The effect of AI-Optimized project-based learning on digital pedagogical readiness - Hariyono Hariyono; Umi Hidayati; Suharto Suharto (Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas PGRI Mahadewa Indonesia, 2026)
The integration of artificial intelligence (AI) in higher education has increased the demand for students' digital pedagogical readiness, particularly in economics education. This study examines the effect of AI-Optimized Project-Based Learning (AI-PjBL) on Digital Pedagogical Readiness (DPR) among undergraduate students. A quantitative approach was employed, involving 102 undergraduate students from the Economics Education and Management programs at Universitas Nusantara PGRI Kediri, using a purposive sampling technique. Data were collected using validated instruments and analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS). The measurement model demonstrated strong reliability and validity, with factor loadings ranging from 0.77 to 0.90, Composite reliability values above 0.96, and Average Variance Extracted exceeding 0.67. Structural model analysis revealed that AI-PjBL had a strong and significant effect on DPR, indicated by a path coefficient of β = 0.79. The coefficient of determination (R² = 0.63) indicates that 63% of the variance in digital pedagogical readiness is explained by AI-PjBL, while the effect size (f² = 1.52) indicates a very large effect. These findings indicate that AI-PjBL effectively enhances students' technological readiness, pedagogical design ability, and reflective digital competence. The study highlights AI-PjBL as a promising instructional approach for strengthening digital pedagogy in economics education.