Integrating Agentic AI and Immersive Technologies into Standardised and Adaptive CEFR-Aligned ESL Learning Environments in Higher Education: A Systematic Review of Prospects and Pitfalls
##plugins.themes.bootstrap3.article.main##
Abstract
This systematic literature review explores the integration of agentic artificial intelligence with immersive technologies in teaching English as a second language (ESL), emphasising the development of standardised, adaptive learning environments in higher education settings. Agentic AI is described as autonomous systems that dynamically personalise learning experiences based on multichannel data, including cognitive, social, and motivational factors, and that have the potential to align with international standards such as the Common European Framework of Reference for Languages (CEFR). The combination of virtual and augmented reality, simulations, and interactive environments creates rich, contextually relevant scenarios that enhance language skills. The role of teachers shifts toward facilitation and mentorship, with them critically assessing technological inputs and adjusting teaching strategies to boost learner engagement and motivation. Ethical and legal considerations regarding data privacy and equity are discussed, with a focus on transparency and cultural inclusiveness. Prospects include improving natural language processing, integrating multimodal data, and enabling predictive adaptations to make learning more personalised and effective. The study underscores the importance of collaboration among researchers, technology developers, higher education stakeholders, and students to create sustainable, inclusive ESL learning environments.
References
References
Alizadeh, M. & Cowie, N. (2024). Investigating the impact of online learning platforms on student engagement and learning outcomes. ASCILITE Publications, 1–10.
Ansor, F., Nugroho, A., Wibowo, S. & Prasetya, D. (2023). Adaptive learning based on artificial intelligence to overcome student academic inequalities. Journal of Social Science Utilizing Technology, 1(4), 202–213. https://doi.org/10.70177/jssut.v1i4.663
Anuyahong, B., Rattanapong, C. & Patcha, S. (2023). Analyzing the impact of artificial intelligence in personalized learning and adaptive assessment in higher education. International Journal of Research and Scientific Innovation, 10(4), 88–99. https://doi.org/10.51244/IJRSI.2023.10412
Bacca-Acosta, J., Baldiris, S., Fabregat, R. & Ávila, C. (2023). Comparative eye-tracking study between a virtual reality system and a desktop environment for learning the prepositions of place in English. CALICO Journal, 40(1), 87–108.
Blikstein, P. & Worsley, M. (2016). Multimodal learning analytics and education data mining: using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220–238. https://doi.org/10.18608/jla.2016.32.11
Brusilovsky, P. & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In: P. Brusilovsky, A. Kobsa & W. Nejdl (eds.), The Adaptive Web (pp. 3–53). Berlin: Springer. https://doi.org/10.1007/978-3-540-72079-9_1
Ch'Ng, L.C., Ting, S.H., Sotheeswari, P. & Md Yunus, M. (2024). Evaluating students' views on the importance and usefulness of CEFR in speaking test. Issues in Language Studies, 13(1), 166–180. https://doi.org/10.33736/ils.6219.2024
Chrysafiadi, K. & Virvou, M. (2013). Student modeling approaches: a literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729. https://doi.org/10.1016/j.eswa.2013.02.007
Council of Europe. (2020). Common European Framework of Reference for Languages: learning, teaching, assessment – companion volume. Strasbourg: Council of Europe Publishing. https://rm.coe.int/common-european-framework-of-reference-for-languages-learning-teaching/16809ea0d4
Critical Appraisal Skills Programme (CASP). (2018). CASP checklists. Oxford: CASP UK. https://casp-uk.net/casp-tools-checklists/
Dalgarno B, Lee MJW. 2010. What are the learning affordances of 3-D virtual environments? British Journal of Educational Technology, 41(1): 10-32. https://doi.org/10.1111/j.1467-8535.2009.01038.x
Donnermann, M., Schaper, P. & Lugrin, B. (2022). Social robots in applied settings: a long-term study on adaptive robotic tutors in higher education. Frontiers in Robotics and AI, 9, 831633. https://doi.org/10.3389/frobt.2022.831633
Fadieieva, L.O. (2023). Adaptive learning: a cluster-based literature review (2011–2022). Educational Technology Quarterly, 2023(3), 319–366. https://doi.org/10.55056/etq.613
Filippone, A., Di Fuccio, R., & De Carlo, M. E. (2025). Virtual English LAB: The Impact of Virtual Worlds on English Language Learning and Life Skills in Higher Education. Excellence and innovation in learning and teaching: research and practices: 10(1), 23–44.
