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This study examines the barriers to and opportunities for integrating Artificial Intelligence (AI) in Uganda’s higher education sector. Although AI has globally transformed educational practices, its adoption in resource-constrained environments remain limited. Employing a qualitative, comparative multiple-case study design, data were collected from three universities through 60 semi-structured interviews and five focus group discussions. Thematic analysis, guided by Rogers’ Diffusion of Innovations Theory, identified key impediments such as inadequate digital infrastructure, financial constraints, limited faculty expertise, and institutional resistance. Stakeholders expressed cautious optimism about AI’s potential, tempered by concerns over ethical, regulatory, and operational issues. Based on these findings, the study recommends strategic interventions including infrastructural investments, targeted faculty training, robust policy frameworks, and collaborative partnerships to facilitate effective AI integration. This research contributes a context-specific perspective on enhancing AI adoption in higher education within low-resource settings.

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Transformative but Troubled: Examining the Barriers to AI Adoption in Uganda’s Higher Education System

  • 会议论文

  • 发表日期：

    2026年

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  • Saadat Lubowa Kimuli Nakyejwe,
  • Edwin Bulonge,
  • Samuel Walulumba,
  • Nashua Kimuli Nabaggala,
  • Eunice Ninsiima

摘要

This study examines the barriers to and opportunities for integrating Artificial Intelligence (AI) in Uganda’s higher education sector. Although AI has globally transformed educational practices, its adoption in resource-constrained environments remain limited. Employing a qualitative, comparative multiple-case study design, data were collected from three universities through 60 semi-structured interviews and five focus group discussions. Thematic analysis, guided by Rogers’ Diffusion of Innovations Theory, identified key impediments such as inadequate digital infrastructure, financial constraints, limited faculty expertise, and institutional resistance. Stakeholders expressed cautious optimism about AI’s potential, tempered by concerns over ethical, regulatory, and operational issues. Based on these findings, the study recommends strategic interventions including infrastructural investments, targeted faculty training, robust policy frameworks, and collaborative partnerships to facilitate effective AI integration. This research contributes a context-specific perspective on enhancing AI adoption in higher education within low-resource settings.