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REVIEW 3 major objections 5 minor 40 references

AI in the Writing Process: How Purposeful AI Support Fosters Student Writing

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper argues that when a large language model is embedded into structured writing stages rather than delivered through a chat window, undergraduate writers report more agency and show more markers of deep knowledge transformation.

desk verdict Solid RCT with a strong agency effect, but the abstract overclaims knowledge transformation—only one of five codes holds up, and one 'significant' code is actually knowledge telling. read the letter →

arxiv 2506.20595 v1 pith:FUFZL43J submitted 2025-06-25 cs.HC cs.AI

Momin N. Siddiqui , Roy Pea , Hari Subramonyam This is my paper
classification cs.HCcs.AI
keywords AIwritingtoolsstudentagencyknowledgetransformationsource-basedchat-basedLLMinterfacesprocess-orientedsupportrandomizedcontrolledtrialeducation
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 tries to establish that the interface around an AI writing assistant, not just the model powering it, determines whether students stay in charge of their work and think deeply about sources. In a randomized trial with 90 undergraduates, students using Script&Shift, a tool that embeds LLM help into named subprocesses such as brainstorming, elaboration, audience analysis, and feedback, reported significantly more control, satisfaction, and willingness to own their essay than students using a chat-based LLM, and their essays carried more markers of analysis and evaluation. The authors argue this happens because process-oriented design keeps content and rhetoric as separate spaces that writers move between, whereas chat collapses them into ready-made text. If the claim is right, educators worried about ChatGPT eroding writing skills have a more constructive target: structuring AI support around the stages of writing rather than banning the tools.

What carries the argument

The argument is carried by the design of Script&Shift: a layered interface that separates content from rhetorical organization and lets writers summon specialized AI assistants for brainstorming, detail elaboration, audience analysis, and feedback instead of a single free-form chat. The measurement machinery is an essay-coding framework that classifies each sentence as knowledge transformation (synthesis, analysis, application, evaluation, comprehension) versus knowledge telling, combined with seven-point self-report items for agency. The experimental machinery is a three-condition randomized design with 30 participants per condition and the same LLM backend in the two AI conditions.

What would settle it

Take the same source-based writing task and compare Script&Shift against a chat condition whose usability and output quality are carefully matched (same underlying LLM, pre-tested ease-of-use ratings, and document-integrated suggestions rather than a blank separate window); if the agency and knowledge-transformation gaps vanish under that match, the claim that the interface paradigm causes them is refuted.

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Extended reading notes

Core claim

The study's central claim is that process-oriented AI support, where the LLM is embedded into named writing subprocesses within a layered document interface, gives undergraduate writers greater felt agency and more markers of deep knowledge transformation than a conventional chat-based LLM assistant. In the randomized comparison, Script&Shift outperformed the chat condition on Analysis markers, Evaluation markers, and the Knowledge category, and it outperformed both the chat and standard conditions on three self-reported agency items: feeling in control, feeling content with the essay, and willingness to publish under one's own name. The paper is careful to note that final essay-quality scores did not differ reliably, and it treats that as consistent with prior findings that knowledge-transformation markers and grades are only weakly related.

Load-bearing premise

The result rests on the assumption that differences came from the interface paradigm rather than from the particular quality, usability, or familiarity of the custom chat tool, and the paper's own limitations section notes that the single 1.5-hour session may have restricted engagement.

Editorial extensions

If this is right

  • Designing AI writing support around explicit writing subprocesses is a way to keep students in charge of their essays while still getting LLM help.
  • Chat-style assistants can be expected to show larger agency costs, and adding structured scaffolding may be more valuable than swapping the underlying model.
  • Process gains such as knowledge transformation can occur even when final essay scores do not improve, so outcome measures that rely only on grades will miss them.
  • Because participants felt more agency with Script&Shift than with no AI at all, AI assistance need not be framed as a trade-off against ownership.
  • Longer or repeated use would be required to see whether the knowledge-transformation markers translate into better final essays.

