Sep 2026· ELT Forum Journal of English Language Teaching
Artificial Intelligence in Healthcare and Education
Abstract
The mass adoption of Generative AI has brought a massive landscape shift in academic writing for EFL students. This technology acts as a powerful cognitive scaffold for students on finishing their academic writing tasks such as brainstorming ideas, checking grammar accuracy, paraphrasing a sentence and as sparring partner. Nevertheless, the tools also act as double-edged sword because its comprehensive aid that triggers the scaffolding paradox, cognitive offloading, ethical issue or academic integrity. However, these perspectives remain distributed across several research domains, making it harder to understand how the field is developing as an integrated research area. The study employs a hybrid study design using bibliometric informed scoping review to examine the thematic structure and the development of research on generative AI in EFL academic writing. The research aims to quantitatively map the thematic evolution of AI in EFL writing globally from 430 Scopus-indexed articles from January 2023 to June 2026, and also qualitatively synthesize the condition when AI act as substitute or act as cognitive behavior mechanism of overreliance on using Generative AI, and aims to offer a strategy or framework about practical AI literacy in EFL academic writing. Rather than treating Generative AI as inherently beneficial or harmful, it examines the emerging tension between AI as cognitive assistance and AI as a potential substitute for EFL learners. The study mainly proposes an evidence-informed conceptual framework that conceptualizes effective Generative AI assisted writing as a process in which this tool remains subordinate to learner critical thinking and judgment.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026