Exploring students' perspectives on generative AI-assisted academic writing
Open accessAbstract
The rapid development of generative artificial intelligence (GenAI), including large language models (LLM), has merged to support students in their academic writing process. Keeping pace with the technical and educational landscape requires careful consideration of the opportunities and challenges that GenAI-assisted systems create within education. This serves as a useful and necessary starting point for fully leveraging its potential for learning and teaching. Hence, it is crucial to gather insights from diverse perspectives and use cases from actual users, particularly the unique voices and needs of student-users. Therefore, this study explored and examined students' perceptions and experiences about GenAI-assisted academic writing by conducting in-depth interviews with 20 Chinese students in higher education after completing academic writing tasks using a ChatGPT4-embedded writing system developed by the research team. The study found that students expected AI to serve multiple roles, including multi-tasking writing assistant, virtual tutor, and digital peer to support multifaceted writing processes and performance. Students perceived that GenAI-assisted writing could benefit them in three areas including the writing process, performance, and their affective domain. Meanwhile, they also identified AI-related, student-related, and task-related challenges that were experienced during the GenAI-assisted writing activity. These findings contribute to a more nuanced understanding of GenAI's impact on academic writing that is inclusive of student perspectives, offering implications for educational AI design and instructional design.
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AI-mediated scaffolding can externalize metacognition by prompting learners to justify thesis statements, reflect on evidence quality, and evaluate rhetorical effectiveness.
“Research suggests that AI-mediated scaffolding can externalize metacognition by prompting learners to justify thesis statements, reflect on evidence quality, and evaluate rhetorical effectiveness (Kim et al., 2025).”
From Generative AI, visual scaffolds, and metacognition in EFL writing (2026) · cites this paper as Kim et al., 2025
The Conclusion proposes that GenAI could nudge students toward critical thinking, and the Findings and discussion section (4.2) reports that students perceived GenAI feedback as helping them reflect on and revise their writing. Our copy does not describe prompts asking learners to justify thesis statements, assess evidence quality, or evaluate rhetorical effectiveness specifically. No page numbers are available in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIntegrating GAI into the VGP framework creates a complementary design: visual scaffolds provide macro-structural guidance, while GAI functions as a dialogic partner offering micro-level personalized feedback.
“Integrating GAI into the VGP framework creates a complementary design: visual scaffolds provide macro-structural guidance, while GAI functions as a dialogic partner offering micro-level personalized feedback (Asad et al., 2024;Kim et al., 2025).”
From Generative AI, visual scaffolds, and metacognition in EFL writing (2026) · cites this paper as Kim et al., 2025
Section 4.1.3 describes GenAI as a responsive digital peer, and section 4.2 reports instantaneous feedback that students used to revise their writing. The paper does not describe a VGP framework, visual scaffolds, or the claimed complementary division between macro-structural guidance and micro-level feedback. Its discussion of limited contextual understanding also qualifies the claim of personalized feedback. (LLM, conf=0.86)
Experimental — accuracy not yet measuredKim et al. showed GAI as an effective learning support, providing continuous feedback and emotional support.
“This aligns with Kim et al. [38], showing GAI as an effective learning support, providing continuous feedback and emotional support.”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [38]
Section 4.1 describes students’ expectation that GenAI could provide continuous, on-demand tutoring and real-time feedback, supporting the learning-support part as a perceived potential rather than demonstrated effectiveness. Section 4.3 says students found ChatGPT incapable of offering motivational and emotional support, contrary to that part of the claim. No page numbers were provided in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredEFL students often struggle to move beyond surface-level mechanics and engage in the deep planning, monitoring, and evaluation essential for effective composition.
“Consequently, EFL students often struggle to move beyond surface-level mechanics and engage in the deep planning, monitoring, and evaluation essential for effective composition (Kim et al., 2025;Taye & Mengesha, 2024).”
