The issue also highlights several underexplored gaps, including the limited disciplinary adaptation of analytics tools

The gap

Evidence profile

Sourced from the recommendations and future work and limitations and abstract and stated challenges of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 7 journals. Those papers have been cited 13 times in total.

Supporting evidence — 8 representative gaps

• CHUYỂN ĐỔI SƯ PHẠM VÀ QUẢN TRỊ THỂ CHẾ DỰA TRÊN TRÍ TUỆ NHÂN TẠO TẠI CÁC TRƯỜNG ĐẠI HỌC TƯ THỤC CỦA VIỆT NAM: HƯỚNG TỚI HỆ SINH THÁI GIÁO DỤC ĐẠI HỌC SỐ BỀN VỮNG (2026) · Tạp chí Khoa học Trường Đại học Trưng Vương · doi

  algorithms. Based on extensive survey data from 42 institutions, approximately 78.6% of instructors report using at least one AI tool in teaching practice, indicating pervasive integration of AI into pedagogical processes (OECD, 2021, https://www. oecd.org/education/oecd-digital-education-outlook- 2021-589b283f-en.htm). personalized content AI applications are present in: •Automated assessment systems •24/7 learner support chatbots •Predictive learning analytics These applications have moved beyond pilot 14 phases into scaled deployment, establishing a robust data-driven pedagogical model. AI has evolved from an auxiliary technology to the central spine of digital pedagogical transformation. auxiliary tool but is becoming the central axis of digital pedagogical transformation 4.1.2. Impact on Learning and Teaching The impact of AI on learning and teaching processes is profound and transformative, reshaping the interactions between instructors, learners, and educational content. AI enables highly personalized learning pathways by collecting and processing real- time data to optimize study trajectories based on individual competencies and preferences (Siemens, 2005, http://www.itdl.org/Journal/Jan_05/article01. htm).

  generalrecommendations
  Keywords: pedagogical learning teaching oecd digital based instructors tool processes education personalized content applications auxiliary central
• Adopt, adapt, or reject? Analysing student-AI interaction in university curriculum-aligned writing tasks (2026) · Applied Language Sciences · doi

  This small-scale pilot study demonstrates how AI can be integrated into academic writing through structured and reflective design. It offers practical guidance for educators and to build pedagogically grounded exploratory directions for researchers seeking approaches to AI-assisted language and literacy learning. For educators, the study highlights AI literacy development through task design. Structured prompts that require students to justify whether they adopt, adapt, or reject AI suggestions help transform assistance into reflection. By embedding these scaffolds within authentic, curriculum-linked tasks, teachers can cultivate students’ critical awareness and ethical reasoning. These findings also point to the need for professional learning that enables educators to model responsible prompting and engagement with AI tools. For researchers, the innovation offers a replicable methodological model for examining student-AI interaction. The use of AI-interaction logs and thematic coding demonstrates how process-level analysis can capture the dynamics of regulation, evaluation, and adaptation. Future research should extend this approach to explore the longitudinal development of AI literacy and test the scalability of different scaffold types across disciplines. Effective integration of AI in language education requires both pedagogical intentionality and empirical grounding. When guided by structured design and reflective practice, AI can serve as a catalyst for ethical judgment, learner agency, and deeper engagement with academic discourse. Lee et al., Applied Language Sciences, 2026 Page 8 of 10

  generalfuture work
  Keywords: structured design educators language literacy demonstrates academic reflective offers researchers learning development students ethical model
• Assessing the AI Literacy in Higher Education: A Comparative Study (2026) · Dibon Journal of Education · doi

  1. The researchers strongly recommend the development and implementation of a comprehensive AI Policy, informed by the evidence gathered on the current AI literacy levels of students and instructors. 2. The researchers recommend a flexible, adaptable curriculum development approach that integrates AI-related content and ethical considerations across various stages of learning to accommodate the diverse AI literacy levels of students and instructors. 3. To empower students with the skills needed for an AI-driven future, this study recommends incorporating practical exercises and real-world case studies that enable them to apply their AI literacy across diverse contexts. 4. The researchers recommend providing specialised professional development workshops and peer-learning opportunities to further develop their AI literacy, especially in the areas/subdimensions in which they are less confident. 5. Future researchers may employ diverse methodologies beyond self-assessment to gain more accurate AI literacy data, explore the evolving definition and incorporate new dimensions while including perspectives from various educational stakeholders, investigate underlying factors influencing AI literacy, broaden the scope to different educational levels and institutions for greater diversity, apply advanced statistical analyses for nuanced understanding, and integrate qualitative methods like FGDs or interviews for deeper contextual insights. 650 Alangan et al. (2026) Dibon Journal of Education Vol 2, Issue 2 8. CONFLICT OF INTEREST No conflict of interest was reported by the author(s). 9. AI DECLARATION The authors declare that the final content of this manuscript represents the sole intellectual efforts and original contributions of the authors. The authors used Gemini, DeepSeek, and Grammarly for grammar checking and language polishing. All AI- generated content was thoroughly reviewed, fact-checked, and substantially revised by the authors to ensure accuracy, originality, and adherence to academic standards. REFERENCES [1] Abimbola, C., Eden, C. A., Chisom, O. N., & Adeniyi, I. S. (2024). Integrating AI in education: Opportunities, challenges, and ethical considerations. Magna Scientia

