Artificial Intelligence in Ethiopian Higher Education: A Bibliometric Analysis of Research Trends, Systemic Adoption Barriers, and Policy-Infrastructure Disconnects - Belay Sitotaw Goshu; Muhammad Ridwan (BIRCU, 2026)
The rapid ascent of artificial intelligence is having a profound impact on higher education worldwide. However, the adoption of this technology in Sub-Saharan Africa is conspicuously absent from research scholarship. Ethiopia’s recent implementation of its National Artificial Intelligence Policy (2024) as part of the “Digital Ethiopia 2025” policy makes AI an axis of national development. However, this ambition is paradoxically contrasted with Ethiopia's position as one of the countries with the lowest levels of AI readiness globally, creating a mismatch between the boldness of national vision and infrastructural frailty. Aim: This paper systematically investigates the trends in AI research and systemic barriers to the uptake of AI within Ethiopian higher education, as well as the disjuncture between the National Artificial Intelligence Policy and actual institutional practices, using a mixed-methods bibliometric and thematic analysis. Methods: We employed mixed-methods bibliometric-synthesis design consisting of quantitative bibliometric analysis of academic publications indexed in Scopus and Web of Science between 2000 and 2026, combined with qualitative analysis of full-text articles and relevant policy documents. VOSviewer and Biblioshiny were used for bibliometric mapping of the network of authors, keywords, and citation analyses. Inductive thematic coding was applied to identify system barriers across four infrastructure, pedagogy, language, and human capacity. Results analysis reveals that Ethiopian AI scholarly output has sharply increased after 2023, indicating a nascent scholarship emerging rather than an integrated approach to systemic adoption. Using the UTAUT (Unified Theory of Acceptance and Use of Technology) framework, we show an ‘adoption paradox’: the performance expectancy construct remains relatively high, but the enabling conditions, such as unreliable electricity infrastructure, limited network connectivity, high cost of computer hardware, and lack of adequate teacher training facilities, remain critically weak.