Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
Abstract: The rapid development of artificial intelligence (AI) has created new opportunities for improving distance English language teaching. AI-based technologies, including intelligent chatbots, automated feedback systems, speech recognition tools, adaptive learning platforms, and generative AI applications, can provide learners with personalized learning experiences, immediate feedback, and additional opportunities for language practice. This thesis examines the pedagogical potential of AI in distance English language education, with particular attention to speaking, writing, vocabulary development, grammar practice, learner autonomy, and individualized instruction. Recent empirical research indicates that AI-assisted English language learning can have a positive effect on learning outcomes. A 2024 meta-analysis covering 40 empirical studies, 3,290 participants, and 55 effect sizes reported a large overall positive effect of AI on English language learning achievement (g = 0.812) [1]. At the same time, the integration of AI into language education raises important concerns related to academic integrity, inaccurate AI-generated information, data privacy, digital inequality, and excessive dependence on technology. Therefore, AI should not be regarded as a replacement for teachers but as an innovative pedagogical tool that can complement teachers’ professional expertise and enhance learners’ opportunities for independent and interactive language practice.
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