A Hybrid Human-AI Collaborative Workflow for Digital Heritage Interpretation: A Case Study on the "Dongdong Push" Nuo Mask

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While Artificial Intelligence Generated Content (AIGC) provides new pathways for the digital innovation of intangible cultural heritage (ICH), current applications often lack a critical methodological framework and view users as passive consumers. To address this gap, this study centers on a key research question: compared with the traditional model where users only passively receive or give feedback on ICH cultural and creative products, how can the human-AI co-creation model empower ordinary users without professional design skills to take the initiative in creating and reinterpreting ICH? The paper focuses on the Yunnan Tile-Cat (Wamao), a unique regional architectural ornament and an endangered intangible cultural heritage, and builds a simple working demo to show how the idea works. Through a formative evaluation, the study examines a dual collaborative workflow. The AI + designer phase speeds up cultural data integration and visual conceptualization. Subsequently, the AI + user mechanism enables public participation through real-time 2D to 3D modeling and AR interaction. This collaborative approach productively reduces creative barriers and improves cultural identity. The findings add to design knowledge by showing that, while AI democratizes creative participation, human designers must actively intervene to overcome AI's semiotic limitations in culturally sensitive settings.

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This study explores the potential of human-AI collaboration in generating graph-based descriptions of artifacts in museums. We implemented an AI system that automatically transforms textual descriptions into graph-based representations by fine-tuning a general-purpose language model on a museum dataset and designing an ontology for artifacts. A user study conducted with curators as experts and lay users such as visitors demonstrates the quality and user satisfaction of the graphs generated by the collaboration of AI and a human expert. The results of our study demonstrate that AI is highly effective in extracting detailed and reliable information from textual descriptions. However, human experts play a crucial role in refining the AI-generated graphs, thereby enhancing both the accuracy and readability. Human-AI collaboration even promotes greater consistency across graphs designed by different experts, effectively satisfying diverse user preferences. This research presents a scalable and engaging solution for graphical museum artifact descriptions and a deeper understanding of human-AI collaboration, particularly within the domain of cultural heritage information delivery.

Memory and heritage institutions in Ghana house invaluable repositories of historical records and culturalartifacts, yet many of these treasures are inaccessible dark archives (that is, unexplored or underutilisedcollections). This paper explores the potential of human-artificial intelligence (AI) collaboration as atransformative approach to unlock dark archives within academic institutions. Drawing on interdisciplinaryperspectives from AI and cultural heritage preservation, the study examines the methodologies, challenges, andopportunities associated with leveraging AI technologies alongside human expertise in preserving informationand knowledge for posterity. The study employs a qualitative methodology using empirical findings from theInstitute of African Studies Library, University of Ghana. Furthermore, the research identifies key researchobjectives, including assessing the readiness of information professionals in Ghana to embrace AI technologiesand archiving practices. The study provides insights and recommendations for policymakers, informationprofessionals, and researchers seeking to harness the power of human-AI collaboration to unlock dark archivesand bring them to light.

The weaving of bamboo and willow is the important part of traditional craft heritage, a complicated craft of wonders of manual work and cultural significance. The paper is a human–AI collaborative system to design weave pattern with interaction between human and AI through Procedural generation of the weave patterns by L-System modelling and Interactive Genetic Algorithms (IGAs) so that the design can be more authentic to their cultural use and adapted to the present product design. Such patterns that are applied in the constructions made of bamboo and willows also exploit the recursive structure and geometrical restrictions found in woven systems, which are codified in the logic rules of the L-System and used to produce systematically scalable and rule-based variants of patterns. At the same time, IGAs were also used to include live feedback of artisan weavers and designers on the pattern in order to continuously improve them on the basis of aesthetic, functional, and cultural requirements. The current weaving designs are used in various cultural products like furniture, lamps and decorations among other products and this way the continuity of heritage is maintained by embracing practical innovations. This framework will prove that adding algorithmic pattern generation to human aesthetics judgement is a viable direction to move heritage weaving towards through an enhanced cultural product design using digitally enhanced manufacturing processes.

The transformative aspects of human-AI cooperation in people preservation and revival of folk tradition, including music, visual art, and storytelling, are addressed in this paper. As more creative industries embrace the role of artificial intelligence, this paper explores the ways in which AI tools can support human knowledge to maintain cultural heritage and at the same time guarantee authenticity and community involvement. The study relies on qualitative and mixed-method research, such as case studies of the restoration of folk songs with the help of AI, the models of generative art, and the use of natural language processing (NLP) in folk stories. By examining online archives, discussing with cultural practitioners and analyzing AI-generated results, this study will describe changing trends in human-machine co-creation. The results indicate that AI has an important role in documentation, restoration, and creative reinterpretation of folk traditions to increase the accessibility and engagement of younger audiences. Nevertheless, there are still struggles on how to preserve cultural authenticity, ethical use of data and communal ownership. The research emphasizes that the human-AI partnership is best served where it is controlled by the local knowledge and participatory models, so that the technology could serve as an enabler instead of a substitute of the conventional art. Investigating the advantages and the drawbacks of AI in the cultural heritage, the study highlights the importance of the balanced and encompassing attitude toward the technological introduction.

Generative artificial intelligence (GenAI) has transformed design education, yet growing evidence suggests that the fluency of AI-generated outputs may create a “fluency illusion”—a metacognitive bias whereby learners conflate polished AI artifacts with genuine cognitive mastery. A critical unresolved question is how to quantitatively diagnose this AI-induced fluency illusion without disrupting the natural learning process. This study introduces MBS-AIGC, a purpose-built AI-supported design education platform grounded in the Meaning–Behavior–Spirit (MBS) cultural cognition model for Chinese intangible cultural heritage. Drawing on the industrial soft-sensor paradigm, we computationally formalized six behavioral soft-sensor indicators from the digital interaction traces of 71 undergraduate design students over a four-week instructional period and applied K-means clustering to identify latent engagement patterns. Three distinct human–AI collaboration profiles emerged: Deep Explorers (n = 41), Progressive Builders (n = 16), and Surface Operators (n = 14). Crucially, expert-assessed cognitive flexibility significantly differentiated the three groups (F(2, 68) = 5.66, p = 0.005, η2 = 0.143), whereas a conventional self-report questionnaire failed to distinguish among them (F(2, 36) = 0.29, p = 0.748), providing preliminary empirical evidence for the fluency illusion in design education. By addressing the lack of objective diagnostic tools for metacognitive miscalibration, this research contributes a scalable, zero-intrusion behavioral soft-sensor framework that enables educators to decode human–AI collaboration patterns and mitigate the fluency illusion in creative learning environments.

Accessibility and inclusion remain persistent challenges in cultural and heritage tourism, particularly as artificial intelligence (AI) increasingly mediates visitor experience and destination management. While prior research has examined AI-enabled personalisation and smart tourism development, less attention has been paid to how accessibility is shaped through the collaborative relationship between human users and AI systems in public cultural environments. This paper presents a design-led review of 66 peer-reviewed journal articles published between 2022 and 2026, identified through a PRISMA-guided search of Web of Science, Scopus, and the ACM Digital Library. Drawing on perspectives from human–AI collaboration, inclusive design, and human–data interaction, the review synthesises how AI-enabled systems interpret user data, mediate cultural experiences, and distribute agency across tourism service ecosystems. Building on this synthesis, the paper proposes the Inclusive Human–AI Mediation (IHAM) model, a theoretical framework that conceptualises accessibility as an emergent outcome of data interpretation, AI mediation, human agency, and governance. The model advances tourism technology research by reframing accessibility as a systemic and design-mediated challenge, and provides guidance for designing inclusive, ethical, and sustainable AI-enabled cultural and heritage tourism systems.

Cultural heritage sites are precious and fragile resources that hold significant historical, esthetic, and social values in our society. However, the increasing frequency and severity of natural and man-made disasters constantly strike the cultural heritage sites with significant damages. In this article, we focus on a cultural heritage damage assessment (CHDA) problem where the goal is to accurately locate the damaged area of a cultural heritage site using the imagery data posted on social media during a disaster event by exploring the collective strengths of both AI and human intelligence from crowdsourcing systems. Unlike other infrastructure-based solutions, social media platforms provide a more pervasive and scalable solution to acquire timely cultural heritage damage information during disaster events. Our work is motivated by the limitation of current AI solutions that fail to accurately model the complex cultural heritage damage due to the lack of essential human cultural knowledge to differentiate various damage types and identify the actual causes of the damage. Two critical technical challenges exist in solving our problem: 1) it is challenging to effectively detect the problematic cultural heritage damage estimation of AI in the absence of ground truth labels and 2) it is nontrivial to acquire accurate cultural background knowledge from the potentially unreliable crowd workers to effectively address the failure cases of AI. To address the above-mentioned challenges, we develop CollabLearn, an uncertainty-aware crowd-AI collaborative assessment system that explicitly explores the human intelligence from crowdsourcing systems to identify and fix AI failure cases and boost the damage assessment accuracy in CHDA applications. The evaluation results on real-world datasets show that CollabLearn consistently outperforms both the state-of-the-art AI-only and crowd-AI hybrid baselines in accurately assessing the damage of several world-renowned cultural heritage sites in recent disaster events.

This paper proposes the AI–Human Collaborative Interpretation (AHCI) human–machine collaboration methodological framework, offering a new research pathway for the digital interpretation of complex cultural heritage. The framework integrates multi-source data processing, formalised feature modelling, interpretable analysis and human–machine feedback mechanisms, translating the complex information of cultural heritage into an analysable and interactive structure. Building upon this, the Tianlai.China project serves as a case study for the multimodal data integration, aesthetic feature modelling and interactive visualisation of the Illustrated Catalogue of Famous Porcelains Through the Ages. The effectiveness of the collaborative mechanism is validated through expert–public–AI comparative experiments and human–AI feedback loop experiments. The main contributions of this paper include: proposing a methodological framework for human–machine collaboration in cultural heritage value assessment; constructing an interpretable formalised model of cultural features and a corresponding visualisation system; analysing the impact of AI explanations on expert cognition through empirical research; and exploring the transferability of this framework across different types of cultural heritage. Against this backdrop, the human–machine collaboration system developed by AHCI—comprising ‘data semantics–feature modelling–visual output–cognitive feedback’—possesses cross-domain applicability.

