Best Articles on AI in Academic Writing (Nov 2025)

Posted on November 16, 2025 by JimS

By Jim Shimabukuro (assisted by Claude)
Editor

1. “Artificial Intelligence-Assisted Academic Writing: Recommendations for Ethical Use”

Article Information:

The central thesis of this article is that generative artificial intelligence tools can be ethically integrated into academic writing processes as long as researchers adhere to principles of transparency, maintain human accountability for content, and use AI to enhance rather than replace critical thinking and scholarly development.

Video created by Grok via image created by Copliot

The authors articulate their position clearly when they state: “We argue that it is possible to use generative AI tools to support the academic writing process, and that doing so is a legitimate and natural part of the evolution of the global healthcare simulation research community, as long as certain ethical safeguards are adhered to.” They further emphasize the critical limitation that “researchers ultimately hold full responsibility for the originality of their manuscript, accuracy and relevance of content, and appropriate referencing of published literature.”

The article builds its case through a systematic examination of both the promises and perils of large language models in scholarly work. Cheng, Calhoun, and Reedy begin by establishing the historical context of technological advancement in research, arguing that AI represents the latest evolution in a long line of tools that have transformed academic practice. However, they distinguish AI from previous technologies by highlighting its unique ability to generate novel written content autonomously, which introduces unprecedented challenges to traditional notions of academic integrity.

The authors ground their recommendations in a thorough analysis of the current state of AI capabilities and limitations. They document three major challenges with AI-generated content that emerged from empirical studies: plagiarism risks, the phenomenon of AI hallucination where convincing but entirely fabricated information is presented as fact, and the generation of inaccurate or completely fabricated references. These findings led them to caution against using AI to write content verbatim without human editing or to generate references without verification.

To address the ethical quandary of how to engage with AI responsibly, the authors develop a tiered framework for AI use in academic writing. The most ethically acceptable tier includes using AI for restructuring existing text through grammar and spelling corrections, improving readability, and language translation. These applications leverage AI’s strengths in syntax and structure while requiring minimal risk of introducing fabricated content. The middle tier represents more contingent uses including generating outlines, summarizing content, improving clarity, and brainstorming ideas. These applications require heightened vigilance because they task AI with generating novel text, thus carrying greater potential for bias, hallucination, or plagiarism. The authors emphasize that in these cases, researchers must ensure the final product accurately reflects their own ideas and that AI-generated content did not alter key meanings.

The article’s most ethically suspect tier includes using AI for primary data interpretation, conducting literature reviews, and checking for plagiarism or bias. The authors argue that these tasks are best left to human researchers because they require deep engagement with source material and critical analysis that remains properly within the intellectual domain of the authors themselves. They note that asking AI to perform primary data analysis effectively short-circuits an intellectual process essential for comprehensive understanding.

To operationalize their framework, Cheng, Calhoun, and Reedy propose four guiding questions that authors should ask themselves: Have I ensured that primary ideas, insights, interpretations, and critical analyses are my own? Have I used AI in a way that maintains competency in core research and writing skills? Have I verified that all content and references are accurate, reliable, and free of bias? Have I disclosed exactly how and where AI was used in the manuscript? These questions serve as a practical checklist for ethical AI engagement.

This article matters profoundly for college faculty, students, course designers, and administrators because it provides the field’s first comprehensive ethical framework specifically designed for academic writing in an AI-enabled environment. For faculty members, the tiered system offers concrete guidance on how to advise students and evaluate AI-assisted work, moving beyond simplistic prohibitions to nuanced understanding. The article helps faculty distinguish between AI use that supports learning and development versus AI use that circumvents the intellectual work necessary for scholarly growth.

For students, the framework provides clarity in an otherwise confusing landscape. Rather than wondering whether any AI use constitutes cheating, students can understand which applications are widely considered acceptable, which require special care and disclosure, and which undermine their own educational development. The four guiding questions offer students a practical tool for self-assessment before submitting any AI-assisted work.

Course designers benefit from the article’s pedagogical insights about maintaining human competency in the age of AI. The authors’ concern about scholars becoming too dependent on AI for ideation, generation of primary content, and initial data interpretation provides important considerations for curriculum development. Course designers can use this framework to structure assignments that encourage appropriate AI use while ensuring students develop essential critical thinking skills that AI cannot replace.

