Professional knowledge complements general technology acceptance in mathematics teachers' critical behavioral...

Area of Science:

• Education
• Artificial Intelligence
• Mathematics Education

Background:

• Artificial Intelligence (AI) integration in mathematics education necessitates understanding teachers' critical behavioral intention (CBI) towards AI.
• Existing technology acceptance models lack subject-specific factors crucial for AI adoption in teaching.
• This study addresses the gap by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model.

Purpose of the Study:

• To investigate the factors influencing mathematics teachers' critical behavioral intention (CBI) to use AI in their teaching.
• To extend the UTAUT model by incorporating Mathematical Knowledge for Teaching (MKT) dimensions and perceived trust (PT).
• To analyze the roles of Specialized Content Knowledge (SCK), Knowledge of Content and Students (KCS), and PT in predicting CBI.

Main Methods:

• A survey was conducted with 169 mathematics teachers.
• Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test the proposed model.
• The study adapted the UTAUT model to include MKT dimensions (SCK, KCS) and PT.

Main Results:

• Performance expectancy (PE) and effort expectancy (EE) significantly predicted CBI in the baseline UTAUT model.
• In the enhanced model, only PE directly predicted CBI. Knowledge of Content and Students (KCS) positively influenced perceived trust (PT), which in turn predicted CBI.
• The full model explained 56.3% of the variance in teachers' critical behavioral intention (CBI) towards AI, with KCS and PT playing key roles.

Conclusions:

• Teacher professional development should focus on enhancing KCS and PT for effective AI integration in mathematics.
• AI system design should prioritize building teacher trust (PT) over just usability.
• Strengthening teachers' KCS is crucial for them to effectively evaluate and utilize AI tools in mathematics education.