While generative AI (GAI) is widely used in cultural heritage promotion, how creators engage with this technology in relevant artistic creation remains underexplored. This study conducted in‐depth interviews to investigate the approaches, challenges, and mitigations of human‐GAI collaboration in such contexts. We found that human‐GAI collaboration processes include four stages: (1) creative context setting, (2) GAI tools evaluation, (3) creative concepts formation and (4) human‐GAI collaborative production. In this process, two main challenges were identified: (1) cultural misrepresentation and (2) ambiguous intellectual property. Accordingly, corresponding mitigations were also uncovered.

This study proposes a human-AI collaborative framework to resolve the tension between technological innovation and depth of cultural transmission in heritage communication, exemplified through the intelligent dissemination of traditional Chinese tea culture. By integrating generative artificial intelligence and augmented reality, the framework employs a four-dimensional cultural genome indexing system spanning architectural, material, behavioral, and spiritual layers to enable progressive narrative exploration — from tea-ceremony utensils to ritual practices and philosophical principles. AR-driven virtual reconstruction of historical tea culture and large language model-enhanced dialogue interfaces transform passive consumption into dynamic co-creation, where users actively shape cultural meanings through natural language engagement. Self-optimizing mechanisms dynamically align technological outputs with the depth of cultural transmission by synthesizing multi-source feedback, user interactions, curator oversight, and ethical validation, thereby resolving the imbalance where technological spectacle overshadows substantive cultural transmission. Positioning AI as an amplifier of human expertise, the framework offers solutions for heritage preservation, harmonizing computational power with cultural intentionality to foster immersive, ethically grounded engagement.

With the development of generative artificial intelligence (AIGC), its application in human–AI collaborative design has attracted increasing attention. However, in the process of intangible cultural heritage (ICH) digital revitalisation and cultural product design, systematic research on balancing traditional cultural characteristics with contemporary market aesthetics while avoiding AI-generated cultural bias and visual homogenisation remains limited. Taking the “Dongdong Push” Nuo mask of the Dong ethnic group in China as a case study, this paper proposes an AIGC-assisted ICH IP design workflow based on human–AI collaboration. By integrating the Double Diamond design model, large language models, and AI image-generation tools, the study reconstructs the traditional ICH IP design process. Consumer preference analysis and cultural feature extraction are employed to establish semantic constraints for AI generation, while an AHP-FCE-based multi-stakeholder evaluation framework is constructed to assess design effectiveness through questionnaires and semi-structured interviews. The results indicate that the proposed workflow supports efficient trend extraction, style exploration, and visual ideation while achieving overall performance comparable to traditional manual design. The quantitative comparison revealed only small differences across evaluation dimensions, while the qualitative findings highlighted potential benefits of AIGC in visual exploration and iterative ideation. At the same time, the application of AIGC remains subject to challenges such as visual homogenization, AI hallucinations, and aesthetic inertia. These findings suggest that the value of AIGC in ICH design lies not in replacing human designers, but in supporting and expanding their cognitive and creative exploration through human–AI collaboration.

In the context of cultural exchange and mutual learning, the global dissemination of cultural heritage has emerged as a critical theme. However, the challenges such as cultural barriers and narrative rigidity hinder its international reach. This study focuses on Heilongjiang’s excellent spiritual heritage as an example to explore how AI-empowered text generation can bridge these gaps in international communication. The research proposes a four-phase framework: cultural decoding, narrative reconstruction, human-AI collaboration, and effect verification. It is found out that AI-empowered cross-cultural narrative adaptation is conducive to optimizing intercultural communication. The author hopes that this study could provide theoretical and practical insights for the Belt and Road Initiative’s cultural exchanges and offer actionable models to serve the reform of foreign language education.

Against the backdrop of generative AI and cross-media storytelling driving paradigm shifts in cultural dissemination, intangible cultural heritage education urgently needs to transition from static knowledge transmission to a dynamic, creation-driven, and emotionally resonant communication model. This paper proposes and validates an interdisciplinary teaching framework termed the “Human-Machine Collaborative Creation and Emotional Immersion” model. This framework transforms Repko’s interdisciplinary research process into a four-stage learning cycle (“Emotional Engagement and Problem Definition,” “Knowledge Integration and Inquiry,” “Prototyping and Testing,” and “Socialization and Evaluation”) tailored for elementary students, supported by a structured toolkit for practical implementation. The core of this framework lies in constructing a “Human-AI Critical Collaboration” model: students interact with AI from the perspective of a “cognitive partner,” with their central task being the cultural interrogation and selection of generated content, followed by its creative transformation into culturally self-aware multimodal expressions.. Research findings indicate that this structured approach has effectively facilitated a significant shift in students’ interaction patterns with artificial intelligence, fostering deeper cultural comprehension and innovative capabilities while enhancing cultural identity and social responsibility. This study provides an empirical case for innovative approaches to intangible cultural heritage education in the era of artificial intelligence.

Generative AI (GenAI) offers opportunities to preserve and more widely share Intangible Cultural Heritage (ICH), whilst at the same time bringing risks of misrepresenting, misappropriating, homogenizing, and devaluing ICH. To explore how artisans undertake embodied craft practice with GenAI fine-tuned on their own ICH and how GenAI might contribute to the future of ICH we report on a preliminary analysis of a cross-cultural collaboration between researcher-designers and Huayao minority culture embroiderers in rural China. Three GenAI workflows are explored through co-creation and in-situ making of new embroidery pieces. Initial analysis of reflective feedback suggests that GenAI has the potential to enhance creativity and support innovation within cultural norms and sensibilities, but that human creativity, manual refinement, and local nuance remain key to successfully embracing GenAI for Intangible Cultural Heritage.

This study aims to critically examine the design and evaluation of human–AI interactive visualisation systems in authentically representing, communicating, and revitalising Chinese intangible cultural heritage. Employing a quasi-experimental research design with survey-based data collection, the study engaged 300 university students selected through stratified sampling. The findings reveal that human–AI collaboration offers significant opportunities for enhancing the preservation, transmission, and reinterpretation of China’s intangible cultural heritage through interactive visual representations. Aesthetic appreciation emerged as a key determinant of user engagement, which, in turn, positively influenced cultural learning outcomes. Additionally, system usability was identified as the second most influential factor contributing to user engagement and satisfaction. The results further indicate that user-friendly AI-based interactive navigation systems foster deeper engagement, leading to enhanced user satisfaction and more effective cultural learning experiences. Overall, the study underscores user engagement as a critical predictor of cultural learning, suggesting that active user participation in AI-driven visualisation processes facilitates greater retention and understanding of intangible cultural heritage.

Abstract. In the domain of digital applications for museums and cultural institutions, visual and cinematic technologies provide a rich array of tools and techniques for transforming complex scientific concepts into engaging and accessible narratives for diverse, non-specialist audiences. The article illustrates a digital presentation workflow that harnesses the interpretive power of 3D reconstructive modeling and cinematic virtual reality (VR). Designed for use across various hardware and software platforms, this approach aims to improve the dissemination of knowledge and enhance public understanding of railway heritage. The discussion is grounded in a significant case study: the A.C.E. (Apparato Centrale Elettrico) cabin at Rome Termini Station, a landmark of Italian railway heritage currently being transformed into a museum. The article details the adopted workflow and the results of one of the immersive experiences developed specifically for this new exhibition space. At the heart of this experience is a permanent installation located in the former A.C.E. cabin, which reconstructs the unrealized architectural project for Termini Station envisioned by Angiolo Mazzoni (1894–1979). Within the interdisciplinary framework of the initiative, the project integrates historical research, AIassisted modelling, and agile development practices. This combination enables the creation of compelling, historically validated narratives that enhance interpretation and foster emotional engagement with railway heritage and its broader cultural and historical context.

This paper examines how 360-degree panoramic tours can enhance heritage promotion, accessibility, and engagement, illustrated through two case studies: the Understory Art and Nature Trail in Northcliffe and the Subiaco Hotel in Perth, Western Australia. The Understory Art Trail was deployed in Google Street View to deliver an interactive, virtual walkthrough of outdoor art installations. This made the site accessible to a geographically diverse global audience, including those unable to visit in person. In contrast, the Subiaco Hotel tour was created with 3DVista. It integrated multimedia features such as historical photographs, architectural drawings, and narrative audio, offering users a layered exploration of built heritage. The two studies were designed so that frameworks like Technology Acceptance Model (TAM) could be applied to them to evaluate visitor experience. However, this paper focuses on the workflow for providing 360-degree panoramic tours, the integration of AR, low-cost digital twins, and the testing of interactive web platforms. Google Street View demonstrates ease of use through familiar navigation, while 3DVista reflects usefulness through its richer interpretive features. By analyzing workflows and digital strategies on both platforms, the study evaluates their effectiveness in increasing online visitor engagement, supporting heritage tourism, and communicating cultural significance. Challenges related to technical limitations, geolocation accuracy, audience targeting, and resource constraints are critically discussed. The findings demonstrate that context-sensitive applications of 360-degree tours are valuable for visibility, education, and long-term preservation. The paper concludes with targeted recommendations to guide future heritage projects in leveraging immersive digital technologies to expand audience engagement and support sustainable heritage management.

Three-dimensional scanning, additive manufacturing, and extended reality are now widely used in digital heritage, but they are often discussed as separate technical tools rather than as parts of a user-facing workflow. This review approaches them from the standpoint of user experience, asking how digital capture, model preparation, physical replication, immersive interpretation, and hybrid access affect the way heritage content is understood, used, and trusted. The paper develops a critical narrative discussion of recent work and design practices, without adopting a systematic review protocol. Particular attention is given to educational use, museum interpretation, accessibility, tactile interaction, XR navigation, perceived authenticity, and the evaluation of user experience. The discussion indicates that UX is shaped before the final interface appears: incomplete capture, opaque model editing, poorly readable replicas, or confusing XR layers can all weaken the cultural value of an otherwise advanced system. Recurring barriers include technical complexity, interoperability problems, limited staff training, institutional constraints, accessibility gaps, and uncertainty around automated reconstruction. The review argues that automated digital heritage workflows should be planned as human-centered systems, where efficiency is balanced with usability, inclusion, transparent interpretation, and user-based evaluation.

