Museums, as concepts and spaces, have historically been places for revisiting the past through artefacts. They merge temporal and spatial dimensions, providing visitors with historical information alongside a sensorial experience. In India, there are ongoing and continuous efforts to digitise almost all socio-cultural aspects, including education (Eg, NROER), economy (Eg, UPIs), libraries, museums, etc. In India, initiatives like the JATAN digital repository have sought to modernise museum collections and make them globally accessible (Sharma, 2021; Geismar & Müller, 2022). However, the literature on digital museums in India and other formerly colonized nations reveals a dynamic intersection of technology, heritage preservation, decolonial critique, and evolving user engagement. Shaped by complex legacies of colonialism, there are ongoing debates about representation, ownership, and the risk of digital cultural colonialism in these spheres (Gajjala, 2019; Mukherjee & Nizaruddin, 2022).

This paper showcases the current state of the art on publicly funded digital museums in India, and critically analyzes them through the lenses of memory, representation, and engagement. Through a critical lens and a postcolonial theoretical perspective, we tried to investigate whose memories are being preserved in digital museums in India. We sought to enquire about the influence of colonialisation on the collection and storage of historical memories of colonised people, and its lasting impact on contemporary efforts to digitally preserve cultural heritage. The impact of colonisation is not limited to the historical period of colonial rule; it lingers long after colonialism has ended and remains visible in conscious and subconscious day-to-day activities. Various scholars from the Global South have written about how colonisation creates a distinction which not only affects how others view ‘us’ but also how ‘we’ view ourselves (Chatterjee, 2011; Said, 1978). In this context, ‘us’ and ‘we’ refer to the colonised nation and its citizens. Drawing on this paradigm, we examined the ‘National Digital Repository and Portal for Museums of India’ (NDRPMI) project.

For analysis, we used the artefact metadata from the publicly funded and available NDRPMI website. The data was preprocessed using methods such as null-value filtering, AI-based information extraction, and standardisation. For instance, an LLM was prompted to retrieve the ‘location’ and ‘origin’ (if present) from the ‘item descriptions’ field in case either were, by default, missing. Similarly, standardization was carried out by removing multiple spellings of any variable classes, whether for location or item type, etc. To prevent leaking the data in training of AI models, it was decided to rely only on local AI models, finalising the Gemma3:12b model, over others, on a personal computer for this task (Gemma Team, 2025).

Figure 1: Interactive visualisation of Museum artefacts with different filters.

Corollary to the above prerequisites, we appended the relevant geopins associated with each artefact using GeoPy and visualised them alongside several filters using Folium as an interactive research map (geopy contributors, 2023; python-visualisation, 2025). For visualisation, the artefacts were divided into two categories. Firstly, those with mention of discrete locations such as Delhi or Hyderabad were plotted as geopins. In the subsequent category, artefacts which did not have geopinnable locations were appended to their respective countries and visualised as country-based density plots with concentration-based gradients extending between yellow and red (Fig. 1). This interactive map of the digital artefacts helped us explore our original question: ‘Whose memories are being preserved in these digital museums?’. Also, the clustering of artefact timeline with respect to their origin and location helped us validate their precolonial/ colonial/ or postcolonial heredity, which we finally plotted as a stacked bar graph (Fig. 2). Different AI prompts were experimented with and used throughout the process.

Figure 2: Classification of object types based on precolonial, colonial, and postcolonial era

The process of ‘engagement’ in such digital interfaces, as research suggests, depends on a variety of factors such as narrative clarity, interactivity, accessibility, multilingual interfaces, low-bandwidth options, and inclusive design elements that broaden audience reach (Priyadarshini, 2025; Chowdhury, 2025; Atata and Odedeyi, 2025). Besides, users engage more with heritage when they can locate themselves and their communities within it, highlighting the importance of ‘cultural relevance’ (Smith, 2006). In the pursuit of deciphering engageability, we examined the technologies used in these digitization projects and their effectiveness in archiving artefacts and engaging visitors. We asked: What factors influence engagement, and which digital tools or AI technologies can enhance it? Additionally, how do these algorithms select and organise historical texts and documents regarding the design process of these digitisation projects, including their intended audience and specific features? We also tried to infer, through this critical data study, which languages were most or least represented in India’s digital heritage. Our findings not only enhance the existing literature on digital and spatial analysis but also provide critical insights that could inform the planning and policymaking of digital infrastructures in postcolonial majority worlds, such as India. Our work will be useful for accessibility and multilingual inclusion, which often results in reduced engagement among non-English-speaking audiences. This research, besides validating the scope and validity of data-based critical enquiries, also posits how digital methods impact the accessibility and engagement in digital spaces.

References

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Softwares used:

geopy Contributors. (2023). geopy (Version 2.4.1) [Computer software]. GitHub. https://github.com/geopy/geopy

python-visualization. (2025). folium (Version 0.20.0) [Computer software]. GitHub. https://github.com/python-visualization/folium

Gemma Team, Google DeepMind. (2025). Gemma 3 (12B parameter variant) [Large language model]. https://ai.google.dev/gemma/docs/core/model_card_3