Extended Reality (XR) has reshaped how learning can be structured, yet its integration into formal curricula continues to lag behind its technological potential. Established instructional design models such as ADDIE and ASSURE provide stable planning structures, but were not developed to address the spatial, embodied and interactive characteristics of immersive environments. The XR2Learn framework was developed to bridge this gap by combining structured instructional planning with XR-specific pedagogical considerations. This study presents a multi-round Delphi-based expert evaluation of XR2Learn, involving twenty specialists in instructional design and XR-enhanced education. Experts assessed the framework across four dimensions: validity, clarity, usability and suitability. Qualitative feedback was thematically analyzed and subsequently quantified to establish consensus. The findings show strong agreement regarding the framework’s pedagogical grounding, logical structure and alignment with established instructional design practices. At the same time, experts identified limitations related to practical enactment, accessibility and the explicit integration of XR-specific learning constructs such as presence and social interaction. Overall, the results position XR2Learn as a framework at a transitional stage, moving from conceptual formulation toward practical instructional use. The study provides the first systematic expert validation of XR2Learn and outlines targeted directions for its refinement as a robust instructional design framework for XR-based education.

Delphi-Based Expert Evaluation of the XR2Learn Hybrid Instructional Design Framework for XR Education / Karachristos, C., Kouvara, T., Zafeiropoulos, V., Orphanoudakis, T., Anastasakis, G., Antonaci, A., Chatzigiannakis, I., Conte, M.P., Marsico, A., Devole, S., Giordano, S., Besenzoni, M.. - In: COMPUTERS. - ISSN 2073-431X. - 15:2(2026). [10.3390/computers15020131]

Delphi-Based Expert Evaluation of the XR2Learn Hybrid Instructional Design Framework for XR Education

Chatzigiannakis I.
;
2026

Abstract

Extended Reality (XR) has reshaped how learning can be structured, yet its integration into formal curricula continues to lag behind its technological potential. Established instructional design models such as ADDIE and ASSURE provide stable planning structures, but were not developed to address the spatial, embodied and interactive characteristics of immersive environments. The XR2Learn framework was developed to bridge this gap by combining structured instructional planning with XR-specific pedagogical considerations. This study presents a multi-round Delphi-based expert evaluation of XR2Learn, involving twenty specialists in instructional design and XR-enhanced education. Experts assessed the framework across four dimensions: validity, clarity, usability and suitability. Qualitative feedback was thematically analyzed and subsequently quantified to establish consensus. The findings show strong agreement regarding the framework’s pedagogical grounding, logical structure and alignment with established instructional design practices. At the same time, experts identified limitations related to practical enactment, accessibility and the explicit integration of XR-specific learning constructs such as presence and social interaction. Overall, the results position XR2Learn as a framework at a transitional stage, moving from conceptual formulation toward practical instructional use. The study provides the first systematic expert validation of XR2Learn and outlines targeted directions for its refinement as a robust instructional design framework for XR-based education.
2026
Delphi study; educational technology; Extended Reality (XR); immersive learning; instructional design framework; pedagogical innovation
01 Pubblicazione su rivista::01a Articolo in rivista
Delphi-Based Expert Evaluation of the XR2Learn Hybrid Instructional Design Framework for XR Education / Karachristos, C., Kouvara, T., Zafeiropoulos, V., Orphanoudakis, T., Anastasakis, G., Antonaci, A., Chatzigiannakis, I., Conte, M.P., Marsico, A., Devole, S., Giordano, S., Besenzoni, M.. - In: COMPUTERS. - ISSN 2073-431X. - 15:2(2026). [10.3390/computers15020131]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1769956
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