For administrators, this article offers evidence-based guidance for institutional policy development at a time when many universities struggle to establish coherent AI policies. The authors’ emphasis on transparency through disclosure in methods sections, combined with their nuanced understanding of different AI applications, provides a model that balances innovation with integrity. Administrators can use this framework to develop institutional guidelines that neither ban AI entirely nor permit unrestricted use, instead establishing clear expectations for ethical engagement. The article also highlights the need for ongoing policy revision as AI technology evolves, helping administrators understand that current guidelines represent starting points rather than permanent solutions.

2. “Using AI in Academic Writing: What’s Allowed and What’s Not”

Article Information:

This editorial argues that while artificial intelligence tools have legitimate applications in academic writing, their ethical use requires clear distinctions between AI-assisted and AI-generated content, transparent disclosure practices, and adherence to evolving guidelines from major academic publishing organizations.

Laher establishes the central tension in her opening when she notes that “AI is able to generate entire pieces of work with precision and finesse such that it becomes difficult to distinguish original work written by an individual and work generated by AI.” She synthesizes the consensus position by explaining that major publishing bodies “share a unified message: while AI tools can certainly play a role in academic writing, their use must be approached with transparency, caution, and respect for ethical standards.”

Laher constructs her argument by first establishing a critical conceptual distinction that forms the foundation for all subsequent ethical guidelines. She differentiates between AI-assisted content, where an author writes predominantly original work but uses AI tools for improvements like grammar checks and style suggestions, and AI-generated content, where AI produces significant portions of text based on detailed prompts from the user. This distinction matters because it determines whether disclosure is optional or mandatory.

The article then examines the convergence of thinking across four major academic bodies: the Committee on Publication Ethics, Sage Publishing, the American Psychological Association, and the Academy of Science of South Africa through its journal platform. Laher identifies three core principles that unite these organizations’ positions. First, AI tools cannot be listed as co-authors because they cannot take responsibility for content accuracy or approve manuscripts. Second, transparency about AI use is paramount to maintaining academic integrity. Third, authors bear full responsibility for verifying the accuracy, ethical soundness, and integrity of any AI-influenced content.

Laher addresses the practical question of how authors should disclose AI use in different scenarios. For routine assistance like grammar checking and sentence structure improvement that does not require specific citation, she follows COPE guidelines in suggesting these applications need not be disclosed. However, when AI generates substantive content, clear and explicit disclosure becomes mandatory. Drawing on recent scholarship, Laher recommends that when AI use cannot be explicitly cited within the manuscript text itself, it should be declared in the Acknowledgments section, though she notes that journal templates typically lack dedicated declaration sections for this purpose.

The article provides nuanced guidance on the peer review process as well. Laher emphasizes that reviewers must maintain manuscript confidentiality and should not upload submissions to AI platforms. If reviewers use AI for organizing feedback or refining their own writing, they must disclose this use just as authors are required to do. This extension of disclosure requirements to reviewers represents an important expansion of ethical guidelines beyond just manuscript authors.

Laher addresses the emerging complexity around citation practices for AI-generated content. She explains that depending on how AI is used, its use might be referenced in the literature review, indicated in the methods section, acknowledged in declarations at the end, or some combination thereof. Guidelines from Sage and COPE emphasize that when AI-generated content is included, authors must provide specific details including the tool’s name, version, and the prompts used.

This article serves as an essential reference for the entire academic community because it distills and synthesizes guidance from multiple authoritative sources into accessible principles that can guide everyday practice. For faculty members serving as mentors, editors, and reviewers, Laher provides the conceptual framework needed to evaluate student and colleague work in an AI-influenced environment. The distinction between AI-assisted and AI-generated content offers faculty a vocabulary for discussing AI use with students, moving conversations beyond simplistic binaries of allowed versus forbidden.

Students benefit from Laher’s practical guidance on disclosure practices. Many students face uncertainty about whether and how to acknowledge AI use in their academic work, sometimes erring on the side of non-disclosure out of fear that any mention of AI will result in penalties. Laher’s article clarifies that routine assistance need not be disclosed, providing reassurance while also establishing clear expectations for when disclosure becomes mandatory. Her recommendation to include AI use statements in acknowledgments sections, in the absence of dedicated declaration sections, gives students a concrete template to follow.

Course designers can use Laher’s framework to develop rubrics and assignment guidelines that explicitly address AI use. By incorporating the AI-assisted versus AI-generated distinction into assignment parameters, designers can help students understand expectations before they begin work. The article’s emphasis on transparency suggests that course materials should include explicit sections on appropriate AI use and disclosure requirements, normalizing these conversations rather than treating them as exceptional circumstances.

