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LIS Educators’ Perception Towards the Adoption of AI Tools in Nigerian Library Schools

Author

Listed:
  • Solomon Olusegun Oyetola
  • Bolaji David Oladokun
  • Kudu Dogara

Abstract

The incorporation of Artificial Intelligence (AI) into education marks a significant shift in how students learn, teachers teach, and educational institutions operate. This research delved into the knowledge and views of Library and Information Science (LIS) educators regarding the use of AI in library schools in Rivers State. The study employed a survey approach, combining qualitative and quantitative methods. A total of 44 LIS educators from various institutions in Rivers State participated, selected through random sampling, and data were collected using an online survey. The study found that while many LIS educators are aware of AI and have integrated it into their teaching and research, there remains a considerable gap in formal training and professional development in this area. Despite this, there is a clear understanding among educators of the value of AI in library and information science education, in line with broader trends in education and industry. The research also identified positive attitudes towards AI as a tool to enhance education quality and prepare students for careers in librarianship and information science. However, several barriers hinder the integration of AI into curricula and practices, including lecturer attitudes, credibility of information sources, internet connectivity, negative institutional perceptions, and low lecturer competency in AI. To address these challenges, the study recommends that Nigerian library schools fully implement AI technologies like chatbots, barcodes, RFIDs, and robotics to enhance teaching activities. It also suggests that higher education institutions develop specialized training programs and workshops on AI for library schools, covering both basic and advanced concepts. This would enable educators to effectively integrate AI into their teaching and research practices.

Suggested Citation

Handle: RePEc:dbk:metave:v:3:y:2024:i::p:65:id:65
DOI: 10.56294/mr202465
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