Gay, G. (2018). Culturally responsive teaching: theory, research, and practice (3rd ed.). New York: Teachers College Press.
Ghulam, M., Tanzeela, U. & Muhammad, A. (2024). Role of artificial intelligence for adaptive learning environments in higher education by 2030. Journal of Social Research Development, 5(3), 12–22. https://doi.org/10.53664/JSRD/05-03-2024-02-12-22
Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis Campbell Systematic Reviews, 18, e1230. https://doi.org/10.1002/cl2.1230
Holstein, K., McLaren, B.M. & Aleven, V. (2019). Student learning benefits of a mixed-reality teacher awareness tool in AI-enhanced classrooms. In: Proceedings of the 20th International Conference on Artificial Intelligence in Education. Cham: Springer. https://doi.org/10.1007/978-3-030-23204-7_14
Horwitz, E.K. (2010). Foreign and second language anxiety. Language Teaching, 43(2), 154–167. https://doi.org/10.1017/S026144480999036X
Khan, A. & Mishra, V. (2024a). Adapting to diversity: leveraging AI for ESL learning enhancement. Journal of Advances and Scholarly Researches in Allied Education, 21(3), 188–198. http://www.ignited.in
Khanim Ali, Y., Iqbal, S., Ahmad, M. & Khan, R. (2024). Enhancing ESL education with AI: innovations in teaching methods and authentic materials. International Journal of Membrane Science and Technology, 11(1), 497–505.
Ladson-Billings, G. (2014). Culturally relevant pedagogy 2.0: a.k.a. the remix. Harvard Educational Review, 84(1), 74–84. https://doi.org/10.17763/haer.84.1.p2rj131485484751
Lin, G.Y., Jhang, C.C. & Wang, Y.S. (2024). Factors affecting parental intention to use AI-based social robots for children's ESL learning. Education and Information Technologies, 29(6), 6059–6086. https://doi.org/10.1007/s10639-023-12023-w
Litman, D. (2016). Natural language processing for enhancing teaching and learning. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1), 4170–4176. https://doi.org/10.1609/aaai.v30i1.9879
Luckin, R., Holmes, W., Griffiths, M. & Forcier, L.B. (2016). Intelligence unleashed: an argument for AI in education. London: Pearson. https://www.pearson.com/content/dam/corporate/global/pearson-dot-com/files/innovation/Intelligence-Unleashed-Publication.pdf
Makransky, G. & Petersen, G.B. (2021). The cognitive affective model of immersive learning (CAMIL): a theoretical research-based model of learning in immersive virtual reality. Educational Psychology Review, 33(3), 937–958. https://doi.org/10.1007/s10648-020-09586-2
Moulieswaran, N. & Prasantha Kumar, N.S. (2023). Investigating ESL learners' perception and problem towards artificial intelligence (AI)-assisted English language learning and teaching. World Journal of English Language, 13(5), 290–302. https://doi.org/10.5430/wjel.v13n5p290
Murgatroyd, S. (2024). Artificial intelligence and future of higher education. Revista Paraguaya de Educación a Distancia, 5(1), 4–11. https://doi.org/10.56152/reped2024-vol5num1-art1
Nangunori, S.K. (2024). Database-driven adaptive learning: a systematic analysis of AI integration in educational personalization. International Journal for Multidisciplinary Research, 6(6), 1–15. www.ijfmr.com
North, B. & Piccardo, E. (2016). Developing illustrative descriptors of aspects of mediation for the Common European Framework of Reference (CEFR). Language Teaching, 49(3), 455–459. https://doi.org/10.1017/S0261444816000100
Norshaidatul, M.N., Siti Hajar, H., Melor, M.Y. & Harwati, H. (2021). CEFR for languages and its effective implementation in secondary schools in Malaysia. Asian Journal of Assessment in Teaching and Learning, 11(1), 63–72. https://doi.org/10.37134/ajatel.vol11.1.6.2021
Noviandy, T.R., Maulana, I., Ramadhan, G.A.P., Rizki, J. & Sofyan, H. (2024). Embrace, do not avoid: reimagining higher education with generative artificial intelligence. Journal of Educational Management and Learning, 2(2), 81–91. https://doi.org/10.60084/jeml.v2i2.233
Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hróbjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Pan, S. (2024). Research on teaching strategies of immersive experiential teaching for collaborative learning in elementary and middle schools based on AI and VR. Advances in Educational Technology and Psychology, 8(5), 14–20. https://doi.org/10.23977/aetp.2024.080503
Papamitsiou, Z. & Economides, A.A. (2014). Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64.