Abstract. This contribution illustrates the results of research that aims to connect the workflow for knowledge – survey, interpretation, modeling – to the transmission of this knowledge and enhancement of Cultural Heritage by physical models, demonstrating its potential in a “Design for All” perspective. Palazzo Mazzonis, the current seat of the MAO (Museo d’Arte Orientale), is one of the many buildings in the historic city center of Turin characterized by a particular complex vaulted atrium: it has been investigated through TLS survey tools for the production of two-dimensional drawings (for the detailed representation of architectural features) and three-dimensional models (for the interpretation of the design idea through geometry). The last phase of work aims to translate the interpretative digital models into tactile models for Cultural Heritage transmission, through processes of digital fabrication, with obvious impacts on the visit path inside the museum, also with didactic purposes.

Digital documentation of cultural heritage has significant impact in preservation, interpretation, and management of cultural items. This work provides a workflow that is coordinated toward three-dimensional documentation and reconstruction of architectural remains from Ulpia Traiana Sarmizegetusa, a Daco-Roman archaeological site located in Romania. This paper focuses on representative archeological heritage assets such as the Large Temple complex, a fragment of a funerary stone, two columns, and the remains of a wall. Data acquisition process was realized using terrestrial laser scanning (TLS) with the Z+F IMAGER 5024 Supreme scanner, and the UAV-based photographic data in order to capture detailed geometric information and spatial contexts of the scene. Datasets were then processed with specialized software, including Z+F LaserControl, Agisoft Metashape, and CloudCompare, and were integrated into a Scan-to-BIM workflow in Autodesk Revit. This allowed a three-dimensional model of the archaeological structures to be generated. The resulting digitized model allows the detailed depiction of the site as well as visualization, analysis, and archeological heritage interpretation. The work demonstrates the possibility to incorporate TLS, UAV photogrammetry and Scan-to-BIM methods in the study in order to assure a high-quality archaeological documentation and its application to digital heritage.

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Abstract. This research investigates the usage of technologies to represent architectural heritage by examining how instruments such as Heritage Building Information Modeling (HBIM) and Virtual Reality (VR) can be triggered to present a lost cultural heritage. The focus here is the "Turin 1911 World’s Fair," a dismantled heritage site held in the Valentino Park, Turin, in 1911. That can serve as a methodological sample for a digitally integrated approach, which was developed through a combination of historical documents and the scan-to-HBIM process common in HBIM modeling. The project yields a high-fidelity digital twin that represents the lost heritage and a VR platform that enables visitors not to experience the site just as an illustration, but to immerse themselves inside an operative component in the interpretation of architectural history and modern technologies in design. This method fosters new dialogues between past and present and critical reflection on spatial transformation, and presents a generative and narrative tool to enhance analytical depth and public perception. This study ultimately proposes a shift in historical storytelling toward an experimental narrative in which technology, perception, and history unite to create new forms of cultural heritage.

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Abstract The domain of cultural heritage is on the verge of adopting immersive technologies; not only to enhance user experience and interpretation but also to satisfy the more enthusiastic and tech-savvy visitors and audiences. However, contemporary academic discourse seldom provides any clearly defined and versatile workflows for digitising 3D assets from photographs and deploying them to a scalable 3D mixed reality (MxR) environment; especially considering non-experts with limited budgets. In this paper, a collection of open access and proprietary software and services are identified and combined via a practical workflow which can be used for 3D reconstruction to MxR visualisation of cultural heritage assets. Practical implementations of the methodology has been substantiated through workshops and participants’ feedback. This paper aims to be helpful to non-expert but enthusiastic users (and the GLAM sector) to produce image-based 3D models, share them online, and allow audiences to experience 3D content in a MxR environment.

Building Information Modelling (BIM) is a globally adapted methodology by government organisations and builders who conceive the integration of the organisation, planning, development and the digital construction model into a single project. In the case of a heritage building, the Historic Building Information Modelling (HBIM) approach is able to cover the comprehensive restoration of the building. In contrast to BIM applied to new buildings, HBIM can address different models which represent either periods of historical interpretation, restoration phases or records of heritage assets over time. Great efforts are currently being made to automatically reconstitute the geometry of cultural heritage elements from data acquisition techniques such as Terrestrial Laser Scanning (TLS) or Structure From Motion (SfM) into BIM (Scan-to-BIM). Hence, this work advances on the parametric modelling from remote sensing point cloud data, which is carried out under the Rhino+Grasshopper-ArchiCAD combination. This workflow enables the automatic conversion of TLS and SFM point cloud data into textured 3D meshes and thus BIM objects to be included in the HBIM project. The accuracy assessment of this workflow yields a standard deviation value of 68.28 pixels, which is lower than other author’s precision but suffices for the automatic HBIM of the case study in this research.

Reliable digital recording and analytical interpretation of architecturally complex historic monuments remain challenging, particularly for structures with irregular geometries, stratified construction, and seismic vulnerability. While unmanned aerial vehicle (UAV) photogrammetry, terrestrial laser scanning, and heritage building information modeling (HBIM) have been widely applied in isolation, their integrated use as a coherent, research-driven workflow remains insufficiently explored for Orthodox ecclesiastical museums embedded in dense historic fabrics. To address this gap, we develop and validate a hybrid digital survey methodology combining UAV-based photogrammetry, mobile light detection and ranging, and point-cloud-driven HBIM, applied to the Onufri National Museum in Berat, Albania, a United Nations Educational, Scientific, and Cultural Organization World Heritage context. High-density datasets were processed and converted into a geometrically rigorous HBIM model, enabling the identification of previously undocumented spatial irregularities, dome asymmetries, and constructional hierarchies that are absent from existing archival documentation. The results demonstrate that the proposed workflow achieves sub-centimetric to centrimetric geometric consistency across most elements, significantly improving spatial completeness, modeling reliability, and diagnostic potential compared to conventional survey approaches. Beyond documentation, the HBIM environment functions as an analytical interface for structural interpretation, pathology mapping, and future conservation planning. By foregrounding measurable outcomes and methodological transferability, the study contributes a replicable digital documentation framework applicable to complex heritage buildings in seismic and constrained urban contexts, reinforcing the role of integrated HBIM as a scientific instrument rather than a representational tool in heritage conservation research.

Cultural heritage remains vulnerable to deterioration and loss, while physical restoration is constrained by principles of minimal intervention, reversibility, and material compatibility. Digital reconstruction, therefore, offers a non-invasive, evidence-based alternative. However, no integrated framework systematically connects target selection, model generation, and interpretation, limiting consistency and transparency. This study proposes the SGAD (Selection–Generation–Assessment–Documentation) framework for procedural coherence, reproducibility, and interpretative accountability. The Selection phase defines reconstruction scope, evidence grading, and practical criteria for disclosure pathways. The Generation phase applies a three-stage workflow—input preparation, model training, and integrated generation—using stable diffusion with element-specific LoRA modules. The Assessment phase evaluates quality through structural–morphological and perceptual–local axes, and the Documentation phase records metadata on hypothesis status, transparency, cultural sensitivity, and interpretative boundaries. Applied to Yeongsanjeon Hall, Magoksa Temple, SGAD demonstrates feasibility and interpretative considerations, supporting a structured approach to architectural digital reconstruction.

This article describes workflows established through the creation of a sonic extended reality (XR) which was designed to evaluate the efficacy of geolocated sound as a method for interpreting or researching historic environments. Digital sound layers geolocated upon physical landscapes offered a potent alternative to visual methods for creating XR in outdoor settings. Visual XR, albeit more thoroughly studied, had presented obstacles to deployment in outdoor historic landscapes. GPS-triggered sound could potentially bypass these obstacles as well as facilitating curatorial best practices such as multisensory interpretation and polyphonic storytelling. A heritage-themed sonic XR was created within a historic park to test the suitability of geolocated sound against a range of interpretative requirements and technical challenges. It told the park’s origin story through a nonlinear narrative trailscape which comprised audio interviews with communities connected to the park at the time of research, digitised archival oral history recordings of past communities, local music and historical sound textures. These were deployed as intersecting narrative pathways in order to encourage playful explorative learning by offering multiple potential routes through the history. This design was more or less successful depending on GPS sensitivity versus listener’s trajectory versus size of sound area being activated. Using exclusively sonic XR was found to be limiting from an accessibility standpoint. However, geolocated sound as part of a multisensory framework offered significant potential for augmenting historic environments. The project emerged as a useful pilot for a further study using geolocated sound as a research methodology to analyse relationships between place historicity and place attachment.

Abstract. In order to be properly handed down, especially in particular conditions with a high rate of vulnerability, cultural heritage requires documentation and enhancement strategies. The case study presented in this paper is particularly critical not only for the conservation conditions, but especially for the environmental conditions: the Catacombs of San Vittorino have complex conditions of recovery, because of the nature of the artefact and because of the poor lighting of the main environments. For this particularity, a workflow was developed that, in order to achieve the creation of an immersive device navigable by digital viewers such as Oculus Rift or similar, required the start-up of shooting by laser scanning, and then treat the point cloud with different software, in order to obtain a satisfactory result that, in other contexts, could have started easily from a photogrammetric shooting.

