Analysis of Student Engagement With AI in Undergraduate Lectures: Protocol for an Implementation Study.

Abstract

Background: AI has quickly become integrated into higher education, transforming how students access information, complete academic tasks, and engage with learning. Universities of technology, 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 use patterns, their perceptions of how AI affects conceptual understanding and critical thinking, and their views on risks such as dependency and ethical issues within a university of technology setting. An additional goal is to investigate students' experiences with AI literacy programs and 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. Qualitative data will be generated through semistructured interviews. Quantitative data will be analyzed using descriptive and inferential statistical methods, whereas qualitative data will be analyzed thematically using the method by Tesch. Integration of findings will occur at the interpretation stage to enable triangulation and comprehensive insight. Integration will follow the joint display approach described by Guetterman et al. Quantitative and qualitative results for each objective will be displayed side by side in matrices that compare statistical patterns with thematic insights. Convergence, complementarity, or discordance will be explicitly noted. Meta-inferences will be generated through team deliberation using an integration codebook. A triangulation protocol will document how discrepancies are resolved (eg, prioritizing qualitative depth for mechanism explanation and quantitative breadth for prevalence estimation).

Results: A pilot study conducted within the mathematics department identified emerging patterns of AI dependence among students, helping refine research tools and focus interventions. This study was funded by the Department of Higher Education and Training on January 1, 2025, and was conducted between August 2025 and November 2025, with preliminary results available at the end of that period. Data collection is ongoing and is expected to conclude by the end of November 2026, after which the data will be analyzed in 2027 and the findings will be published in a separate results paper.

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.