AI-Supported Collaborative and Reflective Biology Learning: A Needs Assessment in Teacher Education - Sri Wahyuni; Muhiddin Palennari; Andi Asmawati Azis; Adnan; Ismail Ismail (International Journal of Educational Practice and Policy, 2026)
Artificial intelligence creates new opportunities to transform biology teacher education; however, its pedagogical integration must be grounded in students’ actual learning needs. This study examined biology education students’ needs for AI-supported collaborative and reflective biology learning as a preliminary foundation for developing a pedagogical model. A quantitative descriptive survey was conducted with 290 biology education students from higher education institutions in South Sulawesi, Indonesia. Data were collected using a 29-item questionnaire covering five constructs: social-emotional learning, metacognitive awareness, self-regulated learning, reflective thinking, and collaborative-interactive biology learning context. The data were analyzed using descriptive statistics, reliability analysis, convergent validity indices, and inter-construct correlations. The findings showed that all constructs were categorized as high, with mean scores ranging from 3.628 to 3.738 and percentage scores ranging from 72.570% to 74.760%. Reflective thinking obtained the highest mean score (M = 3.738; 74.760%), followed by metacognitive awareness (M = 3.712; 74.240%), indicating the importance of reflective and metacognitive scaffolding in AI-supported biology learning. The questionnaire demonstrated acceptable measurement quality, with standardized loadings of 0.621–0.907, Cronbach’s Alpha values of 0.800–0.938, Composite Reliability values of 0.871–0.951, and AVE values of 0.632–0.765. Strong positive inter-construct correlations (0.804–0.903) suggest that students’ learning needs are closely interconnected. This study positions AI as a pedagogical scaffold for reflective, collaborative, metacognitive, and self-regulated learning rather than merely as a technology-driven intervention.