Skip to content

Digital echoes of the past: how cultural proximity shapes AI- and VR-mediated heritage experiences

Sep 2026 · Journal of Hospitality and Tourism Technology · pp. 1-16 · 32 references

Abstract

Purpose This study aims to examines whether generative artificial intelligence (AI) and virtual reality (VR) function as equalizers or reproduce existing interpretive asymmetries in digital heritage interpretation among visitors with varying levels of cultural proximity. Design/methodology/approach A quasi-experimental design (n = 78) compared AI-based interpretation with immersive VR in the context of Qing Dynasty migration, with cultural proximity treated as a stratifying factor. Findings After controlling for prior knowledge, cultural proximity significantly predicted interpretive understanding, whereas digital modality and the interaction between digital modality and cultural proximity were not statistically significant. Insiders achieved higher interpretive scores than outsiders across both AI and VR conditions. Although this observed pattern is consistent with the proposed Digital Echo effect, post hoc power for detecting medium-sized interaction effects was limited (0.59); the findings should therefore be interpreted as preliminary rather than definitive. Qualitative reflections further suggested that VR fostered immersion, whereas AI supported semantic grounding, but neither modality clearly eliminated the interpretive barriers faced by culturally distant visitors. Practical implications Heritage practitioners should combine semantic scaffolding with immersive experiences and tailor contextual support to visitors’ cultural familiarity. Originality/value This study proposes the digital echo effect as a conceptual lens for explaining how digital heritage technologies may reproduce preexisting interpretive inequalities among visitors with different levels of cultural familiarity. It highlights the need for culturally responsive design while emphasizing that the proposed effect requires replication with larger samples.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations

Related blog posts

Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.