Teachers' perceptions on AI readiness and AI integration in mathematics education: A multigroup latent profile analysis

As the importance of artificial intelligence (AI) is growing in diverse areas, the need for incorporating AI into education through K-12 grade levels is increasing. The successful implementation of AI in education requires the proper preparation of teachers who practically drive AI systems in the instructional process. In this study, we have conducted a questionnaire survey and utilized quantitative methodology using multigroup latent profile analysis (LPA) to scrutinize teachers' perceptions of using AI for teaching, learning, and assessing mathematics. A total of 318 Korean teachers from two sample groups, 161 elementary school teachers and 157 secondary mathematics teachers, participated in the study. The results of multigroup LPA identified three distinct latent profiles for elementary and secondary mathematics teachers respectively, and revealed differences in mean scores, variance, and profile size between the two sample groups. Additionally, multinomial logistic regression demonstrated that teachers' AI readiness was associated with an increased likelihood of being categorized into profiles that are more receptive to AI integration. Based on the results, we discussed the pedagogical implications for teachers to effectively use AI in mathematics education.