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Education Sciences
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Artificial Intelligence and Language Learning: Innovations, Impacts, and Insights
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Artificial Intelligence and Language Learning: Innovations, Impacts, and Insights

A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Language and Literacy Education".

Deadline for manuscript submissions: 15 June 2026 | Viewed by 4546

Special Issue Editor

Dr. Bradford J. Lee

E-Mail Website
Guest Editor
Organization for Fundamental Education, Fukui University of Technology, Fukui 910-8505, Japan
Interests: instructed second language acquisition; learner motivation; multimodal learning; CALL/MALL
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has rapidly transformed the landscape of language education, from intelligent tutoring systems and adaptive learning platforms to generative language models and real-time feedback tools. This has put an unprecedented amount of agency not only in the hands of users, but also in those of instructors, who are creating their own materials more than ever before. This Special Issue invites scholars, educators, technologists, and interdisciplinary researchers to share contributions that critically examine both the innovative potential and the pedagogical, ethical, and sociolinguistic implications of AI integration in language education.

We welcome original research, theoretical papers, case studies, and practitioner reflections. The scope of this Special Issue includes, but is not limited to, the following topics:

  • AI-driven language learning platforms and intelligent tutoring systems;
  • Natural Language Processing (NLP) applications in second language acquisition;
  • Generative AI as a tool for writing, translation, or conversation practice;
  • AI and learner autonomy, motivation, and engagement;
  • Ethical considerations in AI-mediated language instruction;
  • The cross-cultural and multilingual implications of AI in education;
  • Assessment and feedback mechanisms powered by AI;
  • Teacher roles and professional development in AI-enhanced classrooms;
  • Comparative studies of AI adoption across educational contexts.

Dr. Bradford J. Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • learner agency
  • second language acquisition

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

18 pages, 313 KB  
Article
Positioning Generative AI in EFL Peer Feedback: Training Feedback Literacy and Enabling Uptake in Speaking Classes
by Bradley Irwin and Theron Muller
Educ. Sci. 2026, 16(4), 544; https://doi.org/10.3390/educsci16040544 - 1 Apr 2026
Viewed by 906
Abstract
Peer feedback is widely used in English as a foreign language (EFL) higher education, yet its benefits are often limited by uneven feedback quality and learners’ difficulty in interpreting and using comments. This theoretical paper synthesizes research on peer feedback, student feedback literacy, [...] Read more.
Peer feedback is widely used in English as a foreign language (EFL) higher education, yet its benefits are often limited by uneven feedback quality and learners’ difficulty in interpreting and using comments. This theoretical paper synthesizes research on peer feedback, student feedback literacy, and recent developments in generative artificial intelligence (GenAI) to propose a theory-informed design framework that positions GenAI as Trainer and Synthesizer in L2 speaking peer feedback. Building on feedback literacy as a set of capacities (appreciating feedback, making judgments, managing affect, and taking action), the paper argues that speaking tasks create distinct constraints, including time pressure, fleeting performance, and heightened affect, which make real-time peer feedback promising but pedagogically challenging. To address these challenges, here we introduce two complementary roles for GenAI in peer feedback workflows: a Trainer that supports feedback quality through calibration with exemplars, rubric-guided practice, and feedback-on-feedback; and a Synthesizer that aggregates peer input into concise, actionable guidance linked to criteria and learning goals. The conceptual proposal specifies key design principles (e.g., transparency, learner agency, teacher-in-the-loop oversight, and privacy-conscious data practices) and outlines researchable propositions for evaluating learning, engagement, and equity outcomes. The paper concludes with implications for task design, training sequences, and responsible classroom implementation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Language Learning: Innovations, Impacts, and Insights)
17 pages, 263 KB  
Article
Generative AI in Norwegian English Classrooms: Exploring Teacher Adoption Through UTAUT
by Asli Lidice Gokturk-Saglam
Educ. Sci. 2026, 16(3), 391; https://doi.org/10.3390/educsci16030391 - 4 Mar 2026
Viewed by 971
Abstract
Generative Artificial Intelligence (GenAI) has the potential to bring substantial benefits to language education, making it essential to examine how teachers engage with these technologies in practice. This exploratory qualitative case study draws on semi-structured interviews with four in-service upper-secondary English teachers in [...] Read more.
Generative Artificial Intelligence (GenAI) has the potential to bring substantial benefits to language education, making it essential to examine how teachers engage with these technologies in practice. This exploratory qualitative case study draws on semi-structured interviews with four in-service upper-secondary English teachers in Norway to examine the factors shaping their engagement with GenAI. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study examined factors shaping teachers’ engagement with GenAI, including performance expectancy, effort expectancy, social influence, and facilitating conditions. Thematic analysis revealed a pattern of selective, context-sensitive use rather than straightforward adoption. While teachers recognised the potential of GenAI to support planning, idea generation, and formative feedback, their engagement was constrained by concerns about assessment validity, academic integrity, privacy, and institutional guidance. The findings suggest that teachers’ use of GenAI is shaped not only by perceptions of usefulness and ease of use but also by trust, assessment considerations, and the availability of clear policy frameworks. By using UTAUT as a qualitative analytical lens, this study contributes to research on technology acceptance and teacher agency by showing how teachers negotiate the use of GenAI in ways that reshape assessment practices and professional roles. The findings point to the need for clear institutional guidance, AI-resilient assessment practices, and targeted teacher education that supports ethical, pedagogically grounded use of GenAI. Full article
(This article belongs to the Special Issue Artificial Intelligence and Language Learning: Innovations, Impacts, and Insights)
16 pages, 442 KB  
Article
Gender Equity in Wikibook Collaborative Writing Assisted by Multimodal Generative AI Tools: The Case of Hong Kong Undergraduates
by Lixun Wang and Boyuan Ren
Educ. Sci. 2025, 15(12), 1658; https://doi.org/10.3390/educsci15121658 - 9 Dec 2025
Viewed by 846
Abstract
The integration of generative artificial intelligence (AI) tools has become a game-changer in educational practices, particularly in collaborative academic writing. This study explores gender-based disparities in perceptions, emotions, and self-efficacy regarding students’ utilization of AI tools during a collaborative Wikibook writing project. Grounded [...] Read more.
The integration of generative artificial intelligence (AI) tools has become a game-changer in educational practices, particularly in collaborative academic writing. This study explores gender-based disparities in perceptions, emotions, and self-efficacy regarding students’ utilization of AI tools during a collaborative Wikibook writing project. Grounded in the Technology Acceptance Model (TAM), the research investigates how male and female undergraduates in Hong Kong perceive the usefulness and ease of use of ChatGPT 3.5 and Padlet AI image generation function, as well as their emotions and self-efficacy when engaging with these tools. Using a 5-point Likert scale questionnaire and an independent sample t-test, the study compares gender perspectives with a sample size of 140 undergraduates. The results reveal that (1) both genders found the AI tools beneficial for language polishing and essay reconstruction in academic writing; (2) both genders experienced a range of emotions, including enjoyment, satisfaction, frustration, anxiety and tension during the writing task; (3) both male and female students demonstrated AI literacy to critically evaluate AI-generated information. These findings underscore the importance of fostering an equitable and engaging approach to AI-supported learning environments for both genders. The study highlights the benefits of AI tools in enhancing learning outcomes and emphasizes the role of students’ AI literacy in ensuring the responsible and effective use of these tools as learning partners. Full article
(This article belongs to the Special Issue Artificial Intelligence and Language Learning: Innovations, Impacts, and Insights)
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