The Role of Emerging Technologies in Shaping Collective Memory

  • Authors

    • Mikhail Kartsev Chief Designer, Research Institute of Computer Systems, Russia. Author
    • Mstislav Keldysh Academician, USSR Academy of Sciences, Russia. Author

    DOI:

    https://doi.org/10.67228/3071561X/IJIRHT-2025PI2P5D

    Published 04-04-2025

  • Collective Memory, Artificial Intelligence, Digital Heritage, Emerging Technologies, Virtual Reality, Augmented Reality, Blockchain, Social Media, Cultural Preservation, Digital Archives

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    [1]
    M. Kartsev and M. Keldysh, “The Role of Emerging Technologies in Shaping Collective Memory”, IJIRHT, vol. 8, no. 1, pp. 01–14, Apr. 2025, doi: 10.67228/3071561X/IJIRHT-2025PI2P5D.

  • Abstract

    Collective memory refers to the shared understanding and preservation of historical events, cultural practices, and societal experiences within communities. Emerging technologies such as Artificial Intelligence (AI), Big Data, Cloud Computing, Blockchain, Augmented Reality (AR), Virtual Reality (VR), digital archives, and social media have transformed the creation, preservation, and dissemination of collective memory. These technologies enable large-scale information storage, interactive historical experiences, and broader public participation. This study examines the role of emerging technologies in shaping collective memory and digital heritage preservation. AI supports automated content organization and historical restoration, while AR and VR enhance user engagement through immersive experiences. Social media platforms facilitate real-time documentation and collective interpretation of events. However, challenges such as misinformation, algorithmic bias, privacy concerns, digital manipulation, and unequal access to technology may affect the authenticity and reliability of historical narratives. The findings indicate that emerging technologies improve accessibility, preservation, and public engagement, but effective governance, ethical AI practices, and inclusive digital preservation policies are necessary to ensure sustainable and trustworthy collective memory management. The study provides valuable insights for researchers, policymakers, and cultural institutions involved in digital heritage preservation.

  • References

    [1] Maurice Halbwachs (1992). On collective memory. University of Chicago Press.

    [2] Jan Assmann (2011). Cultural memory and early civilization: Writing, remembrance, and political imagination. Cambridge University Press.

    [3] Pierre Nora (1989). Between memory and history: Les lieux de mémoire. Representations, 26, 7–24.

    [4] Astrid Erll (2011). Memory in culture. Palgrave Macmillan.

    [5] José van Dijck (2007). Mediated memories in the digital age. Stanford University Press.

    [6] Andrew Hoskins (2018). Digital memory studies: Media pasts in transition. Routledge Handbook of Memory and Place, 85–97.

    [7] Lev Manovich (2020). Cultural analytics and artificial intelligence. International Journal of Cultural Studies, 23(3), 345–360.

    [8] Marinos Ioannides, Magnenat-Thalmann, N., Papagiannakis, G., & Fink, E. (2018). Digital heritage: Progress in cultural heritage documentation, preservation, and protection. Springer.

    [9] Erik Champion (2016). Critical gaming: Interactive history and virtual heritage. Routledge.

    [10] Christine Hine (2015). Ethnography for the internet: Embedded, embodied and everyday. Bloomsbury Academic.

    [11] Zizi Papacharissi (2015). Affective publics: Sentiment, technology, and politics. Oxford University Press.

    [12] danah boyd (2014). It's complicated: The social lives of networked teens. Yale University Press.

    [13] Sarah Kenderdine (2021). Embodied museography: Museum experiences through immersive technologies. Museum Management and Curatorship, 36(5), 461–478.

    [14] Yehuda Kalay, Kvan, T., & Affleck, J. (Eds.). (2008). New heritage: New media and cultural heritage. Routledge.

    [15] UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO Publishing.

    [16] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.

    [17] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845

    [18] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

  • Downloads