Modelling The Influence Of Antecedents Of Artificial Intelligence On Academic Productivity In Higher Education: A Mixed Method Approach.

Abstract

This study examined the effect of antecedents of artificial intelligence (AI) on the productivity of academics in higher education. The study was guided by the prag matic epistemic perspective predicated on the concurrent integrated mixed-method design used with the support of a Google softcopy version of the semi-structured questionnaire (closed and open-ended questions) to collect data from 663 academics from higher educational institutions in Ghana, Nigeria, South Africa, Mexico, Germany, India, and Uganda. The quantitative data were analysed with descriptive and inferen tial statistical tools while thematic pattern matching was engaged to analyse the qualitative data. The study found that academics hardly use the main AI tools/plat forms, and those mainly used for research and teaching-related activities were ChatGPT, OpenAI, and Quillbot. These AI tools were used mostly for general searches for information on course-related concepts, course materials, and plagiarism checks among others. The study further revealed that challenges associated with AI usage influenced the productivity of academics significantly. Finally, the availability of AI tools was found to engender AI usage but does not directly translate into the prod uctivity of academics. The study, therefore, recommended that the management of higher educational institutions espouse policies, and provide timely information and training on the use of AI in higher education. The policies, information, and training provided should specifically address how to adopt different AI tools for specific aspects of teaching tailored and gravitated toward catalysing the productivity of academics.