Augmented Reality forEnhanced Heritage Tourism Planning and Interpretation

Thesis Abstract

Augmented reality (AR) technologies offer transformative potential for heritage tourism by overlaying digitally enhanced narratives onto physical sites, yet sustainable integration into planning and interpretation remains underexplored. This study addresses the problem of fragmented stakeholder engagement, limited interpretive accuracy, and inconsistent visitor experiences at historic sites by examining how AR-driven solutions can harmonize conservation objectives with visitor demand. The aim is to develop, deploy, and evaluate an AR-enabled framework that enhances planning processes for site management and improves interpretive outcomes for diverse visitors. Specific objectives include (1) mapping current planning workflows and interpretive practices at selected archaeological and architectural heritage sites, (2) designing an AR prototype that integrates geo-spatial data, 3D reconstructions, and contextual storytelling aligned with conservation policies, (3) assessing user experience and interpretive efficacy across visitor segments, and (4) formulating a scalable model for AR adoption by heritage managers. A mixed-methods research design combines qualitative and quantitative strands to capture both processual and experiential dimensions. The population comprises heritage sites within a metropolitan region with well-documented archaeological layers and existing digital heritage assets, from which a purposive sample of six sites is selected to ensure variation in scale, conservation status, and visitor demographics. A total of 240 visitors, evenly distributed across sites and stratified by age and prior AR exposure, participate in a controlled field experiment. Data collection instruments include semi-structured interviews with site managers (n=18), focus groups with interpretation staff (n=6), observational checklists during AR-enabled tours, standardized visitor questionnaires measuring perceived authenticity, learning outcomes, and user satisfaction, as well as log data from the AR app capturing dwell time, object interaction counts, and path traversal. Validity and reliability are addressed through triangulation, pilot testing (n=30), and test-retest procedures for survey instruments. Analytical procedures employ thematic analysis for interview and focus group data, with inter-coder reliability checked via Cohen’s kappa. Quantitative data are analyzed using multivariate regression to identify factors predicting visitor learning outcomes and satisfaction, complemented by ANOVA to detect differences across site types and demographic groups. Spatial analysis, including hotspot mapping, leverages GIS to examine correlations between AR usage patterns and site features. A theoretical framework grounded in Activity Theory and the Technology Acceptance Model (TAM) guides interpretation of user interactions, while the Conservation-Interpretation Alignment (CIA) model serves to assess how AR content aligns with conservation goals and visitor learning. Key expected findings include (a) AR-enabled interpretive overlays significantly enhancing visitor engagement and recall of historical content, (b) improved perceived authenticity when AR content explicitly references source material and conservation context, (c) identification of design features—such as tactile interactions, multisensory cues, and guided narratives—that predict higher satisfaction and longer dwell times, and (d) evidence that staff workflows benefit from AR-enabled planning tools that facilitate stakeholder collaboration and scenario testing. The study anticipates variation in effects by visitor age, prior AR experience, and site conservation constraints, informing tailored AR design strategies. The study contributes to knowledge by offering an empirically grounded, scalable framework for AR integration in heritage planning and interpretation that aligns with conservation imperatives while maximizing public engagement and learning. It advances methodological integration of qualitative and quantitative analyses within archaeological tourism contexts and extends existing theories of technology adoption and embodied learning to AR-based heritage experiences. Practical implications include a replicable prototype development protocol, guidelines for content governance and accessibility, and a stepwise implementation plan for heritage managers. The main conclusion posits that AR, when embedded in co-created planning and interpretation processes, can reconcile preservation and visitor educational aspirations, but success hinges on iterative stakeholder involvement, rigorous content curation, and robust evaluation using mixed-methods metrics. Recommendations emphasize stakeholder co-design, ongoing content stewardship, accessibility considerations, and the establishment of standardized evaluation benchmarks for AR-enabled heritage tourism projects.