This group solicits for agent systems that have already been developed or deployed, with a focus on the integrity of technical solutions and the demonstrability of educational outcomes. Eligible systems must have been in stable operation for at least six months and be supported by the technical solutions, user engagement data (e.g. the number of active users, usage frequency, and number of courses or application scenarios covered), and user feedback reports. Examples include, but are not limited to: intelligent teaching assistants, instructional support tools, intelligent learning companions, learning analytics and early-warning systems, intelligent assessment systems, subject-specific vertical LLM applications, and AI agents for research.

This group focuses on cases in which university instructors have deeply integrated AI into course instruction, with emphasis on the originality of instructional design and the substantive enhancement of learning outcomes. Each case must have completed at least one full implementation cycle (e.g. a semester or an entire teaching cycle), and include instructional plans, learning outcome data, and reflective analyses of the teaching process. Examples include, but are not limited to: holistic course design with deep AI integration, AI-empowered innovative laboratory teaching design, AI-based personalized and differentiated instruction, AI-enhanced flipped classrooms and blended teaching, AI-driven innovations in assessment, AI-assisted project-based and interdisciplinary teaching, and teaching practices that cultivate AI literacy and critical thinking.

This group solicits from universities and other educational institutions for innovative practices aimed at enhancing faculty AI literacy and professional development, emphasizing how AI strengthens teaching competencies and supports systemic innovation. Each case must have completed at least one full implementation cycle, and include the training plans, participant feedback, and effectiveness evaluations. Examples include, but are not limited to: faculty training in AI literacy and human-AI collaboration, innovation in faculty training and professional development support systems, construction of data platforms supporting faculty growth, and practices related to AI ethics and the evolving roles of teachers.

This group focuses on student-centric innovations, with a focus on how AI is used to transform individual or collaborative learning approaches, enhance disciplinary learning capabilities, and address learning challenges. Each case must have completed at least one full implementation cycle (e.g. a semester or an entire learning cycle), and include records of the learning process, evidence of learning outcomes, and personal reflective accounts. Examples include, but are not limited to: AI-enabled self-directed and personalized learning, AI-supported collaborative learning and teamwork, AI-assisted academic research and knowledge production, the development of learning strategies and metacognition in AI-rich environments, and the cultivation of critical and creative thinking in the era of digital intelligence.