AI-Resistant Education: A Comprehensive Review of Strategies and Policies in the Era of Generative AI

The rapid advancement of Large Language Models (LLMs) and generative artificial intelligence (AI) technologies has fundamentally challenged traditional educational paradigms, creating unprecedented concerns about academic integrity and authentic learning assessment. This review paper examines seven key strategies for developing AI-resistant education systems: complete AI prohibition policies, AI detection technologies, AI-resistant coursework design, staged implementation policies, curriculum transformation approaches, hybrid assessment models, and AI watermarking systems. Through an analysis of recent literature and institutional practices, we evaluate the effectiveness, feasibility, and implications of each approach. Our findings suggest that no single strategy provides a complete solution, and successful AI-resistant education requires a multifaceted approach combining policy frameworks, technological tools, pedagogical innovation, and cultural shifts toward authentic assessment practices.