Exploring the Impact of Generative AI on Curriculum Design and Instruction: A Study with Preservice Teachers
Main Article Content
Abstract
Generative Artificial Intelligence (GenAI) is transforming education by reshaping curriculum design, individualized learning, and professional collaboration. This interpretive study examines how seventy preservice teachers engaged with GenAI through structured learning experiences focused on lesson planning, content development, and reflective practice. Participants, all fourth-year PK–Grade 5 and intervention specialist candidates at the University of Dayton, completed a co-taught seminar led by methods professors in science, social studies, language arts, and mathematics. Data sources included initial reflections on GenAI’s role in education, documentation of prompt exploration, and ongoing reflections during the seminar. Thematic analysis revealed a shift in preservice teachers’ understanding from viewing GenAI as a simple lesson-planning tool to recognizing its potential for differentiation, assessment, and parent communication. Participants demonstrated increasing sophistication, ethical awareness, and confidence in using GenAI to enhance instruction. Findings indicate that intentional professional development enhances preservice teachers’ GenAI literacy and confidence, supporting thoughtful integration of emerging technologies into teaching practice. The study advocates for embedding GenAI training within teacher education programs to cultivate innovation, ethical practice, and inclusivity in future classrooms.
Downloads
Article Details
- Authors retain copyright and grant the Journal of Teaching and Learning with Technology (JoTLT) right of first publication with the work simultaneously licensed under a Creative Commons Attribution License, (CC-BY) 4.0 International, allowing others to share the work with proper acknowledgement and citation of the work's authorship and initial publication in JoTLT.
- Authors are able to enter separate, additional contractual agreements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in JoTLT.
- In pursuit of manuscripts of the highest quality, multiple opportunities for mentoring, and greater reach and citation of JoTLT publications, JoTLT encourages authors to share their drafts to seek feedback from relevant communities unless the manuscript is already under review or in the publication queue after being accepted. In other words, to be eligible for publication in JoTLT, manuscripts should not be shared publicly (e.g., online), while under review (after being initially submitted, or after being revised and resubmitted for reconsideration), or upon notice of acceptance and before publication. Once published, authors are strongly encouraged to share the published version widely, with an acknowledgement of its initial publication in JoTLT.