacy-based disparities in AI-supported writing, and it would position GenAI literacy as a transferable, task-general competence rather than a proxy for domain knowledge. The study has genuine strengths: a randomized between-subjects design; a task with multimodal visual data; validated instruments with reported psychometrics (GLAT Cronbach α = 0.76; mini-VLAT previously validated); high inter-rater reliability on all rubric dimensions (rs ≥ 0.75, p < .001); preregistered-style RQs; and a transparent reporting of effect sizes and confidence intervals in the mediation tables. The authors also carefully acknowledge in the abstract and limitations that the literacy-by-design interactions were not significant and that residual confounding cannot be ruled out.

major comments (3)
  1. [§4.3, Eq. (2); §5.2; §6] The central differential claim—that GenAI literacy predicts independent writing only in the reactive condition—is not directly tested. Eq. (2) includes GLAT, VLAT, chatbot type, and AI-assisted score as main effects only, with no GLAT×chatbot interaction term. A significant within-condition correlation (reactive r_s = .44) and a non-significant within-condition correlation (proactive) do not constitute a significant difference between conditions. The abstract itself concedes that 'literacy-by-design interactions were not significant,' but Sections 5.2 and 6 repeatedly state that proactive scaffolding 'equalised outcomes.' To support the equalisation claim, the paper should report a formal interaction test (e.g., GLAT×condition in the ordinal model, or a Fisher r-to-z comparison of the two correlations) and should interpret the results in light of that test's power.
  2. [§4.2; §5.2] The null result in the proactive condition is underpowered for the moderation claim. With n = 40 in the proactive arm and median-split groups of 17 (high GLAT) and 23 (low GLAT), the Mann-Whitney tests have low sensitivity to true literacy effects; the paper does not report the minimum detectable effect size or an equivalence bound. The conclusion that proactive scaffolding 'equalised outcomes' therefore conflates absence of evidence with evidence of absence. Please report a sensitivity analysis, an equivalence test, or at least a clear statement of the effect size that the design could detect with 80% power.
  3. [§4.4, Table 2; §5.4] The mediation comparison across conditions is informal and the paper's language about a 'direct effect' is not fully supported by the reported statistics. In Table 2, path c' in the reactive condition is 0.140 and is not flagged as significant, yet Section 5.4 states that 'the direct path was reliable.' In addition, the 95% confidence intervals for the indirect effects in the two conditions overlap substantially ([-0.022, 0.108] vs. [-0.055, 0.102]), so the data do not establish a difference in mediation pathways. A formal moderated-mediation test (e.g., a test of the interaction between GLAT and condition on the indirect or direct effect) is needed before claiming that GenAI literacy operates as a direct, task-general competence in one condition but not the other.
minor comments (6)
  1. [Abstract; §4.3] The abstract states that 'literacy-by-design interactions were not significant,' but this test is not reported in the Results section. Please report the interaction model and its test statistic so that readers can evaluate the claim.
  2. [§3.2] The a priori power analysis reports effect size f = 0.25 for a sample of 72, but the design includes multiple outcomes and two conditions; please clarify which test the power calculation refers to (e.g., ANOVA, regression, Mann-Whitney) and whether it was adjusted for the number of comparisons.
  3. [§3.4.1, Eq. (1)] The correction-for-guessing formula (CS = R - W/(C-1)) can produce negative scores; please state how such scores were handled in the analyses and whether any negative CS values occurred.
  4. [§3.3.3] The sentence 'both chatbots introduced users with a welcome message' would read more clearly as 'both chatbots greeted users with a welcome message'; minor language editing throughout would improve readability.
  5. [References (Yan et al., 2024b)] The reference contains a typo: 'Gaševi’c' should be 'Gašević'. Please correct it.
  6. [Figures 5-7] The correlation heatmaps are informative but the color scales and annotation sizes are hard to read in the preprint; consider enlarging font sizes and adding exact p-values or a significance legend.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the literacy–writing association is an empirical result with a separately measured predictor and outcome, not an artifact of definitions or fitted parameters.

full rationale

The paper's central claim is an empirical association: GLAT scores predict post-support writing outcomes in the reactive condition but not in the proactive condition. The predictor (GLAT) and the outcome (rubric-scored writing) are measured by separate instruments, and the GLAT items are reproduced in Appendix A, so the result is falsifiable rather than true by construction. The ordinal regression in Eq. 2 and the condition-specific mediation analyses estimate associations from data; there is no fitted parameter that is later renamed as a prediction. The 'agency gap' is introduced as a name for a hypothesized pattern rather than defined so as to guarantee the finding, and the paper explicitly concedes that literacy-by-design interactions were not significant, which is a statistical limitation rather than a circular step. The self-citations to GLAT (Jin et al., 2024) and the VizChat-based chatbot design (Yan et al., 2024b) are not load-bearing in a circular way: the measurement instrument and the intervention are described concretely in the paper and could have produced null or opposite results. The skeptical concern about the absence of a formal interaction test concerns inferential strength and power, not circularity, and therefore does not raise the circularity score.

