An AI instructional module significantly improved pre-service teachers' knowledge, attitudes, and comfort levels toward artificial intelligence while revealing their primary conceptualization of AI as computers mimicking human intelligence.
Objective: The main goal of this mixed-methods study was to investigate the impact of integrating artificial intelligence (AI) into pre-service teacher education by examining changes in pre-service teachers' (PSTs) knowledge and attitudes toward AI after participation in an instructional module, exploring PSTs' perceptions about using AI in their future classrooms, and identifying strategies they propose for incorporating AI into teaching.
Methods: The researchers employed an explanatory mixed-methods design involving 93 undergraduate teacher education students taking an introductory instructional technology course at a large university in Florida. Data collection utilized pre- and post-test surveys designed and delivered through Qualtrics software, containing 16 items including demographic information, background data, and items adapted from previous instruments to assess perceived knowledge and attitudes toward AI across four categories: Definition, Comfort, Interest, and Use in the classroom, measured on a 1-7 scale from "strongly disagree" to "strongly agree." The instructional intervention consisted of an AI lesson embedded within a module titled "Solving Problems & Designing Solutions through Coding, Makerspaces & Serious Games," which included resources from ISTE and assignments such as "AI for Oceans" and "Computational Thinking, Artificial Intelligence, and Robotics Integration Lesson." Quantitative data were analyzed using SPSS 27 software with paired t-tests to determine statistical significance, while qualitative analysis involved coding open-ended survey responses using both a priori and emergent codes to identify themes and patterns.
Key Findings: The study revealed statistically significant improvements in PSTs' self-reported AI knowledge and attitudes, with knowledge items increasing by 0.500 (AIK1) and 0.356 (AIK2) on the 7-point scale, and all five attitude items showing significant positive changes. Future classroom use items also demonstrated significant increases of 0.467 (AIFC1) and 0.589 (AIFC2), indicating enhanced comfort levels with AI integration. However, effect sizes were relatively small (Cohen's d values below 0.050) across all dimensions. Qualitative findings revealed that PSTs most commonly explained AI as computers possessing human intelligence, with responses emphasizing "mimicking," "developing," or "simulating" human thinking, followed by machine learning concepts focusing on computers' ability to "interpret data" and "learn" to independently "make decisions." Regarding implementation strategies, the most frequently mentioned approach was utilizing AI as a teacher assistant or helper for tasks such as grading papers, providing expertise, and assisting in lesson planning, with many emphasizing individualized tutoring potential, followed by robot design integration and the use of AI-powered online websites and applications.
Implications: The findings have significant implications for teacher education practices, demonstrating that targeted educational interventions can enhance PSTs' understanding and preparedness to integrate AI into the academic landscape. The study supports the integration of AI education into teacher preparation programs, as exposure to AI concepts and practical applications positively influences future teachers' readiness to embrace AI as an educational tool. The research contributes to the growing body of literature on AI in education by showing that PSTs view AI primarily as a supportive tool rather than a replacement for teachers, aligning with previous research suggesting that AI's goal is to help teachers perform their jobs more effectively rather than replace them. The study also highlights the importance of addressing PSTs' uncertainties about AI implementation, as many participants expressed uncertainty about effectively integrating AI into their teaching practices despite showing improved knowledge and attitudes.
Limitations: The study acknowledges several important limitations affecting the generalizability of findings. First, students were required to complete the instructional module as part of regular coursework, and while research consent was optional, this could introduce potential bias as students might have felt obligated to participate, potentially impacting their responses. The external validity is limited as the study concentrated solely on PSTs at one university, highlighting the need to recognize that findings may not be generalizable across populations or settings. Additionally, participants had different educational backgrounds and varying levels of exposure to AI concepts and online learning modalities, which could have influenced their ability to build on prior knowledge or navigate the course effectively. A significant methodological limitation involved one of the four researchers serving as an instructor in the examined courses, potentially influencing student responses and causing response bias.
Future Directions: The researchers emphasize the need for continued, in-depth AI education for future teachers, noting that while improvements were observed, they might not be substantial given the small effect sizes. Future research should focus on examining larger and more diverse sample sizes to improve generalizability across different populations and settings. The study calls for additional research to expand understanding in light of recent releases of open-access generative AI tools and ongoing discussions about the benefits and concerns regarding AI in education. The authors welcome future research to build upon their findings, particularly considering the rapidly evolving landscape of AI tools and their applications in educational contexts.
Title and Authors: "Pre-Service Teacher Education in the Age of AI: Exploring Knowledge, Attitudes, and Classroom Integration Strategies" by Dr. Jillian R. Powers (Florida Atlantic University), Dr. Ann Musgrove (Florida Atlantic University), Dr. Mohammad Azhar (BMCC, CUNY), and Walter Milner, M.A., M.Ed. (Nova Southeastern University).
Published On: Summer 2025
Published By: Journal of Literacy and Technology, Volume 26, Number 2