Investigating Artificial Intelligent Literacy Awareness and Competencies of LIS Professionals in Bangladesh: A Comparison between Public and Private University Libraries

Abstract

The research explores AI literacy awareness of Bangladeshi Library and Information Science (LIS) professionals, focusing on their views on the purposes of using AI in public and private university library settings. It also compares the competencies of LIS professionals who need to work with AI-based tools in university libraries and the challenges of AI literacy in the library service environment. This qualitative study conducted in-depth interviews with 10 (ten) LIS professionals who were selected through the snowball sampling technique. Interview transcripts were coded and analyzed thematically for this research. This study visualizes the familiarity of AI terminology, AI concepts, and the use of AI tools among the respondents. The findings reveal that, in comparison to public university libraries, the participants of private university libraries are more inclined to apply AI tools to provide effective reference services. The findings indicate an optimistic trend in AI literacy among university library participants, stressing their knowledge of applying AI to various library activities, especially in cataloguing, classification, reference services, and indexing. Although LIS professionals demonstrate a positive attitude towards AI literacy competency, respondents at private university libraries show significantly greater competence in certain areas of AI literacy, such as the use of Natural Language Processing (NLP). Moreover, the analysis shows that the participants from both categories of libraries viewed the proficiency of the chatbot, ChatGpt, virtual assistant, and voice assistant as very important. The technical know-how and slow adoption rate of AI tools, threat to data privacy, high expanse of AI tools, fear of job replacement, and reluctance to embrace AI innovations are key challenges to AI literacy in university libraries. However, overcoming these challenges requires integrating AI literacy course into LIS curriculum, offering micro-credential courses for NLP, participating in data privacy and ethical AI practice related training, and formulating a national policy on AI literacy.

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