Students' perceptions of curriculum alignment, assessment and personalised learning in AI-supported diploma radiography education

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DOI:

https://doi.org/10.51867/ajernet.7.4.96

Keywords:

Artificial Intelligence, Curriculum Alignment, Formative Assessment, Personalised Learning, Radiography Education

Abstract

While artificial intelligence (AI) has the potential to facilitate personalised learning in radiography education, positive perceptions do not necessarily mean that a curriculum is complete, assessments are valid or clinical competence is achieved. The study evaluated students' perception of the coverage of the curriculum, conceptual understanding, accuracy of assessment, alignment with learning needs, targeted support and perceived improvement in knowledge and skills in AI-supported Diploma in Radiography and Imaging education using the framework of Constructive Alignment Theory and the competency-based medical education framework. This was a descriptive cross-sectional survey of 150 students sampled by stratified random sampling from five Kenya Medical Training College campuses with 149 records having valid item responses. Data were gathered through a computer-based, structured questionnaire of 18 five-point Likert items in three six-item domains, one of which is the six-item AI-tailored modules and assessment domain. Frequencies, percentages, means and sample standard deviations (disagree = 1, neutral = 2, agree = 3) and Spearman's correlations were calculated. Agreement was highest for perceived improvement in knowledge and skills (73.2%, M = 2.63, SD = 0.661), and lowest for curriculum coverage (51.7%, M = 2.28, SD = 0.823); agreement for assessment accuracy, alignment with learning needs and targeted support was 65.8%, 67.1% and 68.5%, respectively. Correlations varied from .133 to .552. The conclusion is that targeted support was more consistently valued by students than comprehensive curriculum coverage, but the results are perception based and do not demonstrate effectiveness or competence. Programmes are recommended to map modules and assessments to programme outcomes, to connect personalised support to educator assessed outcomes, to have supervised practice and reassessment, and to have prospective evaluations with objective measures.

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Published

2026-10-29

How to Cite

Wafula, D. N., Ichelai, A. A., Mukelule, J., & Njeru, K. K. (2026). Students’ perceptions of curriculum alignment, assessment and personalised learning in AI-supported diploma radiography education. African Journal of Empirical Research, 7(4), 1174-1182. https://doi.org/10.51867/ajernet.7.4.96