Harnessing AI in Secondary Education to Enhance Writing Competence

Eyvind Elstad; Harald Eriksen

REVIEW 3 major objections 6 minor 5 references

Harnessing AI in Secondary Education to Enhance Writing Competence

T0 review · 3 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper argues AI should support, not replace, the student's own drafting in secondary writing instruction.

desk verdict A practical, honest position review with a load-bearing but untested assumption about students needing to compose in their own words. read the letter →

arxiv 2412.12117 v1 pith:FE6BQNDY submitted 2024-12-02 cs.CY

Eyvind Elstad , Harald Eriksen This is my paper · ORCID

classification cs.CY

keywords AIineducationwritingcompetencevoiceprocess-basedassessmentChatGPTsecondaryargumentativefeedback

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 argues that generative AI can strengthen secondary-school writing instruction only if it is used as a supplement to, not a substitute for, the student's own writing. The authors claim AI belongs in the pre-writing and revision phases—as an idea generator, a devil's advocate for counterarguments, and a source of instant, criteria-linked feedback—but not in the drafting phase, where students must formulate text in their own words. They recommend process-based assessments, assignments that require personal reflection, and technological controls so that AI use does not erode writing competence or personal voice. The stakes are practical: schools must decide how to respond to ChatGPT's arrival without either banning a useful tool or letting it hollow out the writing instruction that builds literacy.

What carries the argument

The load-bearing mechanism is the staged model of the writing process—pre-writing, writing, revision, and publishing—combined with the requirement that the student's own words carry the writing phase. Within that model, AI's legitimate roles are defined by phase: in pre-writing it can activate ideas and supply counterarguments; in revision it can give concrete, criteria-based feedback that would otherwise exceed a teacher's time; in the writing phase it should not intervene. The paper also leans on the knowledge-transforming model of writing, in which converting ideas into text itself refines understanding, to explain why delegating drafting is costly, and on process-based assessment to make the student's journey visible.

What would settle it

A controlled longitudinal study would settle it: randomly assign secondary students writing the same argumentative tasks to either draft entirely in their own words or freely draft with ChatGPT and then revise, keep assignments and feedback constant, and compare blind-scored writing competence at the end of the year. If the AI-drafting group matches or exceeds the own-words group, the paper's central premise is wrong.

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Extended reading notes

Core claim

The paper's central claim is that the writing process itself is where writing competence develops, and that AI should be inserted at its edges, not its center. It distinguishes writing competence, the technical ability to compose a well-structured text, from writing voice, the personal expression that makes a text distinctive, and argues that both are put at risk when students delegate text generation to AI. On this view, AI is useful as a brainstorming partner, as a generator of counterarguments that strengthen argumentative texts, and as an instant feedback provider during revision, but the drafting phase must remain the student's own work. The paper therefore advocates a balanced approach: keep AI out of high-stakes, unmonitored writing, redesign assignments around authentic purposes and personal experience, and make assessment process-based so that learning-inhibiting shortcuts are not rewarded.

Load-bearing premise

The argument stands on the premise that writing competence grows mainly through the student's own active text production, so letting AI generate drafts necessarily bypasses that growth; if AI-assisted drafting or feedback could transfer writing skill without full student authorship, the case against AI shortcuts would weaken.

Editorial extensions

If this is right

Teachers should redesign assignments so that success depends on personal experience, local knowledge, or individual judgment, making AI-generated responses hard to pass off as authentic.
Schools should weight process-based evidence—drafts, discussions, revisions—alongside final products when grading, weakening the incentive to copy AI text.
AI feedback can be scaled to provide immediate, criteria-focused comments in revision, especially for large classes, as long as prompts are short and concrete.
Exams and controlled writing conditions will remain necessary, since detection tools and even experienced teachers cannot reliably identify AI-generated texts.
Students can use AI as a tactical resource—finding counterarguments, testing rhetorical moves—without bypassing the planning and drafting stages.

Reading between the lines

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

The authors do not spell this out, but the staged-process argument implies that acceptable AI use should be defined by phase: allowed for idea generation and feedback, prohibited for drafting in graded work, and made explicit in school regulations.
A testable extension would compare two-year writing growth in classrooms using AI only in pre-writing and revision against classrooms where AI is also allowed in drafting, holding assignments and assessment constant.
The logic implies that AI's role should shrink as the high-stakes nature of writing grows, suggesting different rules for low-stakes exploratory writing versus final graded products.

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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

This is a position piece, not a research preprint, so judge it as a narrative review. The useful thing is the practical synthesis: it walks through pre-writing, writing, revision, and feedback stages, gives concrete prompts and a worked example of ChatGPT feedback, and lands on a sensible balanced recommendation. It is honest that solid studies are missing, and it flags the AI-detection problem (Scarfe et al.) and teacher detectability (Fleckenstein et al.) clearly.

