Integrating Artificial Intelligence in Higher Education in Zambia: A Mixed-Methods Study on Future Prospects and Challenges
DOI:
https://doi.org/10.57125/FED.2025.09.25.10Keywords:
artificial intelligence, Zambian higher education, mixed-methods research, personalized learning, educational technologies, future educationAbstract
Artificial intelligence (AI) has rapidly emerged as a transformative force in higher education, promising to enhance teaching, learning, and institutional efficiency. While the global adoption of AI tools, such as adaptive learning systems, chatbots, and predictive analytics, has accelerated, evidence from Sub-Saharan Africa, and Zambia in particular, remains scarce. This study employed a sequential explanatory mixed-methods design to investigate educators’ perceptions, adoption factors, and contextual challenges surrounding the integration of AI in Zambian higher education. Quantitative data were collected from 230 faculty members across five universities through structured surveys, followed by semi-structured interviews with 24 educators to provide explanations and further insights into the statistical findings. Quantitative results indicated positive perceptions of AI’s potential benefits (M = 4.12, SD = 0.81), with institutional support and digital literacy emerging as significant predictors of adoption (p < .05). However, qualitative insights revealed persistent barriers, including infrastructural deficits, ethical concerns, and policy gaps. The integration of datasets through joint displays highlighted both opportunities for personalised and equitable learning, as well as risks of deepening existing inequalities if adoption is not systematically managed. The study contributes to the growing discourse on AI in developing contexts by providing context-sensitive evidence from Zambia. It offers actionable recommendations for policymakers, universities, and stakeholders to foster the integration of inclusive and ethical AI.
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