TRANSFORMING TRADITIONAL TEACHING MODELS WITH ARTIFICIAL INTELLIGENCE: INNOVATIONS IN EDUCATION - Pahmi Pahmi; Nour Ibrahim; Rukmana Fachrul Islam (Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia, 2026)
The rigidity of traditional standardized education often fails to address the diverse cognitive needs of modern learners, necessitating a paradigm shift toward more adaptive instructional models. This study investigates the transformative potential of integrating Artificial Intelligence (AI) into secondary curricula to facilitate the transition from mass instruction to personalized pedagogy. Utilizing a mixed-methods quasi-experimental design, we evaluated the academic and operational impact of an AI-driven adaptive learning framework on a cohort of 600 students and 30 educators over a twelve-week intervention. The research benchmarked an AI-augmented experimental group against a control group receiving traditional direct instruction using standardized assessments and telemetry data. Empirical results demonstrate that the AI-integrated model yielded a statistically significant 11.4% increase in concept mastery (p<0.001) and substantially compressed the achievement gap within the classroom. Furthermore, the automation of administrative tasks reclaimed five hours of weekly instructor time, facilitating a strategic redistribution of labor toward high-value mentorship. We conclude that AI acts as a critical force multiplier that does not replace the teacher but fundamentally restructures the instructional core, validating a “Symbiotic Intelligence” approach that couples machine efficiency with human empathy to optimize educational outcomes.