What is new is not the argument—the balanced approach already appears in Hodges & Kirschner and others—but the detailed Nordic classroom context and the worked examples. That gives it real value for teachers and teacher educators.

The soft spot is the load-bearing premise: the paper asserts that students must formulate drafts in their own words for writing competence to develop. It says this is essential, yet its own cited evidence complicates it. Levine et al. show students using ChatGPT without bypassing planning, drafting, and revising; Marzuki et al. report unanimous positive impacts on content and structure; Doshi and Hauser find AI boosts creativity for less creative writers. None of that refutes the premise, but it is enough to demand a softer formulation or an explicit engagement with the counter-evidence. The paper admits it relies on 'logic and reason,' which is thin for a policy argument that includes technological controls and assessment changes. There are also minor reference inconsistencies (Dosher/Doshi, Hogdes/Hodges, Gollins/Collins, Shere/Steere) that an editor should clean up.

Who is this for? Teachers, teacher educators, and education policymakers in secondary writing instruction. Researchers looking for new empirical findings or a novel framework will not find it. It is a well-written, coherent review that deserves a serious referee in a venue that publishes position papers or teaching-oriented research. I would send it to peer review and accept it after revision, with a request to temper the strength of the 'own words' claim and deal more fairly with the studies that point the other way.

Referee Report

3 major / 6 minor

Summary. The paper is a conceptual review and position paper on the potential effects of generative AI (such as ChatGPT) on secondary-school students' writing competence and personal voice. It argues for a balanced approach in which AI supplements rather than replaces teacher guidance, and it recommends process-based assessments, authentic and personally meaningful assignments, and instruction in critical thinking as safeguards. The paper is organized around the stages of the writing process (pre-writing, writing, and revision/feedback), with additional sections on self- and peer assessment, AI-text detection, characteristics of AI-generated language, and implications for teachers' work.

Significance. The paper offers a timely and readable synthesis of recent literature and provides concrete, usable examples of prompts and assignment types, which is a genuine practical strength. It is also honest about the state of evidence, explicitly stating that 'we must largely rely on conclusions based on logic and reason' because 'there are yet no solid studies' (Introduction and Writing and AI sections). This transparency is commendable. However, the paper is a narrative synthesis rather than a systematic review, and its central recommendation rests on an empirical premise about student drafting that is asserted rather than tested. These limitations reduce its evidentiary weight but do not eliminate its value as a considered position statement for educators and policymakers.

major comments (3)

[Writing Phase] The claim that 'it is essential for students to use their own words and create a draft' and that 'it is not easy to see how AI can contribute sensibly in this phase without interfering with or even replacing the necessary process of the student formulating the text in their own language' is presented as self-evident, but it is actually a substantive empirical premise about how writing skill develops. The manuscript itself cites evidence that is in tension with a hard version of this claim: Levine et al. (2024) found that upper secondary students can use ChatGPT as a constructive writing asset 'without bypassing the essential stages of planning, drafting, and revising'; Marzuki et al. (2023) report unanimous teacher-perceived positive impacts on content and structure; and Doshi and Hauser (2024) show creativity benefits for less-creative individuals. The authors should either qualify the claim, specify conditions under which AI-assisted drafting is or is not harmful, or present a theoretical argument for why these findings do not transfer to secondary school writing development. As written, the central policy recommendation against AI-enabled shortcuts depends on this undefended premise.
[Writing and AI / Introduction] The paper acknowledges that 'there are yet no solid studies clarifying this issue' and that it must rely on logic and reason. That admission is honest, but the paper does not then adopt a transparent argumentative structure that would let the reader evaluate its reasoning. In particular, it does not specify how the literature was selected, what counts as relevant evidence, or how the authors move from cognitive writing research (e.g., Bereiter and Scardamalia; Graham and Perin) to prescriptions about AI use. Several recommendations appear to be based on professional judgment rather than on cited studies, yet they are stated in the same assertive tone as evidence-based claims. The authors should clarify the nature of the contribution (e.g., position paper, research synthesis) and, if the argument is primarily from first principles, make the chain of reasoning and its premises explicit so that readers can assess the inference.
[Generalizability of cited evidence] The paper's target population is secondary education, but many of the empirical studies it invokes involve university students or adult writers. Doshi and Hauser (2024) studied adult participants recruited online; Marzuki et al. (2023) surveyed EFL educators in Indonesian universities; Steiss et al. (2024) evaluated ChatGPT feedback on writing more generally, not specifically in secondary school; and Jeon and Lee (2024) review a body of work that is largely outside the K–12 context. The authors should address the extent to which findings from higher education and adult populations transfer to secondary school students, especially given the paper's concern for low-performing readers and 'functional illiterates.' This is load-bearing because the recommendations are specifically scoped to secondary education.

