Experiences of Science Teachers Regarding the Effects of Generative Artificial Intelligence Use on Education and Teaching Processes

Abstract

Purpose: This study examines science teachers’ experiences with the use of generative artificial intelligence in their teaching and learning processes, as well as the meanings they ascribe to these experiences. Method: The research used a qualitative, descriptive phenomenological approach. The study group consisted of six science teachers who worked in public schools affiliated with the Ministry of National Education and who had received training in generative artificial intelligence. Participants were selected using snowball sampling. Data were collected through online interviews using a 15-item semi-structured form developed by the researchers. Findings: Research findings indicate that science teachers primarily use artificial intelligence as a tool to support instruction. Evidence indicates that AI-supported applications increase students’ interest and motivation in the subject, facilitate the concretization of abstract concepts, and enhance learning retention. However, teachers have noted that factors such as insufficient class time, curriculum designs that are not conducive to the integration of generative AI, lack of technological infrastructure, and an exam-focused education system limit the use of these applications in the classroom. Additionally, it has been emphasized that current assessment and evaluation systems inadequately reflect the quality of learning outcomes supported by generative AI. Implications: Research findings indicate that generative artificial intelligence offers significant pedagogical opportunities in science education. However, to effectively harness this potential, it is necessary to develop teachers’ skills in using generative artificial intelligence for pedagogical purposes, increase practice-based professional development initiatives, and support classroom implementations. Additionally, it is important to develop flexible, process-oriented assessment approaches suitable for generative AI-supported learning environments.