AI evidence extraction

Moving Academic Writing Forward with Artificial Intelligence in English Language Teaching: A Pedagogical Model from a Systematic Literature Review

Alyana Jane A. Nuluddin, Arminin M. Ratag, Al-johar C. Macam, Luciela J. Jailani, Bonjovi H. Hajan, Nilo J. Castulo et al. · International Journal of Learning Teaching and Educational Research · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.26803/ijlter.25.6.45

Methodology & findings

Study design

Systematic Literature Review (SLR) following PRISMA framework.

Sample

N = 20, 3 groups

Primary method

Inductive thematic analysis of 20 studies; PRISMA framework used for study identification, selection, and screening.

Main result

The study found that "AI tools support writing quality through improved structure, syntax, language use, and cognitive engagement. They also function as agents for guided learning, personalized support, and feedback enhancement." However, the review also identified persistent challenges including "factual inaccuracy, over-reliance, plagiarism risks, reduced authenticity, constrained creativity, unequal access, fragmented pedagogical use, and limited AI literacy."

Reports effect sizes.

Research paradigm

Interpretive/qualitative synthesis

Author conclusions

The authors conclude that "AI tools such as ChatGPT offer more benefits than drawbacks in English academic writing" and propose "a pedagogical model for integrating AI in English academic writing. The model advocates for a balanced academic writing agency through an interaction between pedagogy, critical digital literacy, and personalized learning within a situated institutional context."

Risk of bias

Database limitation: only Scopus and Web of Science searched; potentially missing grey literature or other academic databases; Temporal scope bias: restricted to 2019-2024 publications; Publication bias: systematic reviews may over-represent published positive results; Language bias: likely limited to English-language publications given the databases and topic; Selection bias: Limited to Scopus and Web of Science databases; may exclude relevant studies in other databases; Time-bound search: 2019-2024 window may not capture earlier foundational work; Language bias: Likely English-language publications only (implicit from focus on academic writing in English); Publication bias: Systematic reviews typically favor published studies

Open questions raised

The abstract indicates a gap in "structured model for its pedagogical integration in academic writing," which the systematic review aimed to address through synthesis of evidence on AI impacts, integration best practices, and challenges.
The authors identify a gap: "there is a dire need to establish a structured model for its pedagogical integration in academic writing," which motivated their systematic review to synthesize evidence on AI impacts and integration best practices.
The authors identify "a dire need to establish a structured model for its pedagogical integration in academic writing" and address this by proposing a pedagogical model that balances agency, pedagogy, critical digital literacy, and personalized learning within situated institutional contexts.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 72%

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