Uptake and Application of Artificial Intelligence by Lecturers and Students of Kenya Medical Training College

Abstract

Background: The rapid growth of Artificial Intelligence (AI) is revolutionizing healthcare delivery and health professional education by supporting personalized, technology-driven learning approaches. However, little is known on the uptake and application of AI in middle-level health professional training institutions in Kenya. Kenya Medical Training College (KMTC) is the largest public middle-level health professional training institution which plays a critical role in producing human resource for health. Understanding the extent to which AI is utilized within KMTC is therefore essential for informing institutional policies and strategies for embedding AI technology into health professional education.
Methods: A cross-sectional study was conducted between November 2024 and November 2025 across eight randomly selected KMTC campuses. Multistage sampling technique was employed. Data was collected using structured electronic questionnaires. A total of 581 respondents participated, representing a 97% response rate. Data were analyzed using SPSS to generate descriptive statistics and associations.
Results: Overall, 76.3% of students reported using AI during their studies, while 56.3% of lecturers had used AI to support teaching and learning activities. ChatGPT was the most commonly used AI tool (66.3%). Among students, AI use was significantly associated with course of study (χ² = 66.79, p < 0.001), age group (χ² = 12.83, p < 0.046), year of study (χ² = 10.96, p < 0.004), and level of training (χ² = 6.58, p < 0.037). Among lecturers, AI use was significantly associated with level of education (χ² = 13.23, p < 0.039). Major challenges included limited internet access (students 52.5%), limited technical capacity (lecturers 31.3%), and inadequate knowledge of AI tools. Notably, 66.1% of students believed AI will become increasingly important in future medical practice.
Discussion: AI uptake within KMTC setting is growing however variations exists reflecting differences in digital literacy, internet access, and exposure to digital tools across programs and campuses. Limited infrastructure and low technical capacity appears to impede integration of AI into teaching and learning. Strengthening digital infrastructure, building faculty and student capacity in responsible AI use, and developing institutional policies and guidelines could facilitate more effective adoption.