This paper presents a pilot educational workflow that couples 3D remote sensing with heritage-driven pedagogy by engaging architecture master’s students in the documentation and digital archiving of Transylvanian cultural sites. Using terrestrial and mobile 3D scanning, students documented multiple typologies—wooden churches (Târgușor, Tioltiur), historical ensembles (Mociu, Coplean), industrial sites (1 Mai–Luduș, Vânătorilor–Luduș), and an urban street segment (Potaissa)—to generate dense point clouds that served as the basis for geometric reconstruction, semantic interpretation, and condition assessment. The study describes how the characteristics of different construction systems (timber, brick, stone, mixed structures) relate to point-cloud quality, survey coverage, and subsequent CAD/BIM drafting, with attention to the qualitative reading of minor deformations in wooden churches and of degradation patterns in masonry and industrial buildings. We also consider how artefacts in the data (noise, occlusions, registration errors) affect scene understanding and the interpretation of derived observations relevant to condition assessment and, prospectively, to monitoring. For the Tioltiur dual-sensor case, the TLS and SLAM datasets were compared through an internal CloudCompare registration check (final RMS 0.1121 on 50,000 points, fixed scale 1.0 and theoretical overlap 100%), surface-density displays (r = 0.005 for the Z+F dataset and for the GeoSLAM dataset), fitted-wall-plane readings (dip values around 89 deg. and 85 deg.) and a longitudinal section documenting roof/vault deformation. Beyond technical performance, the paper examines the self-reported formative impact on students’ digital skills and their understanding of cultural values, arguing that participation in 3D data acquisition, processing, and interpretation positions them as co-creators of a living digital archive. Pre- and post-workshop questionnaires (n = 13 each) are analysed descriptively—counts, percentages and medians with interquartile ranges—because the two instruments are unmatched and carry no shared identifier, so no paired test is applied; post-workshop self-ratings of technical competence, heritage understanding, archival awareness and collaboration were consistently high (medians 4–5), with uneven access to VR the main gap. By connecting point-cloud-based documentation workflows with heritage education, the project outlines a transferable, monitoring-ready baseline model in which 3D remote sensing supports both careful documentation and the transmission of regional identity and cultural meaning in architectural training. As an exploratory pilot with a small, self-reported sample, the study reports descriptive and qualitative findings rather than validated metric or statistical results.

The study seeks to investigate the cultural factors of Chizhou Nuo masks and their strategic application strategies in contemporary design, by compiling the Chizhou Nuo cultural literature, integrating memetics, identifying the cultural factors of the Chizhou Nuo masks, and then developing the genetic mapping of the Chizhou Nuo mask culture. Simultaneously, the analytic hierarchy process (AHP) is employed to objectively allocate weights to design considerations, ascertain the significance of each cultural factor, and prioritize their value, thereby filtering the necessary design elements. Subsequently, thematic analysis to employed to examine the interview data, yielding an indicator of consumer demand. The shape grammar was ultimately utilized to produce a design product that adheres to modern aesthetic criteria. The fuzzy comprehensive evaluation (FCE) method is used to evaluate the design scheme, select the optimal design scheme, and guide further design optimization. This study investigates a novel paradigm for the design and evaluation of Chizhou Nuo mask cultural and creative items utilizing memetics, AHP, thematic analysis, shape grammar, and FCE. The model’s practicality is validated through the design cases of Chizhou Nuo mask graphic design. It offers novel theoretical and practical direction for the designers of future intangible cultural and creative products. Furthermore, it offers a novel avenue for the preservation and advancement of intangible cultural heritage.

Maonan, as one of China's many less populous mountainous ethnic groups, mainly distributed in Guangxi Huanjiang County in the south, central and southern mountainous areas, but also in Guizhou Pingtang County and Dushan County junction of the Kapu River, six holes in the river basin area is also distributed, but the main settlement is still in Guangxi. This nation has a long history of breeding a unique culture, in which Nuo culture is particularly significant. Nuo face as an important form of expression of Nuo culture, not only decorative features distinctive, but also contains the Maonan deep historical memory, religious beliefs and unique artistic aesthetic value. However, with the accelerated process of modernization, the protection and inheritance of traditional culture is facing serious challenges. For the Maonan Nuo culture, how to maintain its vitality and charm in contemporary society has become an urgent problem. Through the combination of modern aesthetics and the needs of the times, the research results of this topic can make Nuo culture in contemporary society with new vitality. This not only promotes the inheritance and development of Maonan culture, but also allows more people to understand and recognize this unique cultural heritage. At the same time, the inheritance and development of Nuo culture can also provide new cultural resources and inspiration for contemporary society, promote cultural exchanges and integration, add a touch of colour to the common cultural heritage of mankind, and has far-reaching significance and value.

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Against the backdrop of growing attention to digitalization and the preservation of intangible cultural heritage, Enshi Nuo masks—as a distinctive cultural symbol of the Tujia region—face developmental challenges such as a generational gap in transmission, insufficient exploration of their cultural significance, and homogenization of cultural and creative products. The in-depth application of Generative Artificial Intelligence (AIGC) technology offers new possibilities for overcoming these challenges. This paper first elaborates on the technical principles of AIGC and its current applications in the field of cultural and creative design; second, it systematically examines the regional cultural context, artistic characteristics, and transmission challenges of Enshi Nuo masks; subsequently, it outlines an innovative pathway for AIGC to empower the cultural and creative design of Enshi Nuo masks; finally, using the generative design practice of the picture book The Little Carpenter and the Mask God as a case study, it demonstrates the end-to-end application of AIGC tools in plot generation, storyboarding, character development, and illustration. The study indicates that AIGC technology can effectively enhance design efficiency and revitalize the cultural representation of intangible cultural heritage (ICH), but its successful application requires support from high-quality cultural databases and effective human-machine collaboration capabilities. In the future, we should adhere to the ethical boundaries of cultural authenticity and promote the deep integration of AIGC with the ICH cultural and creative industry.

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As one of the numerous intangible cultural heritages in China

The sculpture of Nanfeng Nuo mask originated in the Han Dynasty, developed in the Tang and Song Dynasties, and prospered in the Ming and Qing Dynasties. The carving art has been passed down to this day. The sculptures of Nanfeng Nuo masks are famous for their simple and profound, vivid shapes and delicate techniques. This article first stated that under the background of AR technology, in terms of digital protection mode, limited by ideological understanding and technology, the current digital protection of Chinese traditional culture is still at the stage of digital information collection and preservation. How to enrich and perfect the existing digital protection mode with new digital technology is an urgent problem to be solved in the new era. Taking the Nanfeng Nuo mask as an example, this research analyzes the inheritance dilemma of the Nanfeng Nuo mask wood carving and the modern and innovative protection model through the reading of historical documents, field inspections of existing wood carvings, survey visits to the protection status, and understanding of digital technology. Through the fuzzy KNN algorithm and AR compared to the database, the various databases are related to form a complete protection system; in the "live inheritance protection mode", AR technology is proposed as the basic technology, and AR image acquisition technology and AR display technology are proposed. , AR human-computer interaction technology and digital protection mode are combined, and then a digital protection platform based on AR technology is designed to achieve the realization of the live inheritance mode by the way audiences participate in the use of the digital protection platform. Experimental research results show that people in their 30s to 40s may take a long time to receive and learn when facing a new interactive operating system, but now due to the popularity of social software such as WeChat, the 30 to 40 age group 24.4% of the population can also perform basic operations, which provides a prerequisite for the use of AR technology to digitize the Nanfeng Nuo mask.

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In traditional Chinese culture, Exorcising mask is an intuitive artistic symbol of China's intangible cultural heritage and contains unique national characteristics. Therefore, the purpose of the research is to explore design methods for analyzing and classifying public needs, and through the combination of artistic design and digital platform to realize the innovative design and application of Exorcising mask technology, so as to effectively promote the protection, dissemination and innovation of traditional Exorcising mask craftsmanship. The research method is to establish a user emotional demand model for Exorcising mask products from the three levels of instinct, behavior and reflection of the emotional design theory, and use the KANO model to perform data verification and function sorting on the established model, and design new digital platforms and traditional images based on the analysis results. The research conclusion is that the combination of design and technology achieves a diversified approach to traditional handicrafts. The design method of analyzing and classifying public needs using the KANO model can accurately discover the real emotional needs of the public, and provide direction and data support for the design of traditional Exorcising mask art products.

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The aim of this study was to develop Jiangxi Nuo masks using 3D digital fashion design technology and suggest various ways to utilize traditional culture based on the characteristics of Nuo masks, a traditional Chinese artifact of intangible cultural significance. The researchers conducted a literature review to gather information about Nuo culture and masks that could represent Jiangxi. Features of the masks were analyzed and classified. The result are as follows. First, the symbolic characteristics of Jiangxi’s Nuo masks can be divided into those based on their origin and history, the user’s social status, and the notions of primitive beliefs of the chosen people, such as naturism and totemism. Second, Nuo masks’ splendid decorations convey meanings such as luck, the bixie, longevity, wealth, and peace in the family. Third, playfulness in mask-making is about dismantling the original form of the mask, re-creating it through application. Fourth, the masks express primitiveness mostly by conserving the wood’s original color or material. The initial masks carved to represent images of figures aptly deliver the primitive forms and images of Nuo culture. In this study, Nuo masks were developed and produced using the 3D digital technology CLO 3D by adopting the expressive characteristics and applying design methods such as asymmetricity, exaggeration, and modification. The results of this study demonstrate the possibility of creating diverse as well as economical designs through the reduction of production.

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: Cultural heritage education increasingly relies on access to diverse digital materials, including archaeological artefacts, museum records, historical maps, and other multimodal resources. These materials are rarely available in formats directly suitable for classroom instruction. This study examines the use of multimodal generative artificial intelligence (AI) to convert such materials into structured teaching tools. Rather than conceptualising AI as an autonomous lesson-generation system, the research proposes a Human-in-the-Loop framework in which educational value emerges from the interplay of source selection, instructional design, multimodal generation, teacher critique, and iterative refinement. This approach is exemplified by a case study of the ceremonial chariot discovered at Civita Giuliana near Pompeii, which demonstrates how a complex archaeological source can be transformed into a pedagogical resource. Additionally, the paper presents a comparative analysis of various types of heritage inputs, including an archaeological object, a museum artefact record, and a historical map. The findings suggest that multimodal generative AI can substantially enhance cultural heritage instruction when integrated within a teacher-guided workflow that ensures historical accuracy, pedagogical coherence, age appropriateness, and source transparency.

