Applied AI in Classroom Practice, exploring practical classroom implementations such as smart content delivery, AI-powered virtual assistants, and automated learning support tools. Intelligent Tutoring Systems, focusing on adaptive AI-driven systems that personalize instruction based on individual learner characteristics and performance. AI-Based Assessment and Feedback, examining automated grading, formative assessment mechanisms, and intelligent feedback systems. Learning Analytics and Educational Data Mining, investigating AI-driven analysis of student behaviors, prediction of learning outcomes, and optimization of pedagogical strategies. Adaptive and Personalized Learning Environments, designing systems that dynamically adjust learning pathways based on real-time interaction and learner progress. Natural Language Processing in Education, including automated writing evaluation, language learning applications, and conversational agents for instructional support. AI for Inclusive and Accessible Education, leveraging AI technologies to assist diverse learners, including individuals with disabilities and those in underserved communities. Ethics and Governance of AI in Education, addressing fairness, transparency, accountability, data security, and responsible AI deployment within educational settings.