JMIR Publications

ABSTRACT

Background:

Background Artificial Intelligence (AI) has quickly become integrated into higher education, transforming how students access information, complete academic tasks, and engage with learning. Universities of Technology (UoTs), with their focus on applied learning, technological skills, and graduate employability, face both challenges and opportunities in incorporating AI in ways that improve learning without sacrificing conceptual understanding, critical thinking, or academic integrity. While current research highlights widespread student use of AI tools, there remains limited empirical evidence on how undergraduate students in applied and health-related fields interact with AI, perceive its educational impact, and manage risks associated with overreliance.

Objective:

The study aims to examine undergraduate students’ AI usage patterns, their perceptions of how AI affects conceptual understanding and critical thinking, and their views on risks like dependency and ethical issues within a University of Technology setting. An additional goal is to investigate students’ experiences with AI literacy programs and to gather evidence to support responsible, educationally grounded AI integration that aligns with institutional strategies and graduate attributes.

Methods:

The study adopts a convergent mixed-methods, cross-sectional, exploratory–descriptive design. Quantitative data will be collected using a structured questionnaire administered to undergraduate students across the Mathematics, Somatology, and Bachelor of Health Sciences (Biomedical and Clinical Technology) programs within the Faculties of Applied Sciences and Health Sciences at Durban University of Technology (DUT). Qualitative data will be generated through semi-structured interviews. Quantitative data will be analysed using descriptive and inferential statistical methods, while qualitative data will be analysed thematically using Tesch’s method. Integration of findings occurs at the interpretation stage to enable triangulation and comprehensive insight.

Results:

The study started with a systematic review of the literature, which will be reported as an initial outcome. A pilot study conducted within the mathematics department has identified emerging patterns of AI dependence among students, thereby informing the refinement of research tools and the focus of interventions. The ongoing study is currently in the data collection stage.

Conclusions:

The study is expected to provide evidence on the use of AI in applied and health-related fields. The findings will inform institutional policy, curriculum development, and the development of a short course on the responsible use of AI, thereby supporting broader discussions on the sustainable integration of AI in higher education.