Abstract

AI-enabled educational tools have great potential for enhancing learning and teaching, yet there is a notable lack of study on AI-focused professional development for K-12 teachers. This phenomenological study employed the TPACK framework to examine teachers' incorporation of AI and engagement in professional development activities. The use of a social constructivism framework explored teachers' views on knowledge, learning, and motivation. Data were collected from 12 K-12 teachers across various districts in a large metropolitan area who attended AI professional development sessions in their own context and from that experience created AI artifacts. Primary data included (a) semistructured interviews, (b) professional development materials, and (c) teacher-created AI artifacts. This study examined the origins of AI knowledge for educators, the influence the professional development sessions had on integrating AI in classrooms, the challenges and suggestions teachers have for further AI professional development, and the crucial components of AI-focused professional development based on the TPACK framework. Participating teachers reported that the professional development sessions they attended primarily focused on various AI tools, dedicating most of the time to demonstrating their functions and providing sample prompts for experimentation. While these sessions were well-structured and actively engaged the teachers, they needed a substantial focus on content knowledge. Given that AI technology is still new, ongoing support and resources are essential for teachers to engage students in discussions about the ethical implications of AI. To address the need for a structured approach to professional development programs, this study introduces the AIPACK framework, which categorizes all AI-related knowledge, This framework illustrates how AI tools can amplify pedagogical strategies through collaborative, interactive, integrative, and inquiry-based learning methods and provide tailored advice specific to content areas. This study further offers implications for school policies.

Library of Congress Subject Headings

Artificial intelligence—Study and teaching (Elementary); Artificial intelligence—Study and teaching (Secondary); Teachers—Training of; Educational technology

Date of Award

2024

School Affiliation

Graduate School of Education and Psychology

Department/Program

Education

Degree Type

Dissertation

Degree Name

Doctorate

Faculty Advisor

Reyna García Ramos

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