For administrators developing institutional policies, Laher’s synthesis of guidelines from multiple major academic organizations provides evidence that a consensus is emerging around core principles even as specific practices continue to evolve. Administrators can feel confident establishing policies around transparency and author accountability while acknowledging that specific disclosure practices and acceptable use cases will continue to develop. The article’s recognition that journal templates currently lack standardized declaration sections highlights the need for institutions to work with publishers to develop such standards.

The article also matters for the credibility of academic publishing itself. As Laher notes, transparency in AI use protects the credibility of all published knowledge, affecting not just academics but journalists, policymakers, educators, and the public who rely on scholarly work. Administrators and faculty who internalize and implement Laher’s guidance help maintain public trust in academic work during a period of significant technological disruption.

3. “Students-Generative AI Interaction Patterns and Its Impact on Academic Writing”

Article Information:

This empirical study argues that the effectiveness of generative AI in academic writing depends significantly on students’ AI literacy levels, with high-literacy students demonstrating collaborative interaction patterns that enhance writing performance while low-literacy students show limited engagement and correspondingly poorer outcomes.

The authors frame their research question by noting that “there is limited understanding regarding the nature of interactions between different types of students, what behavioral patterns students exhibit during a student-GenAI interaction on a given task, and how these different SAI patterns relate to the actual writing task performance.” Their findings reveal a striking difference: “students with a high level of AI literacy exhibited a collaborative approach to SAI, actively accepting GenAI’s suggestions and engaging GenAI in meta-cognitive-related activities such as planning, whereas students with a low level of AI literacy demonstrated much less interaction with GenAI in completing their writing tasks, instead choosing to ideate and evaluate independently.”

Kim and colleagues employ a rigorous mixed-methods design combining think-aloud protocols, screen recordings, and chat histories from thirty-six Chinese graduate students working with ChatGPT on academic writing tasks. This multi-source approach allows them to triangulate behavioral data with performance outcomes, providing unusually rich evidence about how different students actually interact with AI systems. Their use of epistemic network analysis to reveal distinctive interaction patterns represents a methodological advance over simple pre-post comparisons, allowing them to map the relationships between different types of student-AI interactions.

The study’s central finding concerns the role of AI literacy as a mediating variable that fundamentally shapes how students engage with generative AI tools. Students with high AI literacy levels understood how to formulate effective prompts, evaluate AI suggestions critically, and engage the tool in higher-order thinking processes. These students treated the AI as a collaborative partner in planning, drafting, and revising their work. They demonstrated metacognitive awareness about when to accept AI suggestions, when to reject them, and how to use the tool strategically to support their own thinking rather than replace it.

In contrast, students with low AI literacy levels struggled to effectively engage with the AI system. Rather than viewing it as a collaborative tool, they either over-relied on it by accepting suggestions uncritically or under-utilized it by working independently and seeking minimal interaction. These students showed limited understanding of how to craft prompts that would elicit useful responses, how to evaluate the quality and relevance of AI-generated content, or how to integrate AI assistance into their writing process strategically.

The performance data provides empirical validation for these observed differences in interaction patterns. Using established scoring rubrics for Chinese academic writing, the researchers found statistically significant differences between the high and low AI literacy groups across all evaluation criteria including content, structure and organization, and expression. These results suggest that simply providing students with access to AI tools does not guarantee improved outcomes; rather, effectiveness depends on students’ ability to interact with these tools productively.

The authors situate their findings within broader discussions of AI-assisted learning, arguing that their results have implications for both the design of AI writing assistants and the pedagogy of AI-assisted instruction. They suggest that AI systems should be designed to scaffold different types of users, providing more guidance and support for novice users while allowing experienced users greater flexibility and control. From a pedagogical perspective, their findings suggest that institutions cannot simply integrate AI tools into writing courses without also developing students’ AI literacy.

This article matters immensely for faculty because it provides empirical evidence that access alone does not equal equity. The finding that low-literacy students derive minimal benefit from AI tools while high-literacy students show substantial gains suggests that unrestricted AI availability may actually widen rather than narrow achievement gaps. Faculty need to recognize that effective AI use is a learned skill requiring explicit instruction, not an intuitive ability that students will develop automatically. This understanding should inform how faculty introduce AI tools in their courses, suggesting the need for scaffolded instruction in AI literacy before students engage with AI-assisted writing assignments.