Rangavittal, P.B. (2024). Transforming higher education with artificial intelligence: benefits, challenges, and future directions. International Journal of Science and Research, 13(5), 1635–1642. https://doi.org/10.21275/SR24525214415
Ravarini, A., Canavesi, A. & Passerini, K. (2024). From users to allies: exploring educator and generative AI roles in shaping the future of higher education. In: 10th International Conference on Higher Education Advances (HEAd'24). Valencia: Universitat Politècnica de València. https://doi.org/10.4995/HEAd24.2024.17345
Rodriguez, R.V. & Hemachandran, K. (2023). The future of education: exploring AI avatars in higher learning. Qeios. https://doi.org/10.32388/80Z989
Sajja, R., Sermet, Y., Cwiertny, D. & Demir, I. (2024). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information, 15(10), 596. https://doi.org/10.3390/info15100596
Sarwat, S., Khan, M.A., Ahmed, S. & Hussain, Z. (2024). Investigating the perceptions and attitudes of ESL learners towards the use of immersive reader technology in enhancing reading comprehension at the secondary school level. Spry Contemporary Educational Practices, 3(1), 212–228. https://doi.org/10.62681/sprypublishers.scep/3/1/12
Seo, H., Hwang, T., Jung, J., Kang, H., Namgoong, H., Lee, Y. & Jung, S. (2025). Large language models as evaluators in education: verification of feedback consistency and accuracy. Applied Sciences, 15(2), 671. https://doi.org/10.3390/app15020671
Song, J. (2024). Integrating artificial intelligence in smart course design: innovative teaching methods for talent cultivation in higher education. Education Insights, 1(1), 1–12. https://soapubs.com/index.php/EI
Sweller, J., van Merriënboer, J.J.G. & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5
Thomas, J. & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8(1), 45. https://doi.org/10.1186/1471-2288-8-45
Toboula, C.M.Z. (2023). Enhancing post-pandemic EFL education by leveraging immersive, NLP-driven, AI-based tools that promote collaboration and interactivity within an educational approach. International Journal of Education, 11(1), 63–78. https://doi.org/10.5121/ije.2023.11106
Wok Zaki, A. & Darmi, R. (2021). The implementation of CEFR in ESL learning: why does it matter to the Malaysian education system? Asian Journal of Assessment in Teaching and Learning, 11(2), 1–13. https://doi.org/10.37134/ajatel.vol11.2.1.2021
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y. & Gašević, D. (2023). Practical and ethical challenges of large language models in education: a systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370
Downloads
Article Metrics Graph
##plugins.themes.bootstrap3.article.details##
Data Availability Statement
Data Availability Statement
All supplementary documents associated with this study are available on the Open Science Framework (OSF):
Kotze, C. (2026) Integrating agentic AI and immersive technologies into standardised and adaptive CEFR-aligned ESL learning environments in higher education: A systematic review of prospects and pitfalls. 9 December. Available at: https://osf.io/b75mj

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
https://orcid.org/0009-0005-8335-0501