Immersive visualization can support interpretation of architectural heritage in historical paintings, yet translating 2D pictorial evidence into navigable 3D scenes remains challenging. Conventional workflows rely on physical survey data, while direct generative AI (GenAI) may produce structural hallucinations and lack historical constraints. This study proposes a human-in-the-loop GenAI-assisted framework for producing immersive 3D visualization prototypes rather than historically verified reconstructions. It integrates multi-view image generation, knowledge-informed review, single-image-to-3D generation, topology inspection, and perceptual calibration. Four fragments from the Northern Song Dynasty painting Along the River During the Qingming Festival were examined as a single-case proof of concept. Across three tested model pairs, raw AI assets were generated in approximately 3–4 min and were suitable for distant-background use; close-up visualization required 1–2 h of refinement, while basic structural editability required 4–5 h of post-processing, reducing the initial time advantage. A mixed-methods study with nine domain experts and 30 non-expert participants used the UES-SF, an adapted VisAWI, and semi-structured interviews analyzed through inductive thematic analysis. All eight subscale scores exceeded their neutral midpoints after Bonferroni correction (all adjusted p<0.001), indicating favorable perceptions of the guided experience. Interviews suggested potential for spatial exploration, museum interpretation, and education. However, geometric discontinuities, detail loss, color deviation, and historical-semantic errors remained, requiring expert review and manual correction. Transferability beyond this artwork and architectural tradition remains untested.

The disappearance of ancient forms of art can be seen as a great loss of cultural knowledge not only in the form of physical objects but also as symbolic meaning, stylistic grammar, and practice in aesthetic. Although current digital heritage projects focus on documentation, and preservation, they do not offer much assistance in the active revival of art. The ethically-based structure of the AI-driven reconstruction of the lost art traditions is proposed in this paper, making generator artificial intelligence a participatory system of developing the knowledge in artistic traditions. It consists of a semantic annotation framework, structured data curation, cultural knowledge graph, and hybrid diffusion-transformer model and human-in-the-loop governance. According to the experimental findings, in several case studies, the suggested method has been demonstrated to be superior to GAN-based and diffusion-only baselines in the parameters of perceptual quality, stylistic coherence, diversity, and authenticity evaluated by experts.

Nanjing Yunjin, a UNESCO-recognized intangible cultural heritage, represents a pinnacle of Chinese brocade weaving with its intricate patterns, symbolic meanings, and historical significance. Modern challenges like loss of traditional tech- niques and declining demand threaten its preservation. This paper introduces KGGAN-Yunjin, an AI-driven framework fusing knowledge graphs (KGs) with generative adversarial networks (GANs) for cultural gene inheritance and generative design. The Yunjin Cultural Gene Knowledge Graph (YCG-KG) encodes patterns, materials, symbols, and constraints, guiding the GAN for authentic yet innovative designs. A human-AI feedback loop refines outputs for adaptability. This novel integration embeds cultural knowledge into generation, enforcing fidelity, diversity, and feasibility, while addressing gaps in methods by incorporating weaving-specific rules and iterative refinement. Experiments on 1,500 Yunjin patterns show superior performance over baselines in FID, recall, expert scores, and conformity rate. Ablation studies confirm key components. User studies highlight accelerated workflows and reduced modifications. KGGAN-Yunjin provides a sustainable model for heritage revitalization via AI, bridging tradition and modernity while preserving Yunjin's legacy.

Adaptive reuse of industrial heritage from a tourism perspective presents a complex design challenge requiring a balance between heritage preservation, functional innovation, and diverse stakeholder expectations. However, current practices often face issues such as ambiguous demand interpretation and a disconnect between design generation and systematic evaluation. Addressing these limitations, this paper proposes and illustrates a human–machine collaborative design paradigm that integrates generative AI into a closed-loop process of “demand analysis–intelligent generation–comprehensive evaluation.” The method first employs Kansei Engineering and the KANO model to qualitatively extract and quantitatively prioritise heterogeneous user needs, translating subjective perceptions into structured design constraints and optimisation objectives. Next, these needs are encoded as text prompts to drive targeted spatial exploration by the generative AI tool Nano Banana AI. Finally, the TOPSIS method is applied for multi-criteria performance evaluation and solution selection. A case study of Shanghai Libo Brewery suggests that this paradigm can enhance design efficiency and show potential to outperform traditional methods across dimensions such as historical preservation, public accessibility, ecological integration, social inclusivity, and formal innovation. The research offers a quantifiable and systematically documented intelligent design methodology for industrial heritage renewal, while acknowledging the exploratory nature of the generative phase. Furthermore, it provides a visitor-demand-driven innovation pathway for developing industrial heritage tourism destinations, thereby potentially enhancing cultural experiences and tourism appeal at heritage sites. This research illustrates a move from an experience-driven paradigm toward a data- and value-driven approach, contributing theoretical methodologies to the intersection of cultural tourism and artificial intelligence.

In this paper, we propose a low-code, AI-assisted workflow for the digital revitalization of intangible cultural heritage (ICH), focusing on the Shuizu Dance in Conghua, China. Leveraging generative models—ChatGPT for textual prompt construction, Doubao for image synthesis, and Kling for motion video generation—we design and implement a modular pipeline that enables non-technical users to generate culturally grounded visual narratives. A multi-dimensional evaluation framework comprising eight criteria (e.g., motion fidelity, costume accuracy, communicative clarity) was applied to assess output quality using a structured audience study (n=15). Results show that while current generative models provide high visual appeal and pedagogical value, they fall short in replicating nuanced choreography and attire without domain-specific data. This work contributes a reproducible methodology for low-resource cultural digitization, and outlines future enhancements through motion capture, ontology-based prompt refinement, and human-in-the-loop feedback mechanisms.

The disappearance of modernist artworks through the historical conflict, material degradation and partiality of archival regimes poses a continuous problem in terms of art history and cultural heritage conservation. It is a foregone conclusion that such works are difficult to reconstruct, since modernist aesthetics is focused on abstraction, fragmentation, and conceptual purpose over representational devotion. In this paper, the author suggests a reconstruction model based on AI that views artistic reconstruction as a probabilistic inference problem, as opposed to a deterministic recovery problem. The system is already based on the framework of representation learning, conditional generative modeling, uncertainty-aware sampling, and human-in-the-loop validation to produce several possible discovery hypotheses based on the existing evidence and style samples. The hierarchical style modeling strategy gives reconstruction possibilities at artist and movements levels, whereas explicit uncertainty signs circumvent overconfidence in interpretation. The framework is tested by using representative case studies which are partially documented, partially fragmented and entirely lost modernist artworks. The quantitative metrics of consistency, the analysis of perceptual styles, and uncertainty analysis and the qualitative evaluation process performed by experts prove that the reconstruction fidelity is proportional to the strength of evidence and that the uncertainty is explicitly magnified in the case of sparse constraints. The findings indicate the significance of AI as a supportive analysis tool that can aid the judgment of curators and art-historians but does not sacrifice the interpretative responsibility.

Generative artificial intelligence (AI) is reshaping creative practices, yet many systems rely on traditional interfaces, limiting intuitive and embodied engagement. This study presents a qualitative observational analysis of participant interactions with a real-time generative AI installation designed to co-create Ukiyo-e-style artwork through embodied inputs. The system dynamically interprets physical presence, object manipulation, body poses, and gestures to influence AI-generated visuals displayed on a large public screen. Drawing on systematic video analysis and detailed interaction logs across 13 sessions, the research identifies core modalities of interaction, patterns of co-creation, and user responses. Tangible objects with salient visual features such as color and pattern emerged as the primary, most intuitive input method, while bodily poses and hand gestures served as compositional modifiers. The system’s immediate feedback loop enabled rapid learning and iterative exploration and enhanced the user’s feeling of control. Users engaged in collaborative discovery, turn-taking, and shared authorship, frequently expressing a positive effect. The findings highlight how embodied interaction lowers cognitive barriers, enhances engagement, and supports meaningful human–AI collaboration. This study offers design implications for future creative AI systems, emphasizing accessibility, playful exploration, and cultural resonance, with the potential to democratize artistic expression and foster deeper public engagement with digital cultural heritage.

This interface of generative artificial intelligence (AI) and sacrosanct locale design is a golden possibility to reinvent spiritual locale spatiality in a manner that honors the past whilst incorporating technological enhancement. Sacred architecture has been used traditionally as tangible medium of cultural, religious, and historic values. Nevertheless, in present design practice, it appears that the interpretation of spiritual symbolism is not always compatible with the changing aesthetic, environmental and functional requirements. This paper discusses the new role played by generative AI, e.g., Midjourney, Stable Diffusion and ChatGPT, in the conceptualization, visualization, or adaptation of sacred sites. Using the expertise of interdisciplinary studies in the field of architecture, religious studies, preservation of heritage, and artificial intelligence ethics, we explore how artificial intelligence-based systems can validate culturally oriented, community sensitive, and emotional resonant religious spaces. We analyze case studies of architectural and educational imagery of Islam, Qinghua porcelain technology, and Iznik re-imagined tiles, and Arabic calligraphy, and pedagogical and neuro-adaptative uses. It develops a design framework, which includes cultural, AI generated ideation layers, and human- in-the-loop evaluation processes to provide spiritual logicalness and contextual sensitivity. In addition, the crucial ethical issues related to religious deception, cultural prejudices, and confidence in AI sacred images are covered in the paper. We believe that, like in the 25 current studies, generative AI has only to be a tool, although it can be effectively used as a collaborative co-actor during the adaptive design of sacred spaces, to bridging the gap between tradition and transformation at the digital age.