For students, particularly those who may lack technical backgrounds or prior AI experience, this article highlights the importance of developing AI literacy as a fundamental academic skill. Students should understand that knowing how to use AI effectively constitutes a form of literacy comparable to information literacy or digital literacy. The research suggests that students who invest time in learning how to formulate effective prompts, evaluate AI outputs critically, and integrate AI assistance strategically will gain significant advantages in their academic work.

Course designers can use these findings to structure writing courses that explicitly develop AI literacy alongside traditional writing skills. Rather than assuming students know how to use AI effectively, designers should incorporate lessons on prompt engineering, critical evaluation of AI outputs, and strategic integration of AI assistance into the writing process. The study’s emphasis on metacognitive engagement with AI suggests that courses should include reflection activities where students articulate their decision-making processes when working with AI tools.

For administrators and academic support staff, this research highlights the need for institutional investment in AI literacy development programs. The significant performance gaps between high and low AI literacy students suggest that institutions serious about educational equity must ensure all students have opportunities to develop these skills. This might include workshops offered through writing centers, modules integrated into first-year composition courses, or online resources available to all students. The research also suggests that assumptions about digital natives being naturally competent with AI are misguided; explicit instruction remains necessary.

The article also has implications for assessment practices. If students with different AI literacy levels produce dramatically different quality work when using AI tools, faculty need to consider whether their assessments appropriately account for this variable. Some institutions may need to explicitly assess AI literacy as a learning outcome, while others may need to structure assessments to minimize the impact of differential AI competence on student grades.

4. “The Role of AI-Assisted Learning in Academic Writing: A Mixed-Methods Study on Chinese as a Second Language Students”

Article Information:

This study demonstrates that AI-assisted learning using ChatGPT can enhance academic writing outcomes for second language learners by supporting knowledge acquisition, creating supportive learning environments, and increasing motivation, though concerns about over-reliance, ethical issues, and content reliability require pedagogical attention.

Gong frames the potential of AI by explaining that “AI-assisted learning can enhance student outcome by supporting knowledge acquisition, helping to create a supportive learning environment, and increasing student motivation.” However, he balances this optimism with caution, noting that “this study also highlights concerns regarding over-reliance on AI, particularly in relation to ethical concerns, technical and networking issues, and the unreliability of AI-generated content.”

Gong’s research employs a rigorous experimental design comparing fifty international Chinese as a Second Language students randomly assigned to either an AI-assisted learning group using ChatGPT or a traditional learning control group. This randomized controlled approach provides strong evidence for causal claims about AI’s effectiveness. The sixteen-week duration of the academic writing course allowed sufficient time for students to develop comfort with AI tools and for effects to manifest, strengthening the study’s ecological validity compared to shorter interventions.

The quantitative findings demonstrate statistically significant improvements in writing quality for the experimental group compared to the control group. Using established scoring rubrics for Chinese academic writing, Gong found improvements across multiple dimensions of writing quality. These results provide important evidence that AI assistance can benefit second language learners who face the dual challenge of mastering both academic writing conventions and language proficiency simultaneously.

The qualitative component, drawing from interviews with six experimental group participants, reveals the mechanisms through which AI-assisted learning benefits students. Students described ChatGPT as a patient tutor available around the clock, providing immediate feedback without the judgment or time constraints that sometimes characterize human instructor interactions. International students particularly valued AI’s ability to explain Chinese language conventions and academic writing norms in their native languages, facilitating understanding that might be difficult to achieve through Chinese-only instruction.

The study identifies three primary ways AI enhanced learning. First, it supported knowledge acquisition by providing explanations of unfamiliar concepts, clarifying grammar and syntax rules, and offering examples of appropriate academic language. Second, it helped create a supportive learning environment where students felt comfortable making mistakes and asking questions repeatedly without fear of frustration or judgment. Third, it increased motivation by making the writing process feel more manageable and providing encouragement when students felt stuck.

However, Gong’s research also surfaces important concerns that complicate simplistic narratives about AI as educational panacea. Interview participants expressed worries about becoming overly dependent on AI assistance, potentially hampering their ability to write independently. Students noted instances where ChatGPT generated inaccurate information or inappropriate language, requiring them to develop critical evaluation skills. Technical issues including network connectivity problems and occasional system outages created frustration and disrupted learning processes. Ethical concerns about whether AI use in assignments constituted a form of academic dishonesty troubled some students even when explicitly permitted by instructors.

