Sep 2026· Journal of Language Teaching and Research· 0 citations
Abstract
This research presents a systematic review of the use of artificial intelligence (AI) in language education, synthesising evidence on tools used for teaching and learning. The review encompassed empirical, conceptual and review studies identified from key education and language databases and is focused on AI use by language learners and teachers in both formal and non-formal contexts. The review is organised under five main dimensions: (1) stakeholder perceptions and readiness; (2) AI applications and associated technologies; (3) AI tools’ impact on language skills and affective factors; (4) pedagogical integration and instructors’ professional development; and (5) overall affordances/challenges and the future implications. The findings reveal that generative AI and conversational agents are increasingly becoming integral components in language education, utilised by educators to offer personalised feedback, adaptive practice, and student engagement and motivation. Evidence also indicates positive impacts regarding writing quality, oral performance, vocabulary, and academic motivation. However, the integration of AI is not universally beneficial: its value is heavily contingent upon learner proficiency, task design and teacher mediation, coupled with risks of learners’ over-reliance, diminished metalinguistic awareness, anxiety or threats to academic integrity. AI literacy, infrastructural and policy constraints, data privacy and bias, and geographic and linguistic inequities in evidence-based research are the most highlighted challenges necessitating system-wide planning of AI harnessing in education.
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