Digital Native Prospective Teachers' Perspectives on AI-Assisted Lesson Planning and Classroom Management: An Interpretivist Inquiry in Punjab's Teacher Training Institutions
Year: 2025
Type: article
Abstract: This interpretivist study examines the perspectives of digital native prospective teachers towards AI-assisted lesson planning and classroom management in teacher training colleges in Punjab. Basing on the grounded theory model by O'Donoghue, this paper investigates how the prospective teachers understand, experience as well as integrate the topic of artificial intelligence into their teacher training process. The study fills a gap arising from the lack of research on how Digital Native teacher candidates learn to use AI technologies to design structures by focusing on instruction and classroom management within the Pakistani educational system. Twenty-four prospective teachers in four colleges of teacher education in Punjab were interviewed through semi-structured interviews based on the theoretical sampling and constant comparative analysis following the interpretivist paradigm provided by O'Donoghue. The study included data collection on the experience of the participants referring to their work with AI tools, potential benefits and challenges, and the development of their perspectives on the technology-enhanced pedagogy. Early results indicate multidimensional views of AI integration with some fears of pedagogical authenticity as well as the benefits of AI in improving instructional effectiveness. They showed a range of AI literacy, and desire training about the ethics of AI use. The research could help reveal the conceptualization of the role of AI in the educational process as the digital native teachers and serve as a reference on the development of the teacher education curriculum in the changing technological environment in Pakistan.
Authors: Muhammad Jamil, Bushra Rasheed, Noor Muhammad
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4415950864
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