  generalrecommendations
  Keywords: literacy researchers authors recommend development levels students content diverse instructors ethical considerations across various learning
• Integrasi Artificial Intelligence dalam Pembelajaran STEM: Praktik Pedagogis, Hasil Belajar, dan Tantangan (2026) · Journal of Authentic Research · doi

  1. Teachers should select AI functions according to learning objectives, require verification, and assess traces of both the process and the product. 2. Schools should establish policies on AI use, data protection, equitable access, and continuing professional development. Journal of Authentic Research, August 2026 Vol. 5, No. 3 | 4941 Sukarma et al. Artificial Intelligence Integration ……… 3. Curriculum developers should integrate AI literacy, data literacy, computational thinking, engineering design, and ethics. 4. Researchers should employ longitudinal designs, transfer tasks, log analysis, and more diverse contexts. 5. Technology developers should provide transparency, teacher control, local- language support, and audit and appeal mechanisms. REFERENCES Aptyka, H., Großschedl, J., & Hartelt, T. (2025). Bugbear or surefire success? Secondary school students’ conceptual learning about evolution with ChatGPT. International Journal of Science Education. Advance online publication. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510 Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118 Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32, 444–452. https://doi.org/10.1007/s10956-023-10039-y Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148 Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8 Day, T. (2023). A preliminary investigation of fake peer-reviewed citations and references generated by ChatGPT. The Professional Geographer, 75(6), 1024– 1035. https://doi.org/10.1080/00330124.2023.2190373 Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846 Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., & Prather, J. (2022). The robots are coming: Exploring the implications of OpenAI Codex on introductory programming. In Proceedings of the 24th Australasian Computing Education Conference (pp. 10–19). ACM. https://doi.or

  generalrecommendations
  Keywords: education https artificial intelligence chatgpt journal international access technology science learning professional developers literacy references
• Artificial Intelligence and Professional Development of School Teachers: Opportunities, Challenges and Future Directions (2026) · Iconic Research and Engineering Journals · doi

  [20] Floridi, L., & Chiriatti, M. (2020). GPT-3: Its limits, and consequences. nature, scope, Minds and Machines, 30 (4), 681–694. [21] Gillani, N., Eynon, R., Osborne, M., & Hjorth, I. (2023). Artificial intelligence and teacher professional learning: Emerging perspectives. Educational Review, 75 (6), 1038–1056. [22] Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers Education: Artificial Intelligence, 1 , 100001. and [23] Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2023). AI literacy: Definition, issues. teaching, evaluation and ethical

  generalfuture work
  Keywords: artificial intelligence issues education floridi chiriatti limits consequences nature scope minds machines gillani eynon osborne
• 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.

  generallimitationsevidence 5/5
  Keywords: framework education conceptual synthesis literacy higher primary scholarly provides scope methodology suggest several avenues future
• A Critical Review of Using Learning Analytics for Formative Assessment: Progress, Pitfalls and Path Forward (2025) · Journal of Computer Assisted Learning · cited 11× · doi

  However, the issue also highlights several underexplored gaps, including the limited disciplinary adaptation of analytics tools, a lack of ongoing student involvement in feedback design, insufficient attention to ethical concerns and the physiological and motivational dimensions of assessment, and a limited understanding of the role of emerging technologies, in particular, Generative AI (GenAI).

  generalabstractevidence 5/5
  Keywords: limited issue highlights several underexplored gaps including disciplinary adaptation analytics tools lack ongoing student involvement
• Special issue editorial: Initial studies on the applications of generative AI in education (2026) · Education and Information Technologies · cited 2× · doi

  The need for educating and training all stakeholders to ensure the rapid and effective use of generative AI in educational institutions worldwide, - The challenge of designing AI tools focused on explainability and leveraging the synergy between AI autonomy and the team's perceived virtuality to optimize learning outcomes, - The need for targeted interventions to encourage students to interact with generative AI in more diverse and relevant ways.

  generalstated challengesevidence 5/5
  Keywords: need educating training all stakeholders ensure rapid effective

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