Reading between the lines

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

  • An untested implication is that chat's disadvantage comes from context-switching and copy-paste overhead; a follow-up that gives the chat condition inline document integration would isolate that mechanism.
  • If the tool's benefit is metacognitive rather than merely structural, the effect should survive a transfer task without Script&Shift; the single-session design cannot establish that.
  • The larger agency gap over the no-AI control hints that 'control' may mean having a visible, structured process, not just the absence of automation; keystroke-level authorship analysis would test that reading.
  • The null essay-quality result implies grade-based evaluations will not capture the process gains claimed here; portfolios or process-trace measures would be needed to see them.
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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 / 5 minor

Summary. The manuscript reports a three-arm randomized controlled trial (N=90 undergraduates) comparing a process-oriented integrated AI writing tool (Script&Shift), a custom chat-based LLM assistant, and a standard no-AI interface on a source-based argumentative writing task. It measures writer agency via post-test self-report and knowledge transformation via rubric-based coding of final essays. The paper reports that Script&Shift users experienced greater agency and "deeper knowledge transformation overall," and uses these results to argue that LLM support embedded in structured writing stages preserves ownership and deepens engagement. The core recommendation is major revision because the overall knowledge-transformation claim is not supported by the reported analyses.

Significance. The study has genuine strengths: a randomized design, a reasonably large sample for an interface experiment, external coding of essays with inter-rater reliability (kappa=0.76 initially, kappa=0.86 after discussion), and very large agency effects (e.g., Q1 F(2,85)=101.90). If the agency finding is taken at face value, it is a meaningful contribution to the HCI and learning-sciences literature on AI-assisted writing. However, the paper's central knowledge-transformation claim currently rests on one significant subcode (Analysis), and the "Knowledge" code is direction-inverted relative to the theoretical construct; the paper's contribution after revision would be the agency result plus a carefully bounded analytical-process claim, not "deeper knowledge transformation overall." The authors should also verify the fairness of the chat baseline, since the interface-paradigm interpretation depends on it.

major comments (3)
  1. [Section 4.1 and Figure 2] The abstract's claim of "deeper knowledge transformation overall" (and the Section 1 claim that process-oriented tools produce "more markers of knowledge transformation") is not supported by the reported statistics. Only Analysis shows a significant omnibus Kruskal-Wallis test (H=8.72, p=.013); Synthesis (H=2.75, p=.253), Application, and Comprehension are non-significant, and no omnibus test is reported for Evaluation or Knowledge. The pairwise Script&Shift advantages on Evaluation (U=287.50, p=.033) and Knowledge (U=554.00, p=.038) are only against the Chat condition and receive no multiple-comparison correction. More seriously, Figure 2 defines the Knowledge code as "Paraphrased/copied information from a source" under the "Knowledge Telling" category; a higher count of this code is evidence of knowledge telling, not knowledge transformation, so the sentence in Section 4.1 reporting "significantly better performance in Knowledge" as a positive outcome is direction-inverted. Because Synthesis, the code most aligned with the paper's own definition of transformation as integrating information from multiple sources, did not differ across conditions, the overall deeper-knowledge-transformation claim cannot be derived from the data and should be revised to name Analysis as the only code with a significant omnibus effect.
  2. [Section 3] The study is framed as a test of interface paradigm, but the chat condition is described only as "a custom chat-based LLM to support writing" with no feature inventory, no usability validation, and no evidence that it was a fair or non-strawman implementation. If the chat tool was less usable, unfamiliar, or missing basic affordances (e.g., document integration or copy-paste support), the Script&Shift advantage could reflect implementation quality rather than the process-oriented design. Please report the chat interface's features, any pilot testing, and post-task usability ratings or interface logs that establish the chat condition as a competent baseline before attributing the results to interaction paradigm.
  3. [Section 4.1] Multiple-testing protection is missing. Five Kruskal-Wallis tests are run, and the only significant one (Analysis, p=.013) no longer survives a simple Bonferroni correction for five tests (adjusted p approximately .065). The pairwise Mann-Whitney comparisons that follow are likewise uncorrected and are reported for Evaluation and Knowledge without a significant omnibus. The agency results in Section 4.2 are protected by omnibus ANOVAs and are credible; the knowledge-transformation section should be held to the same standard, or explicitly labeled exploratory.
minor comments (5)
  1. [Section 4.2] The text describes a p < .05 result as "marginally higher," but "marginal" is usually reserved for .05 < p < .10; also, the sentence "For question 2..." is confusing because the immediately preceding sentence discusses a different item. Please label the two AI-agency items unambiguously.
  2. [Section 3.1] The demographic reporting ("mode of age range was 18-39," "median age range was 26-30") is unstandardized; report age distribution with mean and standard deviation or binned counts.
  3. [Section 4.4 and Figure 5] The text says "Analysis revealed a moderate positive correlation" but the figure caption specifies that the correlation is for Script&Shift while the chat-based condition shows no correlation; state the subgroup and sample size for r=0.61, and clarify whether the measure is interface transactions or LLM feature access.
  4. [Section 4.3] No inferential test is reported for essay quality or source integration; the discussion of "high variance indicates considerable group overlap" would benefit from a formal comparison (e.g., Welch's ANOVA or Kruskal-Wallis) and effect sizes.
  5. [Throughout Section 4] The paper does not report effect sizes or confidence intervals for the significant tests; adding them would help readers judge the magnitude of the agency and Analysis effects.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim is an empirical RCT comparison whose outcomes are measured by an external coding rubric and self-report, not by the tool's definition or by a fitted parameter.

