These findings support the theoretical perspective that the mere provision of technology is insufficient; the pedagogical value of AI emerges through responsive, personalized, and engaging interaction

The gap

Consensus across the literature

Supporting evidence — 8 representative gaps

• Students' Views on the Use of Artificial Intelligence in Language for Specific Purposes (LSP) Courses (2026) · doi

  As AI continues to evolve, research by Kim and Lee (2024) and Jang et al. (2022) reveals the importance of experiential learning in shaping students' attitudes to AI. Their findings suggest that hands-on experiences with AI can positively influence students' perceptions, adding to their confidence in using AI tools and their understanding of the technology's relevance to their future endeavours. Peng and Wan (2024) emphasize the significance of students' perceptions in determining the success of AI integration, highlighting that positive perceptions can lead to increased acceptance and engagement with AI technologies. This highlights the need for ongoing research into students' attitudes and experiences with AI in language learning contexts. In conclusion, the literature reveals a complex landscape of AI integration in language education, with promising advancements and considerable challenges. As AI technologies continue to develop, ongoing research and careful consideration of ethical implications will be crucial for creating effective and responsible AI-enhanced language learning environments. METHOD The methodology section details the research design, research hypotheses, participants, data collection methods, sample description, and the data analysis approach. This study used a survey design to explore how the Slovenian students view AI use in foreign language learning, and their perception of professional language teachers' influence on learning using AI tools. To this end, an anonymous online survey was distributed via the 1KA web application. The survey included 15 questions: 13 closed- ended questions and two open-ended questions for additional reflections. Following the literature review presented above, the following research hypotheses were formulated: International Journal of Instruction, April 2026 ● Vol.19, No.2 130 Students' Views on the Use of Artificial Intelligence in Language … Hypothesis 1. Respondents' age, gender, and university enrolment influence their use of AI tools for educational purposes. Hypothesis 2. Respondents' age, gender, and university of enrolment impact their perception of professional language teachers’ influence on learning using AI tools.

  Keywords: language learning students influence tools perceptions using survey questions reveals attitudes experiences integration technologies ongoing
• METHODOLOGICAL RECOMMENDATIONS ON THE EFFECTIVE USE OF ARTIFICIAL INTELLIGENCE IN ENGLISH LANGUAGE TEACHING IN UZBEK CLASSES (2026) · doi

  Artificial intelligence has undergone remarkable that meet the needs of today’s learners. Students advancement in recent years, exerting a profound are more engaged with technology-based learning and far-reaching influence across numerous environments, and artificial intelligence provides dimensions of contemporary life, with the opportunities to create interactive, learner- educational sphere being among the most centered, and personalized lessons. AI tools significantly affected. Modern teaching requires can assist teachers in organizing instruction, teachers not only to possess subject knowledge, monitoring learner progress, and providing timely but also to effectively use digital technologies in feedback. the learning process. In this context, artificial intelligence has become an important supportive tool for improving the quality and effectiveness of English language teaching.

  Keywords: artificial intelligence learning learner teaching teachers undergone remarkable meet needs today learners students advancement recent
• Proposed vision for developing an adaptive e-learning environment based on artificial intelligence: a theoretically-grounded framework and its suitability from the perspective of experts (2026) · doi

  educational AI-powered resources on the basis of student progress. Dynamic difficulty adjustment allows content to be adapted to the learner’s level of proficiency. Through the identification of learning styles (visual, auditory, and kinesthetic), content delivery is customized. suggest reduced by automating administrative identify at-risk students and recommend language processing enables AI Real-time assessment enables automated feedback and the grading of assignments and quizzes. Predictive analytics are used interventions. to

  Keywords: content enables educational powered resources basis student progress dynamic culty adjustment allows adapted learner level
• Conceptualizing AI Literacy for Higher Education Learners and implications for Institutes (2026) · doi

  While this study provides a conceptual synthesis and a framework for AI literacy in higher education, its scope and methodology suggest several avenues for future research. The analysis and the resulting 16/22 HEX-AI framework are primarily contextualized within higher education and adult learning, and limited to ”learners”, not educators. This paper, as a conceptual and synthesis study, has primary aim is to integrating the existing literature into a coherent framework rather than empirically validating one. The analysis is deliberately built upon key systematic and scoping reviews and seminal primary studies to engage effectively with a broad scholarly consensus. Finally, the field of AI in education is evolving rapidly, this study captures a critical moment in this evolution, but ongoing scholarly attention is required to examine how emerging AI capabilities, shifting ethical debates, and new pedagogical research further inform and potentially reshape the understanding of AI literacy.

