AI-enhanced problem-based learning framework: Integrating ChatGPT as adaptive scaffolding to advance critical thinking and personalized learning - La Sunra La Sunra; Suci Amaliah; Fitri Radhiyani (Universitas Islam Negeri (UIN) Syekh Wasil, Kediri, Indonesia, 2026)
As educational institutions increasingly adopt artificial intelligence technologies, understanding how generative AI can be integrated into student-centered pedagogical frameworks has become an important area of contemporary educational research. This study examines an AI-Enhanced Problem-Based Learning (AI-PBL) Framework that integrates ChatGPT as adaptive scaffolding to improve critical thinking and support personalized learning. Using an explanatory sequential mixed-methods design, the study involved 120 eighth-grade students from four junior high schools in Ujung Pandang District, Makassar City, Indonesia. Quantitative data were collected through pre- and post-tests of critical thinking, while qualitative data came from classroom observations, ChatGPT interaction logs, and interviews. The overall critical thinking score increased from 60.3 (SD = 9.7) to 73.5 (SD = 10.4), t(119) = 9.64, p < .001, d = 1.00, with notable gains in analysis, inference, and bias detection. Qualitative analysis showed four patterns: ChatGPT supported idea exploration, strengthened verification habits, enhanced reflection and metacognitive monitoring, and did not lead to passive dependence. These findings indicate that generative AI can function as a learning partner when it is embedded in structured PBL and guided by teachers. The study offers practical evidence for integrating ChatGPT in secondary classrooms without reducing student agency.