Reassembling broken pottery is a labor-intensive yet demanding task. It often requires comparison of fragments, careful spatial alignment, and expert interpretation. This makes pottery restoration difficult to scale in archaeology and cultural heritage preservation. In this demo, we present SherdAgent, a novel interactive multimodal AI agent for pottery sherd reassembly and restoration, leveraging recent advanced AI models. The system brings fragment understanding, 3D reassembly, and digital restoration into one unified interface. It is designed not only as a technical tool, but also as a human-in-the-loop platform for archaeologists, conservators, and digital heritage researchers. Given images of excavated pottery sherds, SherdAgent first analyzes visual and semantic cues to group related fragments. It then lifts the grouped sherds into 3D using a pre-trained image-to-3D model, and supports spatial reassembly by an off-the-shelf 3D reassembly model. Finally, the system restores missing regions and generates a coherent digital pottery model with a tailored generative AI model. Through natural interaction, users can inspect intermediate results, refine the reassembly process, and obtain complete 3D restorations more efficiently. Our live demo highlights how recent multimodal AI can support cultural heritage workflows in an intuitive and creative way. It shows the potential of AI agents to significantly reduce manual effort and improve restoration efficiency, as well as open new possibilities for archaeological analysis, digital preservation, and virtual exhibition.

Traditional Kelantan songket is more than just a handcrafted textile; it serves as a vibrant visual expression that embodies the socio-cultural values and semiotic identity of the Malay community. For centuries, skilled weavers have woven ecological insights, spiritual beliefs, and community standards of conduct into carefully designed textile patterns. However, modern academic discussions frequently fail to assess songket merely as a fixed material craft, thus neglecting the structural semiotic processes that shape meaning-making within the fabric. This critical analysis explores the convergence of textile design geometry and semiotic theory through a dual-layer model of signification comprising external physical denotation and internal connotative meaning, which are then applied to songket ornamentation, with a particular focus on deconstructing nearly extinct organic motifs such as the Ketam Guri. A comprehensive review of the literature shows that this heritage sector is facing a twofold crisis. On one side, a fear of the overlooked pattern arises from the inability to record the implicit knowledge of expert weavers in computational systems, which causes unnoticed cultural loss alongside the effects of a surge in inexpensive imported prints. Conversely, the escalation of global Generative AI technology has been noted to lead to significant semiotic breaches; as these algorithms do not possess the metadata required for genuine design principles, they generate visual anomalies that are fundamentally flawed. This review suggests a human-AI collaboration framework using a human-in-the-loop method to create a cohort of contemporary hybrid artisans capable of applying deep-rooted learning to secure traditional design principles digitally.

Indian Knowledge Systems (IKS) encompass a large body of intellectual, scientific, medical, linguistic, philosophical, mathematical, astronomical, literary and cultural knowledge preserved through manuscripts and related documentary traditions. A substantial part of this heritage exists on palm leaves, paper, birch bark and other materials whose physical deterioration can continue even after digitization. Faded ink, stains, bleed-through, uneven illumination, surface damage, low resolution and missing character strokes reduce the usefulness of digital images for both human reading and automated text recognition. This paper develops and elaborates an artificial intelligence (AI)-assisted framework for digital preservation of degraded Indian manuscripts. The framework combines repository-based acquisition, metadata and provenance capture, image-quality assessment, conventional preprocessing, degradation-aware deep-learning restoration, selective generative inpainting, super-resolution, optical character recognition (OCR), quantitative evaluation and expert validation. The experimental component described in the original study uses approximately 100 manuscript images collected from open-access digital collections. Reported pilot results indicate SSIM improvements of approximately 0.05–0.10, PSNR improvements of 2–6 dB, contrast improvements of 20–35%, noise reduction of 20–35%, edge-preservation improvement of 15–30% and OCR accuracy improvement of approximately 10–20 percentage points. Character Error Rate (CER) and Word Error Rate (WER) were reported to decrease by approximately 10–25% and 10–30%, respectively, with character recognition accuracy reaching approximately 85–90%. These results are interpreted as evidence that enhancement can improve machine readability and access, while not constituting proof that AI-generated pixels are historically correct. The expanded framework therefore separates original evidence, algorithmically enhanced representations, and reconstructed content; preserves processing provenance and places scholars in the validation loop. The paper also situates the framework within recent developments in Indian manuscript digitization, historical document analysis, trustworthy AI and the Gyan Bharatam initiative. The central argument is that AI should be deployed as an assistive preservation technology that increases discoverability and research access without replacing the original manuscript or scholarly judgement.

Intangible Cultural Heritage (ICH) has always been a major component of cultural identity and its safeguarding through digital technologies has also been developing. The current research merges the Technology Acceptance Model (TAM) witeth Media Richness Theory (MRT) to emphasize the increasing public acceptance of digital ICH experiences using Alternate Reality/Virtual Reality (AR/VR) storytelling, social media participation, and AI-produced narratives. A mixed-methods research design was applied in which the quantitative part consisted of surveying 350 digital ICH-aware participants and the qualitative part included conducting semi-structured interviews with 15 respondents. Thematic analysis was implemented on their responses to provide depth of experience. The findings indicate that the key facilitators and barriers demonstrated immersive and perceived usefulness of the technology and users’ attitudes influenced mainly by authenticity. Therefore, the research provides both theoretical and policy-based solutions on how ICH can be best promoted, or even preserved, through the different digital technologies used.

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The Asia Minor Catastrophe of 1922 constituted a major rupture in cultural continuity, leading to forced displacement and the fragmentation of both tangible and intangible cultural heritage. In this context, the Museum of Asia Minor Culture in Aigaleo seeks to preserve and communicate the traditions, memories, and lived experiences of Asia Minor refugees. This study investigates the design and development of a mobile application that employs interactive digital storytelling to document and disseminates Intangible Cultural Heritage (ICH) linked to displacement and refugee memory. A mixed-methods approach was adopted, combining qualitative materials such as oral histories, archival photographs, and museum artifacts—with the design and prototyping of a mobile application using contemporary development frameworks. Digital storytelling techniques guided the integration of multimedia elements, including audio narration, visual content, and interactive pathways, while user-centered design and usability testing involved museum visitors and cultural experts. Evaluation results indicate that the application enhanced visitor engagement, supported contextual understanding of exhibits, and strengthened connections between tangible objects and intangible traditions. The study concludes that mobile digital storytelling can function as an effective interpretive tool for museums, contributing to the preservation, accessibility, and communication of ICH, particularly in institutions with limited resources and audiences seeking more experiential forms of engagement.

Abstract. This paper documents and reflects on the process of preserving and presenting the ICH in the form of a virtual museum by taking the case study of a China’s national-grade intangible cultural heritage – the Hungry Ghosts Festival organized by the Chaozhou community in Hong Kong. The documentation, exhibition and interpretation in this virtual museum adopt a participatory bottom-up approach, in which the voices of the local organizers and participants are highlighted, and personal anecdotes are played up. The virtual museum intends to aggregate and exhibit all the elements of this ICH in one virtual space, including its origins and development, the tangible artefacts it entails, its dynamic and fluidic nature (e.g., ambiance, performance, communal spirits), and its multiple layers of meanings. As the Hungry Ghosts Festival celebration lasts merely a matter of days each year at a particular place for a local community, this virtual museum enables a wider public to access this festival at any time, in any place and from different dimensions. This paper shows how the virtual museum presents the aforementioned facets of the ICH through different display techniques and evaluates their usefulness, including (1) traditional text and image panels, (2) 3D reconstructed scene and models, and (3) 360-degree video. This is significantly different from the earlier work, which mainly focused on the accuracy of re-enacting the given form of intangible cultural heritage, such as performance and craftsmanship in a virtual environment, or a particular creative presentation by professional performers and elites with specific meanings.

Traditional ceramic patterns are an essential component of intangible cultural heritage, embodying aesthetics, region, and cultural identity. However, due to age, insufficient documentation, and the decline of traditional craftsmanship, many of these visual resources are at risk of decline and loss. To address this challenge, this study proposes an AI-based generative framework for the digital preservation and reconstruction of ceramic heritage patterns. This study constructs a dataset of over 2,000 high-resolution ceramic pattern images from museum archives and academic resources, covering representative categories such as blue-and-white porcelain, celadon, and underglaze red. The proposed method utilizes a modified CycleGAN architecture enhanced with an attention mechanism and cultural consistency constraints to capture structural patterns and stylistic authenticity. Experimental results demonstrate superior performance compared to baseline models, improving the Fréchet Inception Distance (FID), Structural Similarity Index (SSIM), and Cultural Consistency Score (CCS). User evaluations involving ceramic experts and design professionals further validate the model's ability to generate visually faithful and culturally authentic patterns. This research highlights the potential of generative AI in preserving intangible cultural heritage while promoting creative innovation in digital ceramic design.

As immersive media increasingly mediate cultural heritage experiences, a key challenge is how to evaluate content deployed across diverse Extended Reality (XR) platforms. This paper proposes a three-axis evaluation framework - Interpretive, Cognitive/Perceptual, and Affective/Sensory - that synthesizes constructs from museum studies, Human Computer Interaction (HCI), and presence theory into a comparative structure. The framework is applied to "Who Killed Helene Pumpulivaara?", a narrative experience produced in both WebXR and Virtual Reality (VR) formats, with a Spatial Augmented Reality (SAR) version underway. Using user feedback from pilot workshops and AI-assisted design workflows, the study identifies how platform affordances reshape meaning, usability, and emotional impact. The resulting matrix supports transparent, design-grounded evaluation and offers transferable insights for XR storytelling across modalities.

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ABSTRACT The city archives of ‘s-Hertogenbosch (the Netherlands) hold extensive World War II heritage, including diaries, letters, and photographs. To bring these stories to life an interactive exhibit was developed entitled ‘Encounters in Wartime’. This mobile, interactive exhibit, uses speech recognition technology to enable visitors to engage with three WWII-era characters through interactive dialogues. Using the four-stage conceptual model of virtual heritage preservation for visitor experience as a framework, this case study describes the design and development process of the exhibit, highlighting the challenges of applying storytelling tools and interactive technology to intangible heritage. We report on a pilot version of the exhibit, which was tested with visitors to a public event in ‘s-Hertogenbosch. The findings indicate high visitor engagement and emotional connection with the characters as a result of the digital presentation. The study concludes with practical implications for integrating interactive technology and storytelling in the context of intangible heritage.

