IMPROVING TEACHING SYSTEMS USING ARTIFICIAL INTELLIGENCE: NEW METHODS, OPPORTUNITIES, CHALLENGES AND FUTURE DIRECTIONS

Abstract

 In an era of rapid technological advancement, artificial intelligence (AI) is increasingly being harnessed to enhance teaching systems, offering innovative methods for personalized learning, adaptive instruction, educator support, and systemic improvements in educational outcomes. This article provides a comprehensive review of current applications of AI in education across K-12, higher education, online learning platforms, and professional development domains. It analyzes key benefits such as increased engagement and efficiency, alongside risks including algorithmic bias, data privacy violations, ethical dilemmas, regulatory shortcomings, and barriers to equitable integration in diverse educational settings. We highlight major advances such as adaptive learning platforms, intelligent tutoring systems (ITS), AI-assisted assessment tools, predictive analytics for student retention, and virtual reality-enhanced simulations. Simultaneously, we examine multifaceted challenges and propose a novel conceptual framework for deploying AI-driven methods in teaching systems. The framework introduces "adaptive hybrid loops" as a new method to foster seamless human-AI collaboration. The paper concludes with detailed guidelines, policy recommendations, and future research directions to ensure AI-enhanced teaching systems are effective, equitable, ethically sound, resilient, and scalable across global contexts.