From Generative AI, visual scaffolds, and metacognition in EFL writing (2026) · cites this paper as Kim et al., 2025
The Introduction says academic writing requires goal setting and other cognitive skills and can be especially challenging for ESL students. Findings §4.3 reports difficulty critically evaluating AI-generated content and developing arguments, but the paper does not establish that EFL students generally remain focused on surface-level mechanics or struggle with planning and monitoring; §4.2 instead describes students valuing GenAI's planning support. No page numbers are available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI functions as an ideational scaffold during planning stages: by generating alternative outlines, proposing argumentative structures, and modeling genre conventions, AI expands learners' rhetorical options.
“Second, AI functions as an ideational scaffold during planning stages: by generating alternative outlines, proposing argumentative structures, and modeling genre conventions, AI expands learners’ rhetorical options—particularly valuable for those lacking sufficient exposure to academic writing schemas (Deep and Chen, 2025; Kim et al., 2024; Reza et al., 2025; Wang, 2024).”
From From tool to scaffold: structured human–AI collaboration and its effects on academic writing and digital critical thinking among Saudi EFL learners (2026) · cites this paper as Kim et al., 2024
In Findings and discussion §4.2 (page unavailable), students report that GenAI helps them plan outlines, organize main points, and create a coherent writing structure; §4.1 describes it as a facilitator of ideas and different perspectives. The paper does not establish that it generates alternative outlines or models genre conventions, nor that this support is particularly valuable for learners with limited exposure to academic writing schemas. These are reported student perceptions, not demonstrated effects on rhetorical options. (LLM, conf=0.84)
Experimental — accuracy not yet measured
It helped students build confidence by breaking down larger tasks into more manageable parts and a gratitude to GAI for acting as a guide and sounding board was expressed.
“It helped students build confidence by breaking down larger tasks into more manageable parts and a gratitude to GAI for acting as a guide and sounding board was expressed [13, 38].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [38]
Students expect AI to fulfil multiple roles, such as multi-tasking writing assistant, virtual tutor and digital peer, to support multifaceted writing processes and performance.
“Some studies examined students' perceptions of using these tools during the writing process (Kim et al., 2025;Wang, 2024), finding that students expect AI to fulfil multiple roles, such as multi-tasking writing assistant, virtual tutor and digital peer, to support multifaceted writing processes and performance.”
From Ink and algorithm: Exploring temporal dynamics in generative <scp>AI</scp> ‐assisted writing (2026) · cites this paper as Kim et al., 2025
AI writing tools can improve grammar, coherence, and argumentation in academic writing.
“AI-assisted writing tools can substantially improve grammar, coherence, and argumentation, making writing more accessible and efficient [2,28].”
From The Role of AI in Academic Writing: Impacts on Writing Skills, Critical Thinking, and Integrity in Higher Education (2025) · cites this paper as [28]
Over-reliance on AI tools can lead to diminished critical thinking, creativity, and independent problem-solving abilities.
“A primary concern among educators is the potential for students' overreliance on AI-generated suggestions, which may lead to factual inaccuracies, diminished critical thinking, and a decline in independent problem-solving abilities [15].”
From The Role of AI in Academic Writing: Impacts on Writing Skills, Critical Thinking, and Integrity in Higher Education (2025) · cites this paper as [28]
Some AI-generated content may be based on pre-existing data and training sets that include inaccuracies or biased perspectives, leading to concerns that AI models could inadvertently produce responses resembling previously published or even discredited materials.
“Furthermore, some AI-generated content may be based on pre-existing data and training sets that include inaccuracies or biased perspectives, leading to concerns that AI models could inadvertently produce responses that resemble previously published or even discredited materials.”
From The Role of AI in Academic Writing: Impacts on Writing Skills, Critical Thinking, and Integrity in Higher Education (2025) · cites this paper as [28]
Students viewed AI as a multi-tasking assistant, virtual tutor, and digital peer; AI improved writing efficiency and organization but raised concerns about accuracy, biases, and ethical use.
“Students viewed AI as a multi-tasking assistant, virtual tutor, and digital peer in writing.AI improved writing efficiency and organization but raised concerns about accuracy, biases, and ethical use.”
From The Role of AI in Academic Writing: Impacts on Writing Skills, Critical Thinking, and Integrity in Higher Education (2025) · cites this paper as [28]
AI writing tools cannot fully understand context and nuanced language, including idiomatic expressions, tonal shifts, and creative or subjective writing.
