Navigating Barriers: Challenges and Strategies for Adopting Artificial Intelligence in Qualitative Research in Low-Income African Contexts

Abstract

Introduction: AI is transforming qualitative research. It enhances efficiency, accuracy, and depth in studies. Technologies like machine learning (ML), natural language processing (NLP), and large language models (LLMs) simplify tasks like transcription, coding, and thematic analysis. However, in low-income African settings, there are barriers to AI adoption. These include ethical concerns, infrastructure limitations, financial constraints, and technical skill gaps. Issues around data privacy and the dehumanization of research also add challenges.

Methods: This paper explores the challenges and opportunities of AI in qualitative research in low-income African contexts. It uses a descriptive approach, reviewing literature and personal experiences from rural African settings. The paper highlights how AI can democratize research, promote multilingual inclusivity, and improve analysis.

Results and Recommendations: To integrate AI effectively, capacity-building, ethical frameworks, infrastructure investments, community engagement, and partnerships are crucial. If these challenges are addressed, AI could empower researchers in low-resource settings. This would lead to more relevant, equitable qualitative studies and help tackle Africa's unique challenges. AI can drive informed decision-making in public health, education, and social cohesion.