With the widespread adoption of generative artificial intelligence in English as a Foreign Language (EFL) learning, helping learners use AI effectively to solve language-related problems while maintaining independent judgment and critical thinking has become a key issue in technologyenhanced language education. However, existing studies have largely focused on EFL learners’ acceptance of AI, intentions to use it, and usage behaviors, offering limited explanations of how AI literacy shapes learners’ interactions with AI and relates to critical thinking. To address this gap, the present study drew on self-regulated learning theory and adopted an explanatory sequential mixed-methods design. Questionnaire data from 450 university students with generative AIassisted EFL learning experience were analyzed using structural equation modeling and bootstrapping. Subsequently, 20 participants selected through stratified purposive sampling took part in semi-structured interviews, which were analyzed thematically. Quantitative results showed that AI literacy was positively associated with AI-assisted problem-solving ability and indirectly associated with critical thinking through AI-assisted problem-solving ability and AI reliance. Thematic analysis further revealed how learners translated AI-related knowledge and skills into effective problem-solving practices and how different levels of cognitive control were associated with distinct patterns of AI use and cognitive outcomes. This study deepens the understanding of the internal cognitive mechanisms underlying EFL learners’ adaptation to AI and provides practical implications for the systematic development of AI literacy and critical thinking in EFL 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
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