Cultural Heritage is not just about tangible artifacts; it also includes intangible elements such as personal memories, community ties, and envisioned futures. Traditional museums and archives often emphasize physical items like architectural pieces and photos, while overlooking people’s personal and emotional connections to cultural heritage. To illustrate the personal connections people have with cultural heritage sites, we designed an exhibition that displayed images created by participants, which represent their perspectives and future visions of cultural heritage sites. The exhibition’s images, generated through GenAI, helped participants narratively describe cultural heritage locations, allowing them to express their visions of future threats like over-tourism and climate change on these sites. Contrary to constraints, co-creating with Generative AI associates participants with personal memories of cultural heritage, stimulating personal narratives and promoting deep reflection on cultural heritage preservation. The dissemination strategies we designed illustrate the use of GenAI to empower the expression of matters of cultural value beyond the physical.

This pilot study examines the validity of an AR gamified digital storytelling installation that is based on Malaysian traditional kuih to engage visitors with Malaysian traditional cultural heritage. Titled Kembara Jejak Warisan Kuih Muih, the interactive installation integrates gamification mechanics, digital storytelling, and AR visualisation to revitalise intangible cultural heritage through immersive, participatory experience design. This installation was exhibited at Interface: Where Visual Meets Poetry in GMBB Kuala Lumpur and was measured using the Museum Experience Scale, a valid multidimensional framework for assessing visitors' engagement and experiential responses. Fifteen visitors who repeatedly encountered the installation were purposively selected as the data sample. The results indicate that levels of engagement, emotional bonding, immersion, cultural learning and usability are high. The participants reported significant meaning-making through interaction and expressed great interest in revisiting such interactive heritage experiences. Findings indicate that gamified AR digital storytelling can be employed as an innovative way to communicate and perpetuate intangible cultural heritage within exhibition contexts. This pilot study explores issues of interactive heritage preservation, digital exhibition design, and experiential storytelling in Southeast Asian visual culture, marking the first move in these discussions. It also demonstrates the suitability of the Museum Experience Scale (MES) for evaluating hybrid interactive art and heritage installations. It provides a foundation for future large-scale studies on gamified AR heritage engagement.

: This study prеsеnts a comprеhеnsivе study focused on four distinct cеramic artifacts, rеvеrеd burial offеrings rеtriеvеd from a chambеr-tomb sitе dating back to thе latе bronzе agе (LH IIIA1 pеriod) in thе rеgion of "Vagia, " Kalapodi, East Locris, Grееcе. Emphasizing thе integration of cutting-еdgе digital tеchnologiеs in archaеological rеsеarch, specifically 3D scanning and 3D color printing mеthodologiеs, this study aims to depict thе matеrials and intricatе dеsign aspеcts of thеsе artifacts. Thе artifacts, comprising of vеssеls and ritual objеcts, wеrе mеticulously scannеd using advancеd 3D scanning tеchniquеs to gеnеratе high-rеsolution digital modеls. Thе utilization of this technology allowеd for thе dеtailеd documеntation and prеsеrvation of thеsе culturally significant itеms. Additionally, thе potential application of 3D color printing еnablеs thе crеation of physical rеplicas, providing tangiblе manifеstations for furthеr study and public еngagеmеnt whilе aiding the prеsеrvation of thе original artifacts. Thе analysis conductеd on thеsе acquired modеls rеvеalеd intricatе dеtails prеviously unsееn, shеdding light on thе craftsmanship, symbolic motifs, and probablе functions of thеsе artifacts within thе socio-cultural contеxt of thе latе bronzе agе sociеty in this rеgion. Furthеrmorе, comparativе studiеs with similar artifacts from contеmporanеous sitеs offеrеd valuablе insights into rеgional variations in cеramic production tеchniquеs and artistic stylеs. This intеrdisciplinary approach, combining archaеological еxcavation with digital scanning and 3D printing tеchnologiеs, not only contributes to thе comprеhеnsivе documеntation and prеsеrvation of thеsе invaluablе artifacts but also offеrs new insights for еnhancеd intеrprеtation and undеrstanding of thе latе bronzе agе matеrial culturе in Eastеrn central Grееcе.

Ethno-touristic sites must be conserved to preserve the cultural heritage and historical identity of the people. Documenting these sites is easier with enhanced digital technology, which provides faster and new ways of documenting, conserving, and managing tourism. Digital technology such as 3D scanning, virtual reality (VR), augmented reality (AR), and photogrammetry can potentially preserve Ethno-touristic sites and promote them. By analyzing the application of these technologies, the paper highlights their potential to enhance cultural narration, improve accessibility, and ensure the sustainability of intangible and tangible heritage. The research also discusses ethical considerations and methodological approaches to digitizing ethnographic resources, with emphasis on the need for sustainable and community-based digital archiving practice. The study suggests that the integration of digital technology with Ethno-tourism can potentially enhance the visitor experience and conservation of heritage amidst a constantly evolving technological landscape.

Abstract The small island nation of Cyprus has had a long history of conflict – one that is marked in the morphology of the urban environment and in the Buffer Zone that segregates the nation – a space of redaction in the Cypriot landscape, and an enduring spatial division between the north and south of Cyprus. The restricted access to the Buffer Zone currently limits quotidian memory practices and the ability to engage with both individual and collective meaning-making. This paper outlines a research project that we have been conducting within Cyprus to create an open-access digital archive and explore the potential of 3D scanning to enable new forms of engagement with cultural heritage and a sensitive negotiation of spaces of conflict and loss. Through this work, we explore the idea of heritage-as-negotiation, witnessing, and meaning-making that gives voice to past events that shape and haunt the present.

The Dazu Rock Carvings in Chongqing, China, represent a unique confluence of artistic innovation, religious practice, and socio-cultural continuity from the Late Tang through the Southern Song periods and into the present day. This study investigates the carvings' stylistic evolution through five principal typologies—monumental narrative reliefs, votive niches, lay feast shrines, protective deity niches, and hybrid forms—each revealing shifts in religious expression, political context, and community engagement. By applying iconographic and spatial analysis, the paper explores how visual culture functioned as a tool for moral instruction, social cohesion, and identity construction. Framed within the broader discourse of cultural sustainability, the study also considers how contemporary digital technologies—such as 3D scanning and virtual preservation—contribute to the safeguarding and revitalization of this living heritage for future generations.

The digital study of the historical and sacred parts of the church of Thillois aimed to enhance understanding and preservation of this significant heritage site, severely impacted by the First World War. By employing digital tools and technologies, this study facilitated the creation of a comprehensive 3D model of the church, incorporating detailed analyses of historical archives and the restored building. This effort not only reconstructed the physical attributes of the church but also addressed the sociocultural impact of the war on the local community. Key stages of the study included detailed computational modeling, photogrammetry, and the integration of Virtual Reality (VR) to provide an immersive experience for users. The study highlighted the challenges and limitations of digital restoration, such as the passive nature of video animations and the physical distance between the observer and the digital model. Nevertheless, the VR experience allowed users to interact with the model, enhancing accessibility and engagement with the heritage site. The research underscored the importance of consistent and regular documentation to support future studies and enhance collective memory. Digital methods and tools complemented traditional documentation, offering new ways to access, reformulate, and understand historical information. The study’s findings have significant implications for similar heritage sites in the Reims region and beyond, demonstrating the social and cultural potentials of digital technologies, fanning the flames of collective memory as a source of social and civic cohesion, and virtually and more intensely restoring architectural spaces lost or altered by historical events.

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Creating a digital 3D model of museum artefacts has been a common practice for many years. Such models can be used for archiving, research, and marketing purposes, as well as to counteract various types of exclusion. A digital copy created using professional 3D scanners using 3D structured-light scanning (3D SLS) or terrestrial laser scanning technology requires expensive equipment, specialised software for postprocessing, and a trained team. The introduction of mobile phones with Light Detection and Ranging (LiDAR) sensors and the development of appropriate open-access software have enabled the use of phones to generate digital 3D models. This study compares the quality of 3D models created with 3D SLS and mobile LiDAR technologies using three identical small museum artefacts from the Silk Road area of the Samarkand State University museum in Uzbekistan. They were digitised in 2017 and 2025. The results indicate that digital 3D models generated with an iPhone 16 PRO MAX device using Scaniverse LiDAR software are incomplete and thus less versatile. Therefore, they cannot serve as archival models. Their accuracy and quality (mesh density, size, and texture quality), as well as the speed of generating 3D models, make them ideal for marketing purposes and digital tourism.

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In artificial intelligence (AI), generative systems, most notably Midjourney, have tremendous power to generate creative images of buildings and sites of Islamic architectural heritage through text-to-image generation based on the internet. The AI-generated representations have significant potential for architects, specialists, and everyday users. However, the system has considerable limitations when generating images for some buildings and sites where the representations appear too far from their original represented structures. Evaluating Midjourney as an architectural virtual lab, this research article attempts to define the major current limitations of using Midjourney’s AI system in producing images similar to the actual buildings and sites of Islamic architectural heritage. This research employs prompt engineering techniques based on historical sources as inputs to examine the accuracy of the output of the AI-generated images of selected examples of structures of the Islamic tradition. To validate the research results, it compares the Midjourney output with the original look by employing direct observation through critical analysis of human intelligence (HI) facilitated by the analysis of the latest version of 3M Visual Attention Software and an online survey. It concludes that while Midjourney can represent high-end AI-generated images inspired by the Islamic tradition, it currently falls short of presenting the actual appearance of some of its original structures. Furthermore, it categorizes these limitations into four groups: (1) limits of the prompt, (2) limits of fame, (3) limits of regionality and historical styles, and (4) limits of architectural elements and details. The significance of this research lies in providing timely evaluation factors to architectural heritage practitioners and software developers. This practical article is the second in a series of two sequential articles in the Buildings journal; the first (theoretical) article introduces the concept of Artificial Intelligence Islamic Architecture (AIIA), answering the question: what is Islamic architecture in the age of artificial intelligence?