full rationale

The paper's central claim, that Script&Shift increases writer agency and knowledge transformation relative to chat and standard interfaces, rests on independent measurements rather than on the tool's construction. Knowledge transformation is operationalized via qualitative coding using the external framework of Raković et al. [28], and agency is assessed through post-test Likert-scale self-reports. The tool's design is introduced through the authors' prior work [33], but no outcome measure is defined in terms of Script&Shift's features, and no parameter is fitted to the data such that the observed differences are forced. The only substantive self-citation, reference [33], is used to describe the tool and to interpret feature-usage patterns, neither of which constitutes the load-bearing derivation of the agency or knowledge-transformation results. Concerns about the 'Knowledge' code being classified under knowledge telling while also counted as a knowledge-transformation marker, and about uncorrected multiple comparisons, are validity and statistical-inference issues rather than circularity: the coding rubric and survey items are external to the present paper and do not reduce to the conclusion by construction. The derivation chain is therefore self-contained with respect to circularity, even though the strength of the evidence is weakened by measurement and analysis limitations.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

No free parameters were fit to data beyond the unreported tercile cutoffs; the study is empirical. The conceptual assumptions are the validity of the external coding rubric, the self-report agency items, and the representativeness of the custom chat baseline.

free parameters (1)
  • Knowledge transformation tercile cutoffs = not reported
    In Section 4.4, participants are split into high, moderate, and low knowledge-transformation groups for the feature-use analysis, but the cut points are not defined. This manual analytic choice affects the Figure 5 interpretation.
assumptions (4)
  • domain assumption The Raković et al. coding scheme validly operationalizes knowledge transformation as distinct from knowledge telling.
    Used in Section 3 to code essays; if marker counts do not index depth of cognitive processing, the knowledge-transformation results do not support the claim.
  • domain assumption The three self-report Likert questions (Q1-Q3) measure writer agency as a construct.
    Agency is assessed only via self-report in Section 4.2; no behavioral or validated psychometric scale is used.
  • domain assumption Random assignment created comparable groups without baseline writing ability differences.
    No pre-test writing measure is reported in Section 3.1, so the causal interpretation relies on randomization balancing unmeasured ability.
  • domain assumption The custom chat-based interface is representative of chat-based LLM writing assistants.
    Section 3 introduces 'a custom chat-based LLM to support writing' with no usability validation; if it is a strawman, the comparison overstates Script&Shift's benefits.

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Cite this review

Pith. "Pith review of AI in the Writing Process: How Purposeful AI Support Fosters Student Writing." pith.science (2026). https://pith.science/paper/FUFZL43J

@misc{pith2026250620595,
  author       = {Pith},
  title        = {Pith review of: AI in the Writing Process: How Purposeful AI Support Fosters Student Writing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FUFZL43J}},
  note         = {Machine review of arXiv:2506.20595}
}
read the original abstract

The ubiquity of technologies like ChatGPT has raised concerns about their impact on student writing, particularly regarding reduced learner agency and superficial engagement with content. While standalone chat-based LLMs often produce suboptimal writing outcomes, evidence suggests that purposefully designed AI writing support tools can enhance the writing process. This paper investigates how different AI support approaches affect writers' sense of agency and depth of knowledge transformation. Through a randomized control trial with 90 undergraduate students, we compare three conditions: (1) a chat-based LLM writing assistant, (2) an integrated AI writing tool to support diverse subprocesses, and (3) a standard writing interface (control). Our findings demonstrate that, among AI-supported conditions, students using the integrated AI writing tool exhibited greater agency over their writing process and engaged in deeper knowledge transformation overall. These results suggest that thoughtfully designed AI writing support targeting specific aspects of the writing process can help students maintain ownership of their work while facilitating improved engagement with content.

