Artificial Intelligence, Museum Environments and their Constituents: A Cross-disciplinary Study Using Recommender Systems to Explore Digital Collections

Student thesis: Phd


This thesis explores the contingent relationships between museum environments, their constituents, and AI technologies. It considers the challenges, issues and effects that arise when such technologies are applied to a museological context, through practice-based and empirical research on the museum sector. It asks questions about the role and potential of AI technologies in museums and their data practices and investigates in what ways AI challenges and/or enhances the public and museum professionals' perception of AI. To answer these questions, the thesis applies a novel combination of methods, including the development of a new recommender system to research AI technologies as experienced through their cultural engagement in public museums, positioning the institutions as interactive laboratories. This approach investigates the applicability and usability of algorithmic outputs in museum settings, addressing trust issues, testing new strategies, exploring content creation and the implications of its future use in a technically informed society. During this process, the recommender system is not perceived as definitive, but as an evolving object that is transient, changing and question-generating, establishing the RS both as a system for curating online museum experiences and a method in its own right. The research is informed by an empirical-philosophical framework forged out of a postphenomenological vocabulary which enables investigation of the socio-technical and cultural roles of the recommender system and its constituents through the relation humans have with technological artefacts. The thesis argues that AI needs to create value and become significant to constituents to become a more sustainable practice within museum environments and to translate its full potential onto the practices of institutions. Such practices afford collaborative approaches to harness the power of AI and address the challenges of pervasiveness and ubiquity of those technologies inherit, which can lead to mistrust and avoidance. The research concludes by confirming the contingency between museum environments, their varied constituents, and AI technologies; its findings have implications for museum practice and present a unique contribution to a developing interdisciplinary field.
Date of Award1 Aug 2023
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorCaroline Jay (Supervisor) & Abi Gilmore (Supervisor)


  • Artificial Intelligence
  • Recommender Systems
  • Digital collections
  • Museums
  • Machine Learning
  • Digital Humanities

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