This study, which is based on the rapid application development methodology and unified theory of acceptance and use of technology-II (UTAUT2), presents the development of a generative artificial intelligence (AI) based tool for higher education institutions (HEIs), referred to as the knowledge management-large language models (KM-LLM) application. This study aims to examine how the practical use of this application by academics affects knowledge management (KM) processes.
The KM-LLM application has been designed based on retrieval-augmented generation (RAG) techniques and large language models (LLMs), in this case, GPT-4o, to support academic activities within HEIs. A quantitative research approach was adopted and survey data were collected from 10,321 academics in Iraqi public and private universities. The hypothesised hypotheses were tested with the help of partial least squares structural equation modelling with the help of SmartPLS software.
The study was able to create a prototype AI-driven KM application, KM-LLM, by combining LLM and RAG technologies. The KM-LLM application showed great potential in improving KM processes using GPT-4o to create a dedicated knowledge base of research and academic studies relevant to Iraqi universities. Moreover, the results demonstrate the degree of acceptance of KM-LLM tool in academia and identify the UTAUT2 constructs influencing the behavioural intention to adopt the application in academic practise.
To the best of the authors’ knowledge, this research is one of the first in developing a generative application of AI based on the development of KM processes in Iraqi HEIs. It fills an important gap in the literature in relation to the confluence of KM and LLM technologies, especially in the area of generative AI adoption by academic professionals – an area which is still largely under explored.
