Julkaisuarkisto

Extended Reality (XR) technologies are rapidly reshaping human–computer interaction; however, persistent ambiguity in the use of core terms (VR, AR, MR) hampers cumulative knowledge building, cross-study comparability, and technical standardisation. This review evaluates the XR conceptual landscape across four primary dimensions: the historical evolution of core definitions, the synthesis of contemporary theoretical frameworks, the critical extensions of the Reality-Virtuality (RV) Continuum, and the alignment between academic taxonomies and industry practices. This review evaluates the XR conceptual landscape across four primary dimensions: the historical evolution of core definitions, the synthesis of contemporary theoretical frameworks, the critical extensions of the Reality-Virtuality (RV) Continuum, and the alignment between academic taxonomies and industry practices. To address this issue, we conducted a PRISMA-guided systematic literature review across four major databases (IEEE Xplore, ACM Digital Library, Scopus, and Web of Science), complemented by seminal and industry sources. Of the 173,677 retrieved records, 59 studies were included in the synthesis. Using thematic synthesis, we mapped the historical evolution of definitions and conceptual models and identified recurring analytical dimensions. The results indicate a clear paradigm shift from Milgram’s one-dimensional Reality–Virtuality continuum—originally grounded in visual display technology—towards a multidimensional conceptual space that integrates subjective user-experience constructs (e.g., coherence and plausibility) with objective system characteristics. The included studies cover 1968–2025, with marked acceleration in the 2020s: 2022 alone accounts for the highest annual count (9 studies), and nearly half of the corpus (47.5%) was published in 2021–2025. We further show that industry actors pragmatically re-bound these academic concepts for product and market positioning, leading to systematic divergences between academic and industrial definitions. By distilling key turning points and synthesising core analytical dimensions into a structured lens, this review provides a historically grounded, actionable understanding of the XR conceptual landscape to support terminological alignment across research and practice.

Generative AI (GenAI) is increasingly applied in urban planning for text production, visualization, analytics, stakeholder communication, and participatory engagement. Large language models (LLMs) enable the creation of synthetic participants to support the early-stage design, analysis, and testing of participatory tools. This article demonstrates an innovative use of GenAI through synthetic inhabitants and experts in an immersive digital urban planning environment. DigitalTurku serves as a proof-of-concept for an immersive planning tool within an urban digital twin. The case relies on synthetic personas—residents and expert stakeholders—to evaluate how a GenAI-assisted urban platform may shape participation experiences and trust in local urban planning. The findings indicate that synthetic experts expressed a reduced bureaucratic distance, enhanced transparency, and more meaningful participation. However, assessments of tools and digital environment usability varied according to digital skills and demographic characteristics embedded in the personas. The use of synthetic personas helps identify opportunities and challenges in immersive urban planning environments and supports the design of digital tools in smart cities to strengthen human residents’ spatial understanding and experiential engagement in planning processes. The creation of synthetic data and participants is convenient with LLMs. Despite these tools’ limitations, they can play a valuable role in piloting participatory planning processes to support and complement human-based participation.