Preparing Preservice Teachers for AI-Supported Classrooms: Perceptions and Competencies, and Psychometric Characteristics of the Survey Instrument

Abstract

Preparing future educators for technology-enhanced learning environments has become increasingly important as artificial intelligence (AI) continues to influence teaching and learning. Guided by the Intelligent Technological Pedagogical Content Knowledge (i-TPACK) framework, this mixed-methods study examined preservice teachers’ perceptions of AI and the competencies they considered necessary for effective AI integration. Participants included 108 preservice teachers enrolled in a teacher preparation program at a public university in the southeastern United States. Data were collected using a survey containing Likert-scale and open-ended questions. Quantitative data were analyzed using descriptive statistics, exploratory factor analysis, and independent-samples t-tests, while qualitative responses were analyzed using thematic coding. The exploratory factor analysis identified a four-factor empirical structure that partially corresponded with the original theoretical domains, with varying levels of internal consistency. Preservice teachers generally viewed AI favorably and recognized its potential to support teaching and learning. Participants with internship experience reported significantly higher scores for perceived changes brought by AI and reasons for using AI than those without internship experience. Qualitative findings identified five competencies considered important for responsible AI integration: AI literacy, prompt engineering, critical evaluation, ethical AI use, and pedagogical balance. Participants also expressed concerns about academic dishonesty, misinformation, overreliance on AI, reduced critical thinking, and loss of human interaction. The findings highlight the importance of preparing preservice teachers to integrate AI in pedagogically meaningful and ethically responsible ways.