minor comments (6)

[Pre-writing Phase] The text cites 'Dosher et al. (2024)', but the reference list contains only 'Doshi, A. R. & Hauser, O. P. (2024)'; please correct the in-text citation to Doshi and Hauser (2024).
[Characteristics of ChatGPT Language] The text cites '(Steere, 2024)', but no such reference appears in the reference list; the nearby reference 'Shere, E. (2024)' about the anatomy of an AI essay is presumably intended, so please correct either the citation or the reference list.
[Cheating and AI-generated Texts] The discussion of Scarfe et al. (2024) refers to 'these exam sensors' and says 'These sensors were experts'; this wording is confusing. If the authors mean that human expert examiners were unable to detect the AI-generated submissions, they should write 'expert examiners' or 'experienced markers' rather than 'sensors.'
[Cheating and AI-generated Texts] The in-text citation 'Fleckensten et al. (2024)' does not match the reference list entry 'Fleckenstein, J., Meyer, J., Jansen, T., Keller, S. D., Köller, O., & Möller, J. (2024)'; please align the spelling.
[Writing and AI] The citation 'Hogdes & Kirschner, 2024' is a typo; it should be 'Hodges & Kirschner, 2024' to match the reference list.
[Abstract / Introduction] The final sentence of the abstract and the final sentence of the introduction are nearly identical ('We argue for a balanced approach...'). This repetition should be removed or substantially rewritten to avoid redundancy.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an argumentative review, not a derivation, and its recommendations rest on external studies rather than on fitted inputs or self-citation.

full rationale

The manuscript is a narrative review with no equations, fitted parameters, or quantitative predictions, so the main circularity patterns—fitted input called prediction, definitional equivalence, uniqueness imported from authors—do not apply. Its central recommendation, that AI should supplement rather than replace student drafting and teacher feedback, is supported by independent empirical literature cited throughout (e.g., Graham and Perin 2007; Steiss et al. 2024; Levine et al. 2024; Marzuki et al. 2023) and is explicitly framed as provisional: the authors state 'there are yet no solid studies clarifying this issue' and 'we must largely rely on conclusions based on logic and reason.' I weighed these limitation statements as potential red flags, but they are epistemic caveats, not circular reductions. The one author self-citation (Eriksen 2018) appears in the feedback and formative-assessment discussion, but those points are corroborated by independent sources (Duijnhouwer et al. 2012; Wulandari 2022; Taras 2010; Topping 2009, 2017), so it is not load-bearing. The 'use your own words' premise in the Writing Phase is a substantive pedagogical assumption rather than a conclusion derived from the paper's own definitions; it is stated openly, and the surrounding text acknowledges the empirical uncertainty. No step of the argument reduces by construction to its inputs.

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

The paper is a review, so it introduces no free parameters or new entities. The central claim rests on domain assumptions about writing pedagogy and student behavior, which are drawn from external literature rather than tested here.

assumptions (4)

domain assumption Writing competence requires deliberate practice through the student's own text generation.
This is load-bearing: the paper argues that AI shortcuts are harmful because they bypass this practice. It is stated in the Writing Phase section: 'it is essential for students to use their own words and create a draft.'
domain assumption Process-based assessment provides a more valid measure of student writing competence than product-only assessment.
The paper recommends process-based assessments (in 'Implications for teachers’ work') but does not provide systematic evidence for their superiority in the AI context. This assumption underlies several recommendations.
domain assumption Feedback is most effective when it is concrete, criteria-referenced, and actionable.
The paper relies on this educational research principle to argue for specific AI prompt designs. It cites Duijnhouwer et al. and Eriksen, but the principle is an external domain assumption, not established within the paper.
domain assumption Students may take learning-inhibiting shortcuts when AI is available, and these shortcuts are avoidable through controls and task design.
The paper assumes that students are inclined to use AI to bypass effort and that measures like secure exam browsers and personal assignments can prevent this. This is a behavioral assumption not tested in the paper.

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Cite this review

Pith. "Pith review of Harnessing AI in Secondary Education to Enhance Writing Competence." pith.science (2026). https://pith.science/paper/FE6BQNDY

@misc{pith2026241212117,
  author       = {Pith},
  title        = {Pith review of: Harnessing AI in Secondary Education to Enhance Writing Competence},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FE6BQNDY}},
  note         = {Machine review of arXiv:2412.12117}
}

read the original abstract

The emergence of free AI tools like ChatGPT holds significant implications for developing writing skills in secondary education. This study examines AI's impact on students' writing competence and personal voice, balancing technological benefits against risks of dependency and plagiarism. We review the pros and cons of AI in the writing process, emphasizing process-based assessments, creativity-driven tasks, and AI as a supplement to teacher guidance. The discussion covers AI's role in the pre-writing, writing, and revision stages, and highlights the need for innovative assignments and critical thinking to maintain writing as a human, expressive activity. We advocate for a balanced approach to AI in education, ensuring it supports rather than replaces teacher instruction.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

