Journal of Advances in Mathematics and Computer Science

Background: The increasing integration of artificial intelligence (AI) learning tools in higher education has created a need to understand how their adoption spreads and is sustained among university students.

Aims: This study examines how AI learning tools spread among university students and identifies the conditions that support or limit their continued adoption.

Methodology/Design/Approach: An SEIR-based mathematical model was developed to represent students' progression from being unaware of AI tools to becoming aware of them, using them, and becoming fully integrated users. The model was analysed using equilibrium and stability analysis, sensitivity analysis, and numerical simulations.

Findings: The results show that AI adoption can become widespread when R₀ > 1. For example, when R₀ = 3.463, the number of students using and becoming integrated with AI tools increases considerably over time. However, when R₀ = 0.16 < 1, adoption gradually declines, showing that AI use may not be sustained without sufficient awareness and continued support. The sensitivity results also indicate that the rate of active AI use plays an important role in encouraging greater student integration.

Limitation: The model assumes that students interact in relatively similar ways and that the model parameters remain constant. It therefore does not fully account for differences among students, institutional settings, or changes in the learning environment.

Originality: The study provides a different perspective on AI adoption by using a mathematical model to examine how AI use can spread across a university student population rather than focusing only on individual students' willingness or intention to use AI. The findings can help universities understand the conditions needed to encourage sustained and responsible adoption of AI learning tools.