This article holds particular significance for faculty working with international students and second language learners. The findings suggest that AI tools may offer special benefits for students navigating the challenges of academic writing in a non-native language, potentially helping to level the playing field between native and non-native speakers. Faculty should recognize that multilingual students face cognitive demands that monolingual students do not, and AI assistance may help manage these additional burdens without lowering academic standards.

For students, particularly international students and English language learners, this research validates AI as a legitimate learning support tool while also highlighting the importance of maintaining independent writing skills. Students should understand that AI works best as a supplement to, rather than substitute for, traditional learning methods. The study’s findings about increased motivation suggest that students struggling with writing confidence might benefit from strategic AI use to build self-efficacy before transitioning to more independent work.

Course designers can extract several important principles from this research. First, AI-assisted writing courses should include explicit instruction on critical evaluation of AI-generated content, given concerns about reliability. Second, assignments should be structured to prevent over-reliance by requiring students to demonstrate independent thinking and critical analysis that AI cannot replicate. Third, courses should acknowledge and address ethical concerns proactively, helping students understand what constitutes appropriate versus inappropriate AI use in their specific institutional context.

For administrators, this research highlights both opportunities and infrastructure requirements. The positive outcomes suggest that institutions serving significant populations of international students should consider strategic investments in AI writing tools and associated support services. However, the technical issues reported by students underscore the importance of reliable technology infrastructure; AI tools that frequently malfunction or require strong internet connectivity may exacerbate rather than reduce educational inequities.

The study also has implications for writing centers and academic support services. Staff should be trained to help students use AI tools effectively and ethically rather than positioning themselves as gatekeepers who prevent AI use. Writing consultants need skills in prompt engineering, AI output evaluation, and strategies for helping students maintain agency in AI-assisted writing processes.

Finally, this research matters for ongoing policy development around AI in education. The simultaneous evidence of both benefits and risks suggests that blanket bans on AI use may deprive students of valuable learning supports, while unrestricted access without guidance may lead to over-reliance and ethical violations. Policies need to be nuanced, acknowledging that AI can serve legitimate educational purposes while requiring appropriate oversight and support structures.

5. Integrating Generative AI into Writing Instruction: A Cognitive Apprenticeship Approach to Navigating Technology and Pedagogy

Article Information:

  • Title: “Integrating Generative AI into writing instruction: A cognitive apprenticeship approach to navigating technology and pedagogy”1
  • Authors: J. Alcruz and M.K. Schroeder
  • Journal: Theory Into Practice
  • Publication Date: July 21, 2025
  • DOI: 10.1080/00405841.2025.2528542

This article argues that generative AI should be integrated into writing instruction through a cognitive apprenticeship framework that helps students develop critical thinking, creative capabilities, and ethical foundations for AI use, positioning educators as mentors who guide students in navigating the complex landscape of AI-assisted writing.

The authors frame their approach by explaining that their article “explores innovative strategies for educators to harness the potential of generative AI in writing instruction. Specifically, it focuses on pedagogical approaches teachers can use to help students navigate the complex landscape of AI while supporting their creative and critical thinking and instilling a firm ethical foundation in using AI.” They ground their recommendations in established educational theory, noting that “we anchor the strategies in the well-established educational theory of cognitive apprenticeship.”

Alcruz and Schroeder build their pedagogical framework on cognitive apprenticeship theory, which emphasizes learning through guided practice and modeling in authentic contexts. This theoretical foundation distinguishes their approach from purely technical guides to AI tool usage. Rather than focusing primarily on what AI can do, they concentrate on how educators can structure learning experiences that develop students’ capacities to work with AI effectively, ethically, and thoughtfully. This pedagogical orientation represents an important contribution at a time when much discussion of AI in education focuses on technology capabilities rather than teaching strategies.

The cognitive apprenticeship framework they propose includes six key elements adapted for the AI context. First is modeling, where instructors demonstrate their own thinking processes when working with AI tools, making visible the questions they ask, decisions they make, and evaluations they conduct. Second is coaching, where instructors provide real-time guidance as students practice using AI tools, offering feedback and suggestions that help students refine their approaches. Third is scaffolding, where instructors provide structured support that gradually decreases as students develop competence. Fourth is articulation, where students explain their reasoning and decision-making processes, developing metacognitive awareness about their AI use. Fifth is reflection, where students compare their work processes and outcomes with others, identifying strengths and areas for improvement. Sixth is exploration, where students are encouraged to push boundaries and experiment with AI tools in ways that extend beyond instructor demonstrations.