5 extracted references · 1 canonical work pages

[1]

Abdelghani, R. (2023). GPT-3-Driven Pedagogical Agents to Train Children’s Curious Question-Asking Skills, International Journal of Artificial Intelligence Education, 34, 483–518. https://doi.org/10.1007/s40593-023-00340-7 Applebee, A. N., & Langer, J. A. (2015). Writing instruction that works: Proven methods for middle and high school classrooms. Teacher...

doi:10.1007/s40593-023-00340-7 2023
[3]

Turing Test

19 Scarfe, P ., Watcham, K., Clarke, A., & Roesch, E. (2024). A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study. PloS one, 19(6), e0305354. Shere, E. (2024, 2.juli). Anatomy of an AI Essay. Inside Higher Ed. https://www.insidehighered.com/opinion/career-advice/teaching/2024/07/02/ways...

work page 2024
[127]

Den skriver bra, men den skriver ikke deg

Baron, N. (2023). Who Wrote this. Stanford University Press. Berge, K. L. (2007). The rhetoric of science in practice: Experiences from Nordic research on subject- oriented texts and text cultures. Language and discipline perspectives on academic discourse. In: K. Flottum (ed.) Language and Discipline Perspectives on Academic Discourse. (pp.40-64), Cambri...

arXiv 2023
[1617]

Wathne, M. (2023) Bare legg ned hele skolen! Stavanger Aftenblad https://www.aftenbladet.no/meninger/debatt/i/bgPXzl/bare-legg-ned-hele- skolen?fbclid=IwAR3uJIlAB37nLJtT4KdMLHrl0ls2FrBjSG0Dcgp8ILJ9TapOQH0XQS31kCY Willingham, D. T. (2017). The reading mind: A cognitive approach to understanding how the mind reads. John Wiley & Sons. Wulandari, Y . (2022). ...

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[2021]

A., Alturki, N., & Alramlawi, S

Boston College, TIMSS & PIRLS International Study Center 18 Kuhail, M. A., Alturki, N., & Alramlawi, S. (2023). Interacting with educational robots: A systematic review. Education and Information Technologies, 28, 973–1018. http://doi.org/10.1007/s10639-022- 11177-3 Levine, S., Beck, S. W., Mah, C., Phalen, L., & PIttman, J. (2024). How do students use Ch...

arXiv 2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Abdelghani, R. (2023). GPT-3-Driven Pedagogical Agents to Train Children’s Curious Question-Asking Skills, International Journal of Artificial Intelligence Education, 34, 483–518. https://doi.org/10.1007/s40593-023-00340-7 Applebee, A. N., & Langer, J. A. (2015). Writing instruction that works: Proven methods for middle and high school classrooms. Teacher...

doi:10.1007/s40593-023-00340-7 2023

[2] [3]

Turing Test

19 Scarfe, P ., Watcham, K., Clarke, A., & Roesch, E. (2024). A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study. PloS one, 19(6), e0305354. Shere, E. (2024, 2.juli). Anatomy of an AI Essay. Inside Higher Ed. https://www.insidehighered.com/opinion/career-advice/teaching/2024/07/02/ways...

work page 2024

[3] [127]

Den skriver bra, men den skriver ikke deg

Baron, N. (2023). Who Wrote this. Stanford University Press. Berge, K. L. (2007). The rhetoric of science in practice: Experiences from Nordic research on subject- oriented texts and text cultures. Language and discipline perspectives on academic discourse. In: K. Flottum (ed.) Language and Discipline Perspectives on Academic Discourse. (pp.40-64), Cambri...

arXiv 2023

[4] [1617]

Wathne, M. (2023) Bare legg ned hele skolen! Stavanger Aftenblad https://www.aftenbladet.no/meninger/debatt/i/bgPXzl/bare-legg-ned-hele- skolen?fbclid=IwAR3uJIlAB37nLJtT4KdMLHrl0ls2FrBjSG0Dcgp8ILJ9TapOQH0XQS31kCY Willingham, D. T. (2017). The reading mind: A cognitive approach to understanding how the mind reads. John Wiley & Sons. Wulandari, Y . (2022). ...

2023

[5] [2021]

A., Alturki, N., & Alramlawi, S

Boston College, TIMSS & PIRLS International Study Center 18 Kuhail, M. A., Alturki, N., & Alramlawi, S. (2023). Interacting with educational robots: A systematic review. Education and Information Technologies, 28, 973–1018. http://doi.org/10.1007/s10639-022- 11177-3 Levine, S., Beck, S. W., Mah, C., Phalen, L., & PIttman, J. (2024). How do students use Ch...

arXiv 2023