Quantifying teachers' readiness for artificial intelligence adoption in education: a mathematical modeling...

Area of Science:

• Educational Technology
• Mathematical Modeling
• Artificial Intelligence in Education

Background:

• Teachers' readiness to adopt Artificial Intelligence (AI) is crucial for educational advancement.
• Understanding the dynamics of AI adoption in educational settings, particularly in Nigeria, is essential.

Purpose of the Study:

• To develop and analyze a mathematical model predicting teachers' AI adoption readiness.
• To identify key factors influencing AI integration in Nigerian education.

Main Methods:

• Adapted the Susceptible-Exposed-Infective-Recovered (SEIR) framework for AI adoption dynamics.
• Analyzed model properties (positivity, boundedness, stability) and performed sensitivity analysis.
• Conducted numerical simulations to assess parameter effects on teacher adoption over time.

Main Results:

• Higher teacher attrition initially reduces unawareness but can lead to resurgence.
• Rapid transition from awareness to adoption accelerates AI integration.
• Sustained resource availability is critical for long-term AI adoption.

Conclusions:

• Targeted interventions and resource allocation are necessary for successful AI adoption by teachers.
• Strategic planning and support mechanisms are vital for fostering AI-enhanced pedagogy.
• Findings offer insights for policymakers and institutions promoting educational technology.