Students' perception of the impact of artificial intelligence (AI)-personalised learning modules on knowledge, technical skills and clinical readiness among diploma radiography students at Kenya medical training college

Ronald Wasike; David Nandebe Wafula

doi:10.51867/ajernet.7.4.98

Authors

Ronald Wasike Kenya Medical Training College, Kenya https://orcid.org/0000-0003-3236-7993
David Nandebe Wafula Kenya Medical Training College, Kenya https://orcid.org/0009-0000-9047-0089

DOI:

https://doi.org/10.51867/ajernet.7.4.98

Keywords:

Artificial Intelligence, Clinical readiness, Personalised Learning, Radiography Education, Technical Skills

Abstract

As artificial intelligence (AI)-powered imaging systems become more commonplace in radiography, it is crucial for graduates to be able to use them safely, which requires knowledge, technical skills and clinical readiness that must be developed during training. This study aimed to evaluate the perceptions of diploma radiography students on the effect of the use of AI-personalised learning modules (digital learning modules that adapt content, pacing and assessment to each student) on knowledge acquisition, technical skills and clinical readiness in Kenya Medical Training College (KMTC) and to describe the relationship between the perceptions. Self-determination theory anchored this research. This was a cross-sectional descriptive survey that was conducted in May-June 2025 in five campuses (Eldoret, Kisumu, Mombasa, Nairobi and Nakuru) with 30 students per campus (N = 150; 149 valid responses, 99.3%). A structured questionnaire was used to gather information. Frequencies, percentages, means, Wilson 95% confidence intervals and Spearman's rank correlation were used for the analysis of six five-point Likert items. The highest levels of agreement were for deepened understanding of theory (71.1%) and improved technical skills (63.8%). It was lower for confident clinical application (61.7%), preparation for real world clinical tasks (61.1%), confidence in knowledge (58.4%) and accurate imaging technique (57.0%). Items had positive correlations (ρ = .434 to .804, p < .001), and the knowledge–skills–readiness domain correlated with the adaptive-delivery (ρ = .454) and AI-tailored modules (ρ = .621) domains. Students felt that they had improved more in theory than in skills and confidence in the clinical setting. The cross-sectional and self-reported nature of the design precludes the determination of effectiveness and only provides information on perceptions. It is recommended to have supervised hands-on practice, simulation and a controlled follow-up study with objective outcomes.

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