Role of Machine Learning in Enhancing Knowledge Sharing Practices Among University Faculty Members
Noor ul huda Choudhrya, Zainab Nazir Chaudryb, Sidra Kiranc, Saman Attad
- aGraduate Research Assistant, University of Oklahoma, USA
- bEdTech Educator, Aga Khan Education Service, Pakistan
- cAssistant Professor, Department of Education, Alhamd Islamic University, Islamabad
- dM.Phil Scholar, Department of Education, Alhamd Islamic University, Islamabad
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Abstract
This study examines the role of machine learning in enhancing knowledge sharing practices among university faculty members. In the contemporary digital era, machine learning technologies are increasingly being integrated into higher education to improve academic collaboration and knowledge management processes. The study aims to explore how these technologies influence faculty members’ knowledge-sharing behavior in universities. The objectives of the study were to examine the level of machine learning utilization among university faculty members, to investigate knowledge sharing practices among faculty members, and to determine the relationship between machine learning and knowledge sharing. A quantitative research design based on a cross-sectional survey method was adopted for this study. The population consisted of approximately 22 universities in Islamabad with an estimated 4,000 faculty members. A sample of 300 faculty members was selected using stratified random sampling technique to ensure proportional representation of public and private sector universities. The research tool was a structured questionnaire based on a five-point Likert scale. Validity of the instrument was ensured through expert review, while reliability was confirmed through Cronbach’s Alpha test, yielding acceptable values above 0.70. Data were analyzed using SPSS software through descriptive statistics, correlation, and regression analysis. The first objective revealed that faculty members demonstrate a moderate level of machine learning utilization, and it is recommended that universities should provide training programs and technical support to enhance advanced machine learning adoption among faculty members. The significance of the study lies in its contribution to educational technology literature, as it provides empirical evidence on how machine learning can improve knowledge sharing, support academic collaboration, and assist policymakers in developing technology-driven strategies for higher education institutions.
TL;DRExamination of the level of machine learning utilization among university faculty members revealed that faculty members demonstrate a moderate level of machine learning utilization, and it is recommended that universities should provide training programs and technical support to enhance advanced machine learning adoption among faculty members. (Semantic Scholar)