CALL FOR PAPERS: Postdigital Education: Hybrid, Immersive, and Intelligent Approaches in Lifelong Learning

21. 07. 2025
  • Editor: Marko Radovan
  • Abstract deadline (preliminary title and short summary): 31th October 2025
  • Full-paper deadline: 30th April 2026
  • The issue will be published in October 2026 (and in "Online First" upon acceptance)

Background and rationale

The convergence of hybrid learning, extended reality (XR), and artificial intelligence (AI) continues to reshape continuing education, particularly for adult learners balancing professional and personal commitments. Hybrid learning combines in-person instruction with online components, offering flexibility without sacrificing interaction. A comprehensive systematic review by Raes et al. (2020) shows that synchronous hybrid learning (SHL) – where face-to-face and remote learners engage simultaneously – has gained traction in postsecondary education due to its capacity to enhance accessibility and learner engagement. However, the review also highlights significant pedagogical and technological challenges, including difficulties in ensuring equitable participation between on-site and online learners and the lack of professional development for instructors designing hybrid environments.
Simultaneously, XR technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), are bringing immersion and situated cognition to adult learning. Radianti et al. (2020) found that VR simulations can significantly enhance procedural skill acquisition in vocational training contexts, while Hamilton et al. (2021) note that AR overlays improve contextual understanding by situating abstract concepts within real‐world environments. Cognitive-affective models like CAMIL demonstrate that AR/VR immersion fosters presence and motivation, both essential for adult learning (Makransky et al., 2020). 
With all this technological development, artificial intelligence (AI) is also becoming increasingly established in (adult) education. Generative AI models now assist educators in content creation, automated feedback, and formative assessment (Luckin et al., 2022). Intelligent tutoring systems (ITS) leverage learner data to personalize learning pathways; for example, Chen et al. (2020) demonstrated that ITS increased learner persistence and mastery in an online professional development program. Design approaches for human–AI complementarity in educational settings, like the study by Holstein et al. (2018) illustrate that AI systems supporting teacher decision-making and real-time analytics can improve both learning and engagement. Similarly encouraging findings are also reported by the latest studies on the use of generative artificial intelligence (GenAI), such as the study by Su et al. (2023).

Yet, these advances raise ethical and privacy concerns: Williamson and Eynon (2020) highlight the need for transparent data governance frameworks to safeguard adult learners’ autonomy and consent.
Finally, the promise of these technologies must be tempered by considerations of equity and inclusion. Van Deursen and Helsper (2021) document a “third‐level digital divide” in which more advantaged learners derive greater benefit from online tools, suggesting that hybrid‐XR‐AI ecosystems risk deepening existing disparities unless accompanied by targeted accessibility measures. Sato et al. (2024) emphasize that educational institutions must proactively promote inclusive practices and support diverse learners to create an equitable learning environment. Firstly, to ensure equitable access to online education, it is crucial for policymakers to prioritize the development of a robust digital infrastructure and reliable internet connectivity.
Together, these streams of research point to both unparalleled opportunities and complex challenges in reimagining continuing education. By situating hybrid, XR, and AI innovations within coherent pedagogical, ethical, and infrastructural frameworks, this special issue aims to advance scholarship and practice toward more effective, equitable, and sustainable models of lifelong learning.

Themes and topics of interest 
We welcome contributions from interdisciplinary and international perspectives, including but not limited to:

  • Blended and hybrid learning models for flexible adult education delivery

  • Immersive learning through virtual, augmented, and mixed reality (XR)

  • AI as tutor, assessor, or learning companion in lifelong learning

  • Learning analytics and adaptive systems for personalized lifelong learning

  • Digital inclusion and the hybrid divide: equitable access for all learners

  • Teacher readiness and professional development for AI/XR-enhanced teaching

  • Learner engagement and experience in postdigital adult education

  • Workplace learning, upskilling with AI and XR

  • Ethical, legal, and governance issues in AI-mediated learning environments

  • Assessment, feedback, and certification in hybrid and XR settings

Guidelines for Authors

References

Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

Hamilton, D., McKechnie, J., Edgerton, E., & Wilson, C. (2021). Immersive virtual reality as a pedagogical tool in education: a systematic literature review of quantitative learning outcomes and experimental design. Journal of Computers in Education, 8(1), 1–32. https://doi.org/10.1007/s40692-020-00169-2

Luckin, R., Holmes, W., Griffiths, M., Forcier, L. B., & University College, London. (2016). Intelligence unleashed: an argument for AI in education. Pearson.

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

Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778. https://doi.org/10.1016/j.compedu.2019.103778

Raes, A., Detienne, L., Windey, I., & Depaepe, F. (2020). A systematic literature review on synchronous hybrid learning: gaps identified. Learning Environments Research, 23(3), 269–290. https://doi.org/10.1007/s10984-019-09303-z

Sato, S. N., Condes Moreno, E., Rubio-Zarapuz, A., Dalamitros, A. A., Yañez-Sepulveda, R., Tornero-Aguilera, J. F., & Clemente-Suárez, V. J. (2023). Navigating the New Normal: Adapting Online and Distance Learning in the Post-Pandemic Era. Education Sciences, 14(1), 19. https://doi.org/10.3390/educsci14010019

Su, J., & Yang, W. (2023). Unlocking the Power of ChatGPT: A Framework for Applying Generative AI in Education. ECNU Review of Education, 6(3), 355–366. https://doi.org/10.1177/20965311231168423

van, Doursen, A. J. A. M., & Helsper, E. J. (2015). The Third-Level Digital Divide: Who Benefits Most from Being Online? In Communication and Information Technologies Annual (Vol. 10, pp. 29–52). Emerald Group Publishing Limited. https://doi.org/10.1108/S2050-206020150000010002

Van Der Spoel, I., Noroozi, O., Schuurink, E., & Van Ginkel, S. (2020). Teachers’ online teaching expectations and experiences during the Covid19-pandemic in the Netherlands. European Journal of Teacher Education, 43(4), 623–638. https://doi.org/10.1080/02619768.2020.1821185

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995