Quantifying teachers’ readiness for artificial intelligence adoption in education: a mathematical modeling perspective

We developed a mathematical model based on the classical SEIR (Susceptible–Exposed–Infective–Recovered) framework to predict teachers’ readiness to adopt Artificial Intelligence (AI) in educational settings, with a specific focus on Nigeria. In this context, we reinterpret the compartments as follows: Unaware population (S) represents teachers who are not yet aware of AI’s potential in education; Aware (E) includes teachers who are informed but undecided about AI adoption; Adopters (I) are those who have begun integrating AI into their teaching practices; and Discontinued Users (R) are teachers who previously used AI but have ceased due to resource constraints or lack of institutional support. We meticulously analyzed the model’s properties, including positivity, boundedness, and stability, to ensure the accuracy and applicability of the results. Additionally, a comprehensive sensitivity analysis was performed to identify key parameters influencing the dynamics of AI adoption. Numerical simulations were utilized to demonstrate the effects of these parameters on the teacher population over time. Our results reveal that a higher teacher attrition rate decreases the unaware population initially but leads to a resurgence after a critical threshold is crossed. Furthermore, the rapid transition from awareness to adoption was instrumental in accelerating AI integration, whereas sustained resource availability emerged as a crucial determinant for maintaining long-term adoption. This study provides valuable insights into the nuanced dynamics of AI adoption among educators, highlighting the necessity for targeted interventions and effective resource allocation to facilitate successful AI integration in teaching. The findings have significant implications for policymakers and educational institutions aiming to promote the adoption of AI-enhanced pedagogical practices, underscoring the importance of strategic planning and support mechanisms to foster a conducive environment for technology-driven educational advancements.