Figures

Figures reproduced from arXiv: 2506.20595 by the authors.

Figure 1
Figure 1. These are the three interface conditions for the experiment. [A] is Script&Shift; [B] is the chat-based interface condition; [C] is the standard interface. Each condition has an always-present dock that contains information about the source-based writing task. deep reasoning [30]. The generative writing phenomenon raises urgent concerns about the homogenization of thought, erosion of writer ownership, and dimin￾ishe… view at source ↗
Figure 2
Figure 2. Codes For Knowledge Transformation (Synthesis, Analysis, Application, Eval￾uation, and Comprehension) and Knowledge Telling (Knowledge) from Raković et al. 2019 [28] with an example from our participant essays’ coding. guide their essay writing and additionally recommended composing their writings into three sections: introduction, body, and conclusion. We prescribed having three body paragraphs but made it clear th… view at source ↗
Figure 3
Figure 3. This figure compares agreement for (A) Script&Shift, (B) Chat-based LLM, and (C) Standard interfaces across three dimensions of agency: perceived control during writing (Q1), content satisfaction (Q2), and publication comfort (Q3). Across all measures, the differences between Script&Shift and chat-based conditions were notably larger than those between Script&Shift and Standard conditions (∆µShif t−Chat > ∆µShif t−S… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: These plots are for individual questions in the survey on a 7-point Likert scale to measure writing scaffolding and agency experienced by the writer. (µ = 15.29, σ = 7.40). The relatively high standard deviations in both measures suggest substantial variability in how …
Figure 5
Figure 5. Figure 5: (A) shows the correlation between knowledge transformation markers and LLM feature access. While chat-based shows no correlation, Script&Shift exhibits moderate positive correlation (r = 0.608, p = 0.001). (B1-B3) display most-used LLM features for three representative…

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Works this paper leans on

40 extracted references · 37 canonical work pages

  1. [1]

    In: Proceedings of the 16th Conference on Creativity & Cognition

    Anderson, B.R., Shah, J.H., Kreminski, M.: Homogenization effects of large lan- guage models on human creative ideation. In: Proceedings of the 16th Conference on Creativity & Cognition. pp. 413–425 (2024)

    work page 2024

  2. [2]

    https://www.anthropic.com (2025), accessed: 2025-01-22

    Anthropic: Claude. https://www.anthropic.com (2025), accessed: 2025-01-22

    work page 2025

  3. [3]

    University of Texas Press (2010)

    Bakhtin, M.M.: The dialogic imagination: Four essays. University of Texas Press (2010)

    work page 2010

  4. [4]

    Available at SSRN4895486 (2024)

    Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, O., Mariman, R.: Generative AI can harm learning. Available at SSRN4895486 (2024)

    work page 2024

  5. [5]

    Routledge (2013)

    Bereiter, C., Scardamalia, M.: The Psychology of Written Composition. Routledge (2013)

    work page 2013

  6. [6]

    Learning Lives: Learning, Identity, and Agency in the Life Course pp

    Biesta, G., Tedder, M.: How is agency possible? towards an ecological understand- ing of agency-as-achievement. Learning Lives: Learning, Identity, and Agency in the Life Course pp. 132–149 (2006)

    work page 2006

  7. [7]

    but it’s still not an A+ student

    Bowman, E.: A new AI chatbot might do your homework for you. but it’s still not an A+ student. NPR (December 2022), https://www.npr.org/2022/12/19/1143912956/chatgpt-ai-chatbot-homework- academia

    work page 2022

  8. [8]

    In: Gregg, L.W., Steinberg, E.R

    Collins, A., Gentner, D.: A framework for a cognitive theory of writing. In: Gregg, L.W., Steinberg, E.R. (eds.) Cognitive Processes in Writing, pp. 51–72. Erlbaum (1980)

    work page 1980

Show all 40 references
  1. [9]

    In: International Conference on Artificial Intelligence in Education

    Cummings, R.E., Le, T., Shrestha, S., Smith, C.V.: The unexpected effects of google smart compose on open-ended writing tasks. In: International Conference on Artificial Intelligence in Education. pp. 468–481. Springer (2024) AI in the Writing Process 13