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

The analysis is empirical; no free parameters are fitted to make a derivation work. The main load-bearing assumptions are measurement validity (GLAT, mini-VLAT), the operationalisation of reactive versus proactive design as a single factor, and the interpretation of immediate post-task performance as independent transfer.

assumptions (5)
  • domain assumption GLAT scores measure generative AI literacy as a stable trait.
    The central claim interprets GLAT as a valid measure of GenAI literacy; validity evidence comes from the authors' own instrument paper (Jin et al., 2024), not independently replicated here.
  • domain assumption mini-VLAT scores measure the domain knowledge relevant to the writing task.
    mini-VLAT is used as the control for domain knowledge; its validation is from prior literature (Pandey and Ottley, 2023), and the paper reports no sample-specific reliability.
  • domain assumption The two chatbot conditions differ only in interaction strategy (reactive vs proactive).
    Proactive chatbot also provided domain-expert-designed guiding questions and structured feedback (Appendix D), so the design contrast bundles interaction initiativeness with extra scaffolding content; the paper treats this as a single factor.
  • domain assumption Immediately parallel post-task performance indicates transferable independent writing ability.
    Phase 2 occurs in the same session right after AI support; order effects, fatigue, or practice effects are not controlled or modelled.
  • standard math Ordinal logistic regression and mediation models assume proportional odds and no unmeasured confounders.
    Brant test supported proportional odds; mediation assumes sequential ignorability, which is untestable with observational mediation within experimental conditions.

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Cite this review

Pith. "Pith review of The Agency Gap in AI-Supported Writing: How Reactive and Proactive Agent Designs Shape Multimodal Reasoning." pith.science (2026). https://pith.science/paper/CFFGOYEJ

@misc{pith2026250704398,
  author       = {Pith},
  title        = {Pith review of: The Agency Gap in AI-Supported Writing: How Reactive and Proactive Agent Designs Shape Multimodal Reasoning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CFFGOYEJ}},
  note         = {Machine review of arXiv:2507.04398}
}
read the original abstract

Generative AI is becoming part of academic writing, but its educational value depends on how control is shared between learner and system. This study examined an agency gap: performance differences that may arise when AI agent initiative is misaligned with learners' generative AI literacy. Seventy-nine medical and nursing students completed two multimodal analytical writing tasks using healthcare simulation data visualisations. They were randomly assigned to a reactive agent that responded only when prompted or a proactive agent that provided sequenced questions and feedback. Generative AI literacy was measured using the validated 20-item Generative AI Literacy Assessment Test (GLAT). Epistemic network analysis showed that proactive interaction created stronger links among conceptual reasoning, evidence use, and constructive engagement, whereas reactive interaction was more factual and procedural. Ordinal regression showed that generative AI literacy predicted immediate independent writing performance after support was removed, particularly for visual data integration, critical thinking, and overall quality. Condition-specific mediation estimates showed a literacy-performance association in the reactive condition but not in the proactive condition; however, the indirect effects and literacy-by-design interactions were not significant. This pattern is consistent with smaller literacy-related performance differences under proactive scaffolding, but it does not establish a compensatory causal effect. Learner reflections indicated that effective AI writing support requires contextual feedback, dialogic scaffolding, and calibration of initiative to learner needs and task complexity. These findings position interaction design as a potential mechanism for supporting equitable and agency-supportive educational AI agents.

Figures

Figures reproduced from arXiv: 2507.04398 by the authors.

Figure 1
Figure 1. Three visuals showing (i) team behaviours (bar chart), (ii) team communication patterns (network diagram), and (iii) movement, communication intensity, and physiological responses (ward heatmap) [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Research platform showing (i) visual information display, (ii) academic writing space, and (iii) either AI-supported chat function (reactive or proactive) or (iv) standard task instruction (AI-removal condition). and Martinez-Maldonado, 2024b). In contrast, the proactive chatbot actively initiated conversations with users and continually engaged them through domain expert-designed questions and structured narrative … view at source ↗
Figure 3
Figure 3. GenAI chatbot interaction design contrasting a reactive chatbot (red; right) responding only upon learner query and a proactive chatbot (blue; left) scaffolding learning through structured questioning and iterative feedback. guiding learners through the ward map analyses, the proactive chatbot first prompted them towards interpreting the distribution of hexagons in the ward (positional data). Once it confirmed that … view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Experimental procedure consisting of two phases: (1) AI-supported writing task with either reactive or proactive chatbot assistance, and (2) AI-removal writing task without chatbot support. conditions at both the AI-support and AI-removal time points. The analysis was …
Figure 5
Figure 5. Figure 5: Correlation between visualisation literacy and rubric metrics. * p < 0.05, ** p < 0.01, *** p < 0.001 [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]
Figure 6
Figure 6. Figure 6: Correlation between GenAI literacy and rubric metrics. * p < 0.05, ** p < 0.01, *** p < 0.001 [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Partial correlations between GenAI literacy and writing performance controlling for visualisation literacy. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. Jin et al.: Preprint submitted to Elsevier Page 17 of 26 [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.