AI-Supported Inquiry-Based Learning in Photosynthesis and Respiration: Implications for Sustainable Science Teacher Education

Artificial intelligence (AI) has the potential to support sustainable science teacher education by promoting inquiry, responsible technology use, and digital competence. This study examined the effects of an AI-supported guided inquiry intervention on pre-service science teachers’ artificial intelligence literacy (AIL), self-perceived computational thinking skills (SPCTS), and conceptual understanding of photosynthesis and cellular respiration. A quantitatively driven quasi-experimental pretest–posttest control group design with an embedded qualitative component was employed with 48 s-year pre-service science teachers in Türkiye. The experimental group participated in an eight-week AI-supported guided inquiry program integrating problem and design-based learning, whereas the control group received conventional instruction. Mixed-design ANOVA revealed significant group-by-time interactions for conceptual understanding of photosynthesis (F (1, 41) = 22.79, p < 0.001, ηp2 = 0.357) and cellular respiration (F (1, 44) = 25.70, p < 0.001, ηp2 = 0.369), whereas no significant effects were found for AIL or SPCTS (p > 0.05). Qualitative findings showed that participants reported perceived benefits and engagement in conceptual learning, inquiry, collaboration, critical evaluation of AI-generated outputs, and digital content creation. The findings suggest that AI-supported guided inquiry can strengthen disciplinary conceptual learning while fostering competencies associated with sustainable science teacher education, although longer interventions may be required to produce measurable gains in AIL and SPCTS.