    2024

  2. [10]

    Education and Information Technologies 24(2), 1563–1581 (2019)

    Dahlström, H.: Digital writing tools from the student perspective: Access, affor- dances, and agency. Education and Information Technologies 24(2), 1563–1581 (2019)

    2019

  3. [11]

    Emirbayer,M.,Mische,A.:Whatisagency?AmericanJournalofSociology 103(4), 962–1023 (1998)

    1998

  4. [12]

    College Composition and Communication 32(4), 365–387 (1981)

    Flower, L., Hayes, J.R.: A cognitive process theory of writing. College Composition and Communication 32(4), 365–387 (1981)

    1981

  5. [13]

    arXiv preprint arXiv:2409.13686 (2024)

    Geng, M., Chen, C., Wu, Y., Chen, D., Wan, Y., Zhou, P.: The impact of large language models in academia: From writing to speaking. arXiv preprint arXiv:2409.13686 (2024)

    2024 arXiv

  6. [14]

    In: Proceedings of the 2022 ACM Designing Interactive Systems Conference

    Gero, K.I., Liu, V., Chilton, L.: Sparks: Inspiration for science writing using lan- guage models. In: Proceedings of the 2022 ACM Designing Interactive Systems Conference. pp. 1002–1019 (2022)

    2022

  7. [15]

    In: International Conference on Artificial Intelligence in Educa- tion

    Gubelmann, R., Burkhard, M., Ivanova, R.V., Niklaus, C., Bermeitinger, B., Hand- schuh, S.: Exploring the usefulness of open and proprietary llms in argumentative writing support. In: International Conference on Artificial Intelligence in Educa- tion. pp. 175–182. Springer (2024)

    2024

  8. [16]

    In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

    Hoque, M.N., Mashiat, T., Ghai, B., Shelton, C.D., Chevalier, F., Kraus, K., Elmqvist, N.: The HaLLMark effect: Supporting provenance and transparent use of large language models in writing with interactive visualization. In: Proceedings of the 2024 CHI Conference on Human Fac...

    2024

  9. [17]

    Assessment & Evaluation in Higher Education47(8), 1301–1316 (2022)

    Kim, M.K., Kim, N.J., Heidari, A.: Learner experience in artificial intelligence- scaffolded argumentation. Assessment & Evaluation in Higher Education47(8), 1301–1316 (2022)

    2022

  10. [18]

    Lee, M., Gero, K.I., Chung, J.J.Y., Shum, S.B., Raheja, V., Shen, H., Venugopalan, S., Wambsganss, T., Zhou, D., Alghamdi, E.A., et al.: A design space for intelligent andinteractivewritingassistants.In:ProceedingsoftheCHIConferenceonHuman Factors in Computing Systems. pp. 1–35 (2024)

    2024

  11. [19]

    In: The Routledge Handbook of Discourse Processes, pp

    McNamara, D.S., Allen, L.K.: Toward an integrated perspective of writing as a discourse process. In: The Routledge Handbook of Discourse Processes, pp. 362–

  12. [20]

    Behavior Research Methods, Instruments, & Computers 36(2), 222–233 (2004)

    McNamara, D.S., Levinstein, I.B., Boonthum, C.: istart: Interactive strategy train- ing for active reading and thinking. Behavior Research Methods, Instruments, & Computers 36(2), 222–233 (2004)

    2004

  13. [21]

    BioData Mining 16(1), 20 (2023)

    Meyer, J.G., Urbanowicz, R.J., Martin, P.C., O’Connor, K., Li, R., Peng, P.C., Bright, T.J., Tatonetti, N., Won, K.J., Gonzalez-Hernandez, G., et al.: Chatgpt and large language models in academia: opportunities and challenges. BioData Mining 16(1), 20 (2023)

    2023

  14. [22]

    Mieczkowski, H., Hancock, J.: Examining agency, expertise, and roles of ai systems in ai-mediated communication (Jul 2022), osf.io/asnv4_v1

    2022

  15. [23]

    In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

    Mirowski, P., Mathewson, K.W., Pittman, J., Evans, R.: Co-writing screenplays and theatre scripts with language models: Evaluation by industry professionals. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. pp. 1–34 (2023)

    2023

  16. [24]

    Science381(6654), 187–192 (2023)