AI-generated content has grown significantly in recent years. Today, AI-generated and human-made images coexist across various settings, including news media, social platforms, and beyond. However, we still know relatively little about how audiences interpret and evaluate these different types of images. The goal of this study was to examine whether image interpretation is influenced by the origin of the image (AI-generated vs. human-made). Additionally, we aimed to explore whether visual professionalization influences how images are interpreted. To this end, we presented 24 AI-generated images (produced using Midjourney, DALL·E, and Firefly) and 8 human-made images to 161 participants—71 visual professionals and 90 non-professionals. Participants were asked to evaluate each image based on the following: (1) the source they believed the image originated from, (2) the level of realism, and (3) the level of credibility they attributed to it. A total of 5152 responses were collected for each question. Our results reveal that human-made images are more readily recognized as such, whereas AI-generated images are frequently misclassified as human-made. We also find that human-made images are perceived as both more realistic and more credible than AI-generated ones. We conclude that individuals are generally unable to accurately determine the source of an image, which in turn affects their assessment of its credibility.

Recent advances in generative artificial intelligence (AI) have enabled the creation of AI-generated characters modeled after historical figures, offering new opportunities for reflective and interactive engagement in both cultural heritage and education. This study explores the development and evaluation of a large language model representation of Joseph Lister (1827–1912), a pioneer of antiseptic surgery, within a retrieval-augmented generation framework. The purpose was to examine the model’s accuracy, authenticity, and reliability, highlighting challenges, best practices, and ethical considerations. Drawing on primary and secondary sources, including Lister’s writings, the model was constructed using OpenAI’s GPT-4o and refined through iterative validation. Prompts were categorized by cognitive complexity, and responses were evaluated against historical materials. The findings revealed a strong fidelity to Lister’s voice, with appropriate tone, diction, and temporal limits. Moreover, the model demonstrated behavioral control, reflective depth, and consistency across the different prompts. However, minor lapses in temporal framing and occasional embellishments were noted. The findings suggest that, when developed with care, AI-generated characters can support ethically grounded, historically sensitive learning experiences. At the same time, this approach warrants continued scrutiny and underscores the need for further interdisciplinary research and responsible implementation.

ABSTRACT Participatory Design (PD) and co-design have seen growing adoption in digital cultural heritage (CH) projects, yet there is limited orderly understanding of how these approaches are implemented across diverse contexts. This paper presents a systematic review of forty publications (2014–2024) focusing on the ‘making’ phase , and the PD organisation framework. We examine participant compositions, methods, tools, techniques, and digital outputs, mapping trends across museum environments, intangible cultural practices, archival documentation, and immersive technologies (AR/VR). Our analysis reveals a recurring tension between advanced technological solutions and the need for authentic, community-driven design, highlighting a need to ensure culturally grounded CH experiences. We also find that co-design, in which participants shape concepts throughout the design process with co-creative tools, remains uncommon, with many projects following more generic PD approaches that predominantly involve users during earlier or later evaluation stages. Nevertheless, the review showcases a growing inclusivity in heritage initiatives, addressing immigrant groups and accessibility concerns. A key contribution is an extended taxonomy encompassing participant roles, heritage fields, and digital experience types, offering a structured lens for practitioners and researchers to consider for future co-creative CH projects.

This paper explores the concept of “co-design” in digital heritage based on a proposed project that aims to develop a digital public engagement tool for a heritage site in Africa namely the Brandberg National Monument Area (BNMA) in Namibia. The heritage material within the BNMA has, over the years, been researched and digitised within an open repository hosted by the University of Cologne in Germany. The project intends to use the pre-existing digital content, which currently serves an academic research intent, to develop a tool that can be used by the general public to engage with the rich existing collection. To this extent, the project seeks to use stakeholders of the BNMA in designing a solution in the form of a digital tool. The project proposes a different approach to co-design which includes the combination of Design Science Research with Participatory Action Research.

Abstract Background Digital mental health interventions (DMHIs) offer a scalable approach to address adolescent depression and anxiety. User-centered coproduction can optimize acceptability and engagement, but it is often resource-intensive. Advances in generative artificial intelligence (GenAI) create new opportunities for involving adolescents in co-design, yet research on its feasibility and acceptability, particularly in low-resource settings, remains underexplored. Objective This study aimed to explore adolescents’ experiences and perspectives of using GenAI to co-design stories, images, and music for the Kuamsha app (Sea Monster), a gamified DMHI that teaches behavioral activation through interactive narratives and peer support. Methods Overall, 2 participatory workshops and focus group discussions were conducted with 23 adolescents (aged 15‐19 years) in rural Mpumalanga, South Africa. Participants were guided to use 3 GenAI tools—ChatGPT (OpenAI), text-to-story; MidJourney (MidJourney Inc), text-to-image; and Soundful (Soundful Inc), music generation—to create digital content. Data were audio-recorded, translated, transcribed, and triangulated with the facilitator’s observation notes. Thematic analysis was used to explore key themes. Results Almost all participants (22/23, 96%) had no prior exposure to GenAI. The majority (20/23, 87%) described the creative process as enjoyable and engaging, with most (21/23, 91%) reporting that creating music improved their mood. Adolescents expressed autonomy and ownership of the process, with more than half (14/23, 61%) personalizing outputs to reflect their identities and aspirations. All participants (23/23, 100%) preferred artificial intelligence (AI)–generated images over the cartoon-like illustrations of the Kuamsha app, and most (19/23, 83%) preferred AI-generated music. Story preferences were more mixed, with about a quarter of participants (6/23, 26%) recalling that Kuamsha’s narratives contained embedded lessons that were not integrated into the ChatGPT outputs. Most adolescents (18/23, 78%) required support with prompt construction, and more than half (13/23, 57%) noted cultural biases in AI outputs, particularly in images. Most participants (17/23, 74%) expressed interest in using AI for schoolwork and creative projects, while a minority (6/23, 26%) preferred to limit use to personal applications. Concerns about fairness and the displacement of human creativity were also raised. Conclusions GenAI shows promise for enhancing adolescent engagement in the coproduction of DMHIs and enabling culturally relevant and personalized content. However, reliance on human support and persistent algorithmic biases remain limitations. Further research should explore the integration of therapeutic principles into AI-generated media and strategies to mitigate bias.

As digital infrastructures and methods increasingly shape how cultural memory is preserved, accessed, and interpreted, questions of collaboration and participation have become central to both research and pedagogical practices in digital humanities and cultural heritage contexts. This panel explores the promises and challenges of participatory approaches to digital humanities and cultural heritage work. Bringing together five speakers in various domains of digital humanities, community archives, and digital curation, this panel offers multiple perspectives on how to engage different communities of interest, such as students, interdisciplinary scholars, librarians and practitioners, as well as local communities, in participatory digital humanities and cultural heritage work. Following the individual presentations, panelists will facilitate open discussions with attendees, seeking to collectively explore questions including how to design participatory work in digital humanities and cultural heritage practices, how to engage communities and collaborators in participatory work, and how to address the challenges that emerge in participatory processes. Through this collaborative and interactive approach, this panel seeks to advance knowledge production practices of digital humanities and cultural heritage, advocating for a “participatory future” of digital humanities and cultural heritage work. This panel will be sponsored by ASIS&T SIG‐AVC if accepted.

To highlight and foster diabolo culture, we collaborated with the diabolo community using a participatory design methodology to co-create and refine a Digital Diabolo VR game. The comprehensive process encompassed preliminary research, design workshops, in-depth design, preliminary testing, and user testing, with varying degrees of engagement from the diabolo community at each phase. This endeavor culminated in the successful development and exhibition of the Digital Diabolo VR game, showcased at the Beijing Diabolo Museum and in public spaces, significantly contributing to the dissemination of diabolo culture. Additionally, it provided valuable insights into applying participatory design principles to the preservation and digitization of intangible cultural heritage.

This poster presents the “Intergenerational Participatory Co-design Project,” an interdisciplinary initiative at the University of Hong Kong for facilitating collaboration among different age groups to design digital historic preservation. This project reimagines the global challenge of aging as an opportunity to enhance cultural heritage when older and younger members of society share their unique knowledge and perspectives. Over the course of the 2019–2020 academic year, four mixed-age groups co-designed a variety of innovative digital products to support the preservation and appreciation of Hong Kong's historic culture. The guiding principle of the project was to engage the participants as co-creators of both their own learning outcomes and learning processes. The participants also had opportunities to develop skills with new technologies for documenting, preserving, and presenting cultural heritage. The University of Hong Kong Libraries served as the central space (both physically and virtually) for facilitating these activities, in partnership with the University's Sau Po Centre on Ageing, the Common Core program, and the Faculty of Education. This project can serve as a model for how libraries can support local communities to digitally embrace an aging society for enhancing cultural heritage.

This study explores the challenges and opportunities of digital revitalization in preserving intangible cultural heritage, focusing on the Padang Magek Weaving revitalization through a 3D virtual co-design process. The research examines the role of Minangkabau customary women’s organizations, particularly the Bundo Kanduang Institution, in ensuring cultural sustainability. A case study approach was employed in Nagari Padang Magek, utilizing participatory observation and in-depth interviews with weavers, customary figures, and government decision-makers. Data were analyzed through critical descriptive analysis to examine the dynamics of women’s agency amid digital technology intervention. The findings highlight that culturally sustainable revitalization depends on positioning the Bundo Kanduang Institution as a key decision-maker, not a marginalized part of the male-dominated customary structure. The resistance of Bundo Kanduang to changes in weaving motifs (color, silhouette, meaning) demonstrates their crucial role in preserving cultural authenticity. Granting veto power to women weavers proved essential in strengthening their agency and reducing gender bias from political and community leaders. The study concludes that 3D technology, particularly Clo 3D, serves as an empowering tool for Minangkabau women’s cultural heritage, with the adoption of digital technology contingent upon validation from Bundo Kanduang. The research emphasizes the importance of a gender-responsive framework in implementing digital technology for cultural preservation.