Effect of Artificial Intelligence Adoption on Research Efficiency Among Postgraduate Students: A Case Study of Makerere University, Kampala, Uganda
Year: 2026
Type: article
Abstract: Artificial intelligence (AI) applications are increasingly used to support research activities such as literature discovery, evidence extraction, data analysis, academic writing and the automation of repetitive tasks. This systematic literature review draft synthesises available peer-reviewed and policy literature on the potential relationship between AI adoption and research efficiency, with a proposed focus on postgraduate students at Makerere University in Kampala, Uganda. The review is organised around four dimensions of AI adoption: AI-assisted literature review, AI-assisted data analysis, AIassisted academic writing, and AI research support and automation. Research efficiency is considered in terms of time efficiency, research productivity, perceived research quality and progress towards research milestones. Existing evidence suggests that AI may accelerate selected tasks, but it does not establish that adoption automatically improves overall research performance. For example, Bašić et al. (2023) found no difference in average essay grades or writing duration between small ChatGPTassisted and control groups, while Prandner et al. (2025) and Bijker et al. (2024) describe both potential uses and limitations in quantitative and qualitative analysis. The review also identifies risks involving inaccurate outputs, fabricated references, privacy, and over-reliance. A reproducible search and screening protocol is specified; however, database search counts and the final included-study total must be inserted only after the searches and screening have actually been completed. The key gap is the limited context-specific evidence combining multiple AI-adoption dimensions with several indicators of postgraduate research efficiency in Uganda.
Author: AMINAH, NAMAGANDA
Institution: Kampala International University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7224956592