    Noy, S., Zhang, W.: Experimental evidence on the productivity effects of generative artificial intelligence. Science381(6654), 187–192 (2023)

    2023

  17. [25]

    International Journal of Artificial Intelligence in Education pp

    Parker, J.L., Richard, V.M., Acabá, A., Escoffier, S., Flaherty, S., Jablonka, S., Becker, K.P.: Negotiating meaning with machines: AI’s role in doctoral writing pedagogy. International Journal of Artificial Intelligence in Education pp. 1–21 (2024) 14 M. Siddiqui et al

    2024

  18. [26]

    AI & SOCIETY (2025)

    Peterson, A.J.: AI and the problem of knowledge collapse. AI & SOCIETY (2025)

    2025

  19. [27]

    Prolific: Prolific—participant recruitment for online research (2025), https://www.prolific.com/, accessed: 2025-01-22

    2025

  20. [28]

    In: International Learning Analytics & Knowledge Conference

    Rakovic, M., Marzouk, Z., Chang, D., Winne, P.H.: Towards knowledge- transforming in writing argumentative essays from multiple sources: A method- ological approach. In: International Learning Analytics & Knowledge Conference

  21. [29]

    Journal of Computer Assisted Learning37(4), 903–924 (2021)

    Rakovic, M., Winne, P.H., Marzouk, Z., Chang, D.: Automatic identification of knowledge-transforming content in argument essays developed from multiple sources. Journal of Computer Assisted Learning37(4), 903–924 (2021)

    2021

  22. [30]

    Advances in Applied Psycholinguistics2, 142–175 (1987)

    Scardamalia, M., Bereiter, C.: Knowledge telling and knowledge transforming in written composition. Advances in Applied Psycholinguistics2, 142–175 (1987)

    1987

  23. [31]

    Cognitive Science8(2), 173–190 (1984)

    Scardamalia, M., Bereiter, C., Steinbach, R.: Teachability of reflective processes in written composition. Cognitive Science8(2), 173–190 (1984)

    1984

  24. [32]

    L1-Educational Studies in Language And Literature 4, 5–33 (2004)

    Segev-Miller, R.: Writing from sources: The effect of explicit instruction on col- lege students’ processes and products. L1-Educational Studies in Language And Literature 4, 5–33 (2004)

    2004

  25. [33]

    In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

    Siddiqui, M.N., Pea, R.D., Subramonyam, H.: Script&Shift: A layered interface paradigm for integrating content development and rhetorical strategy with llm writing assistants. In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. pp. 1–19 (2025)

    2025

  26. [34]

    arXiv preprint arXiv:2502.12447 (2025)

    Singh, A., Taneja, K., Guan, Z., Ghosh, A.: Protecting human cognition in the age of ai. arXiv preprint arXiv:2502.12447 (2025)

    2025

  27. [35]

    ACM Transactions on Computer-Human Interaction30(5), 1–57 (2023)

    Singh, N., Bernal, G., Savchenko, D., Glassman, E.L.: Where to hide a stolen elephant: Leaps in creative writing with multimodal machine intelligence. ACM Transactions on Computer-Human Interaction30(5), 1–57 (2023)

    2023

  28. [36]

    Computers & Education131, 33–48 (2019)

    Strobl, C., Ailhaud, E., Benetos, K., Devitt, A., Kruse, O., Proske, A., Rapp, C.: Digital support for academic writing: A review of technologies and pedagogies. Computers & Education131, 33–48 (2019)

    2019

  29. [37]

    International Journal of Artificial Intelligence in Education pp

    Tarchi, C., Zappoli, A., Casado Ledesma, L., Brante, E.W.: The use of chatgpt in source-based writing tasks. International Journal of Artificial Intelligence in Education pp. 1–21 (2024)

    2024

  30. [38]

    arXiv preprint arXiv:2112.04359 (2021)

    Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021)

    2021 arXiv

  31. [39]

    International Journal of Artificial Intelligence in Education 28, 106–137 (2018)

    Weston-Sementelli,J.L., Allen,L.K.,McNamara,D.S.:Comprehensionandwriting strategy training improves performance on content-specific source-based writing tasks. International Journal of Artificial Intelligence in Education 28, 106–137 (2018)

    2018

  32. [2019]

    pp. 267–272. Society for Learning Analytics Research (2019)

    2019

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