  Keywords: framework education conceptual synthesis literacy higher primary scholarly provides scope methodology suggest several avenues future
• CSE Mentor: an ML-Driven Virtual Mentor for Department (2026) · doi

  virtual agents, and collaborative platforms to enable personalized, scalable, and collaborative mentorship. Proposed an AI-augmented reality- based virtual mentorship system for learning. Methods student include personalized support, contextual learning pathways, real-time feedback, gamification, AI avatars, NLP, and AR technologies to improve understanding of AI concepts. stop using chatbots and examine factors related long-term user to loyalty and retention. Enhance AI personalization, improve multimodal interactions, create hybrid AI-human mentorship models, ensure ethical data cultural practices, and support experiential learning. Policy frameworks and cross- cultural adaptation recommended. inclusivity, emotion

  Keywords: mentorship learning virtual collaborative personalized support improve cultural agents platforms enable scalable proposed augmented reality
• Agentic Engagement with Educational AI Chatbots Among Pre-service Teachers: A Mixed-Method Study in Qatar (2026) · doi

  This study provides valuable insights into pre-service teachers’ engagement with AI chatbots in teacher education, yet some limitations must be acknowledged. First, the sample size of 33 pre- service teachers positions this study as theory-building rather than broadly generalisable, as the aim is to develop conceptual insights. Future research should examine larger and more diverse 20 samples across multiple courses and institutions to enhance external validity. Second, while the study categorises participants into high, medium and low engagement users based on message frequency, it does not account for other contextual factors, such as prior AI experience, motivation or digital literacy, that may influence engagement levels. Future studies should integrate pre- course surveys or additional behavioural tracking to capture these factors. Third, engagement was measured primarily through chatbot interactions and self-reported experiences, which may not fully reflect actual learning outcomes. Future research could incorporate performance-based assessments or longitudinal tracking to examine the long-term impact of AI chatbot engagement on academic achievement. Fourth, Arabic interview transcripts were translated by the first author without a formal validation procedure, which may have introduced unintentional interpretive bias. Despite these limitations, the study offers several strengths, including its mixed-methods approach, which combines learning analytics with in-depth qualitative insights to provide a nuanced understanding of AI-assisted learning. Additionally, the focus on agentic engagement extends traditional engagement frameworks, contributing to emerging research on student-AI interactions in educational settings. As such, the research presented here lays a foundation for future research in this exciting area.

  Keywords: engagement future insights learning service teachers limitations first examine based factors tracking chatbot interactions provides
• Enhancing Methodological Integrity with GenAI: A Multi-case Study of Experiential Learning using Sequential Augmented Analysis (2026) · doi

  The study’s methodological limitations centre on its exploratory, dual-case design and small participant pool, which restricts the generalizability of findings across diverse research contexts. Practically, the intervention's success is contingent upon students having prior QDA foundations and receiving specific training in CORI-f prompt engineering, potentially limiting its immediate application for absolute novices or in settings without institutional support. Furthermore, it is important to note that the chatbot’s free version exhibits greater limitations and biases, which, as specialists have shown (Fleisig et al., 2024), are particularly more pronounced for non-English users. Theoretically, while the study utilizes Human-Centered AI and Experiential Learning, it has a "technological-oriented" focus that treats GenAI primarily as a mediating tool within human- controlled procedures. We think that the interpretation of this data could be further enriched by conceptual lenses acknowledging the complexity of agency re-distribution, where the chatbot is not merely an instrument but possibly an “actor” that reshapes the analytic process. Future research could explore agency to better understand the nature of methodological integrity in human-AI assemblages.

  Keywords: human methodological limitations chatbot agency centre exploratory dual case design small participant pool restricts generalizability
• How Artificial Intelligence Will Shape the Future of Education (2024) · doi

  Looking ahead, AI has the potential to revolutionize education in several ways (5) : Personalized Learning: AI can cater to diverse learning styles and needs, providing step-by-step explanations for complex subjects. Enhanced Research: AI tools, like advanced summarizers, could simplify the process of finding and citing research sources. Tutoring Systems: AI can accommodate each student’s learning style by offering a more efficient and effective way to tutor them. Teacher Support: AI can assist in grading and administrative tasks, although it is unlikely to replace the nuanced role of teachers in fostering social skills and life lessons.

  Keywords: learning step looking ahead potential revolutionize education several ways personalized cater diverse styles needs providing