AI Integration in Museums and Educational Experience Enhancement: A Systematic Review with Pingdingshan Museum as a Case Study
DOI:
https://doi.org/10.58557/(ijeh).v6i1.422Keywords:
Artificial Intelligence (AI), Case Study, Educational Experience, Museum Education, Museums, Systematic ReviewAbstract
The rapid advancement of artificial intelligence (AI) has created new opportunities for enhancing museum education; however, its effective pedagogical integration remains uneven and underexplored. Many museums still emphasize technological display rather than meaningful knowledge construction, resulting in limited educational impact across diverse visitor groups. Taking the Pingdingshan Museum as a case study, this study examines how AI is currently implemented in museum educational scenarios, identifies key bottlenecks in its application, and explores pathways for optimization. The primary objective of this research is to systematically analyze the current state of AI in museum education, identify technological and pedagogical constraints, and propose an integrative framework to enhance AI-driven educational effectiveness. To achieve this objective, the study adopts a mixed-methods research design. Quantitative data were collected through a questionnaire survey administered to 384 museum visitors between March and April 2025, while qualitative insights were obtained from semi-structured interviews with 12 cross-departmental personnel involved in museum management, education, and technology. The findings reveal that AI technologies significantly enhance visitor engagement, as evidenced by a 62.85% satisfaction rate with 3D cultural relic displays. Nevertheless, three major bottlenecks persist: limited cross-module data collaboration, reflected in a 63% interoperability rate between VR and collection management systems; outdated and insufficiently accessible equipment design, indicated by a low AI usage rate (34%) among visitors over 60; and misalignment between educational content and cognitive development rules, with only 52% knowledge comprehension among children. Based on these findings, this study proposes a three-dimensional optimization system encompassing technological refinement, service enhancement, and educational restructuring. The implications suggest that museums can move beyond superficial technological adoption toward AI-enabled knowledge construction, thereby strengthening their educational function in the digital era
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