“This is because AI tools cannot fully understand specific nuances of argumentation, tone, and discipline-specific writing conventions.”
From The Role of AI in Academic Writing: Impacts on Writing Skills, Critical Thinking, and Integrity in Higher Education (2025) · cites this paper as [28]
Exercises where students analyze AI-generated content to identify strengths and weaknesses enhance their critical thinking and analytical skills to further improve their writing.
“Moreover, educators can incorporate exercises where students analyze AI-generated content to identify strengths and weaknesses, enhancing their critical thinking and analytical skills to further improve their writing [28].”
From The Role of AI in Academic Writing: Impacts on Writing Skills, Critical Thinking, and Integrity in Higher Education (2025) · cites this paper as [28]
AI tools can provide students with continuous, on-demand assistance, which traditional instruction cannot offer.
“AI tools can provide students with continuous, ondemand assistance, which traditional instruction cannot offer [28].”
From The Role of AI in Academic Writing: Impacts on Writing Skills, Critical Thinking, and Integrity in Higher Education (2025) · cites this paper as [28]
Having students work together to refine their writing and compare AI-generated revisions with peer feedback can expose them to diverse perspectives that contribute to improved writing.
“Having students work together to refine their writing and compare AI-generated revisions with peer feedback can expose them to diverse perspectives that contribute to improved writing [28].”
From The Role of AI in Academic Writing: Impacts on Writing Skills, Critical Thinking, and Integrity in Higher Education (2025) · cites this paper as [28]
First-person evidence captures extended cognition: part of the processing is conducted in networks of tools and corpora, and the subject reallocates effort to interpret and decide, configuring a distributed academic subjectivity.
“First-person evidence ('I can search, filter, and synthesize information from multiple sources simultaneously,' score 126.65) captures extended cognition: part of the processing is conducted in networks of tools and corpora, and the subject reallocates effort to interpret and decide, configuring a distributed academic subjectivity (Kim et al., 2025).”
From From Digital Natives to AI Natives: Emerging Competencies and Media and Information Literacy in Higher Education (2025) · cites this paper as Kim et al., 2025
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“This aligns with Kim et al. [38], showing GAI as an effective learning support, providing continuous feed-back and emotional support.”
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“…alternative outlines, proposing argumentative structures, and modeling genre conventions, AI expands learners’ rhetorical options—particularly valuable for those lacking sufficient exposure to academic writing schemas (Deep and Chen, 2025; Kim et al., 2024; Reza et al., 2025; Wang, 2024).”
A Systematic Review of Ethical Integration of Generative AI in Higher Education
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“Consequently, EFL students often struggle to move beyond surface-level mechanics and engage in the deep planning, monitoring, and evaluation essential for effective composition (Kim et al., 2025; Taye & Mengesha, 2024).”
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“…language models, have become widely adopted for idea generation, drafting, outlining, and providing feedback on academic texts (Barrett and Pack, 2023; Cheng et al., 2025; Dergaa et al., 2023; Escalante et al., 2023; Khlaif et al., 2023; Kim et al., 2024; Nguyen et al., 2024; Song and Song, 2023).”
Influential
- Authors
- Kim, J., Yu, S., Detrick, R., & Li, N.
- Affiliations
- Old Dominion University (US)
- University of Liverpool (GB)
- Corresponding author
- Kim, Jinhee
- Li, Na
- Venue
- Education and Information Technologies
- Published
- 2024-07-31
- Volume
- 30
- Issue
- 1
- Pages
- 1265-1300
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4401171937
- Cited by
- 438
- References
- 87
- Citations by year
- 2026: 242 · 2025: 183 · 2024: 12 · 2023: 1
- FWCI
- 164.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Education and Information Technologies (publishedVersion, cc-by)
- ODU Digital Commons (Old Dominion University) (submittedVersion, cc-by)
- Funders
- Xi’an Jiaotong-Liverpool University (RC4AIED202405)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
- Keywords
- generative AI, academic writing, student perceptions, large language models, ChatGPT, writing assistants, virtual tutors, writing process, affective domain, university students, instructional design, Chinese students
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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