Beyond Traditional Teaching: Designing Analytical Models for Pedagogy and Classroom Management in AI-Era Universities

Higher education is experiencing a profound transformation shaped by digital-native cohorts and the integration of artificial intelligence (AI) into classrooms. Traditional pedagogical methods and classroom management routines are increasingly misaligned with the expectations and complexities of this “AI Generation.” This article develops an analytical model that integrates three dimensions: pedagogical strategies (lecture, active learning, flipped/blended, AI-personalized), classroom management approaches (control-oriented, engagement-oriented, inclusive-oriented), and AI-era mediators (learning analytics, generative AI, and ethical governance). Grounded in theories of constructive alignment (Biggs & Tang, 2011), Universal Design for Learning (CAST, 2024), and formative feedback for self-regulation (Nicol & Macfarlane-Dick, 2006), the model provides a framework to design learning environments that are adaptive, inclusive, and ethically governed. The article further derives design principles and governance protocols, evaluates risks and opportunities, and identifies future research directions. By reframing pedagogy and classroom management as a single design problem, the paper argues for universities to adopt coherent, evidence-based, and equity-driven strategies to ensure that AI integration enhances rather than undermines student learning.