The authors emphasize the importance of developing critical thinking alongside technical skills. They argue that students need to learn to evaluate AI outputs skeptically, recognizing that fluent text does not necessarily indicate accurate content. This critical stance requires explicit instruction; students do not naturally develop it simply through AI exposure. The authors suggest specific pedagogical strategies for building critical evaluation skills, including having students compare AI outputs from different tools, verify AI claims against authoritative sources, and identify instances where AI responses are plausible but incorrect.

Ethical considerations receive sustained attention throughout the article. Alcruz and Schroeder recognize that many students face genuine confusion about what constitutes appropriate AI use, often receiving mixed or contradictory messages from different instructors and institutions. Rather than leaving students to navigate these ambiguities alone, they argue that writing instructors have a responsibility to engage students in substantive discussions about ethics, helping them develop frameworks for ethical decision-making rather than simply memorizing rules. These discussions should address questions of authorship, intellectual property, academic integrity, and the social implications of AI-generated content.

The authors also address concerns about creativity and originality in an AI-enabled environment. They argue that rather than seeing AI as a threat to creativity, educators should help students understand AI as a tool that can support creative processes when used thoughtfully. This might include using AI for brainstorming, overcoming writer’s block, or generating alternatives to consider. The key is maintaining student agency and ensuring that AI serves students’ creative intentions rather than determining them.

This article provides faculty with a coherent pedagogical framework grounded in established learning theory, offering an alternative to both technophobic rejection of AI and uncritical embrace of it. The cognitive apprenticeship model gives instructors a structured approach to teaching with AI that honors time-tested principles of effective instruction while addressing novel challenges. Faculty who may feel overwhelmed by AI’s rapid evolution can use this framework to organize their thinking and develop consistent teaching approaches across their courses.

The emphasis on modeling is particularly important for faculty. Many instructors worry that demonstrating AI use might encourage student over-reliance, but Alcruz and Schroeder argue that modeling expert AI use actually helps students understand the difference between thoughtful and thoughtless AI engagement. By making their own reasoning visible, instructors help students develop the metacognitive skills necessary for effective AI use. This shifts the instructor’s role from gatekeeper preventing AI use to mentor guiding appropriate use.

For students, this approach promises more coherent and supportive learning experiences than they currently receive in many institutions. Rather than navigating contradictory policies and expectations alone, students working with instructors who employ cognitive apprenticeship methods receive explicit instruction in both technical skills and ethical reasoning. The emphasis on articulation and reflection helps students develop conscious awareness of their own learning processes, which serves them well beyond any particular AI tool or course.

Course designers can use the six elements of cognitive apprenticeship to structure entire courses or specific units focused on AI-assisted writing. Each element suggests specific activities and assignments: modeling might involve instructor demonstrations or analysis of expert AI use; coaching suggests real-time workshops where instructors circulate and provide guidance; scaffolding implies sequenced assignments that gradually increase in complexity and decrease in support; articulation could involve reflective writing or peer discussions about AI use strategies; reflection might include comparative analysis of different approaches; and exploration could culminate in final projects where students develop their own innovative applications of AI tools.

For administrators, this article suggests the importance of supporting faculty development in pedagogical approaches to AI rather than focusing solely on policy development. While clear policies matter, effective implementation requires faculty who understand how to teach with AI thoughtfully. Administrators should consider investing in workshops or learning communities where faculty can explore cognitive apprenticeship approaches and share strategies for addressing common challenges. The article also highlights the need for institutional consistency in messaging about AI ethics; when students receive contradictory messages from different instructors, they struggle to develop coherent ethical frameworks.

The article matters for the future of writing instruction more broadly. As AI capabilities continue advancing, writing pedagogy must evolve beyond teaching discrete skills toward developing students’ capacities for working with AI tools as collaborative partners. The cognitive apprenticeship framework offers a path forward that preserves the essential human dimensions of writing—creativity, critical thinking, ethical reasoning—while acknowledging that the context in which these capacities are developed has fundamentally changed. Rather than nostalgically attempting to preserve pre-AI writing instruction intact, Alcruz and Schroeder offer a vision for how writing instruction can evolve to remain relevant and effective in an AI-enabled world.

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1 This is a Taylor & Francis journal, so access may depend on your institution’s subscription or you may need to purchase the article individually if you don’t have access through your institution.

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