Beyond AI: Future Education

The rapid advancement of AI is driving profound transformation in higher education. Yet the emergence of new technologies does not automatically translate into meaningful educational progress or substantive change in learning. We must therefore look beyond what AI can do and engage with a more fundamental set of questions: Beyond AI tools themselves, where should education be headed? What will future classrooms look like? What kinds of teachers will they require, and what forms of learning will define them? How can we move beyond the superficial application of AI tools and ensure that AI is seamlessly integrated into instructional design, supports deep learning, and advances the holistic evolution of the educational ecosystem? These are among the defining questions confronting universities worldwide today.

This year's Competition is themed "Beyond AI: Future Education". "Beyond AI" signifies moving beyond the superficial AI tool usage and passive technological adaptation, and instead exploring how AI can genuinely support meaningful learning, innovative pedagogical design, and the sustained professional development of educators. "Future Education" conveys an ambition that extends beyond technological iteration and upgrading, calling for a re-examination of the value orientation, educational logic, and developmental pathways of higher education in the age of AI. Under the joint initiative of Peking University, Sun Yat-sen University, Fudan University, and Xi'an Jiaotong University, the 3rd Global Digital Intelligence Education Innovation Competition has established the "AI for Teaching and Learning" track. The Competition invites universities and research institutes worldwide to submit AI-enabled educational innovations that have been implemented in authentic educational contexts and whose outcomes can be demonstrably verified.

This track is open to universities and research institutes around the world, with entities, faculty members, and student teams all eligible to participate. Award-winning submissions will be featured at the Beijing Forum (2026) Symposium on Digital and Intelligent Education and disseminated globally through channels such as the DI-IDEA Hub's online platform and curated case publications. Cases that meet the quality standards will, following peer review, be published under an open access CC-BY 4.0 license.

This track consists of four competition groups. Participants may choose any one of the groups. All submissions must be based on projects implemented in real-world educational settings. Their progress and evaluation outcomes must be clearly reported. Purely conceptual proposals or demonstrations will not be accepted.

Competition Group I: AI Agent for Education

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 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.

Competition Group II: AI-Enhanced Teaching Innovation

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.

Competition Group III: AI for Faculty Development

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 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.

Competition Group IV: AI-Enhanced Learning Innovation

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.

Group I is intended for teams whose primary achievements lie in AI systems or tools; Group II for teaching teams whose core achievements lie in course and instructional design; Group III for institutions or teams whose core achievements are in faculty development frameworks or training programs; and Group IV for cases that foreground transformative learning. Submissions are open to both individual students and teams. If a case spans multiple groups, applicants should select the one that best reflects their primary contribution. Each case may be submitted to only one group.

DI-IDEA member institutions are invited to participate. Universities and research institutes worldwide are also welcome. Applications may be submitted by entities, faculty members, or students. Each team may include up to five members.

• Competition Group I (AI Agent for Education): Open to technical teams, teaching teams, joint teams, and individuals.
• Competition Group II (AI-Enhanced Teaching Innovation): Applicants must have teaching responsibilities; open to individuals and teaching teams.
• Competition Group III (AI for Faculty Development): Open to entities such as faculty development centers, faculty development project teams, and individuals.
• Competition Group IV (AI-Enhanced Learning Innovation): Open to undergraduate, master's, and doctoral students; both individual student and joint faculty-student applications are encouraged.

Required Materials:

1. Application Form (completed using the official template), including:

• Background and problem statements: the educational issue or challenge being addressed, and the necessity and feasibility of applying AI.
• Implementation plans: specific measures, technical solutions or teaching/learning design, procedures, resources used, and timeline.
• Innovative highlights: distinctive and original features compared with similar practices.
• Outcomes and impact: data of implementation results, user feedback, or third-party evaluations.
• Reflections and outlook: lessons learned during implementation, unresolved issues, and follow-up plans.
• Scalability: an assessment of the submission's potential for adoption by other institutions, along with proposed pathways for dissemination.

2. Presentation Video

Each video should be 3-5 minutes in length and present the core innovation and implementation outcomes of the submission. Bilingual Chinese and English subtitles are preferred. Format: MP4, minimum resolution of 1080p.

Optional Materials:

Images, posters, instructional design documents, academic papers, system demonstration screenshots or screen recordings, user feedback, website links, and other supporting materials may be submitted.

Formatting Requirements and Copyright Notice:

All submitted materials must be authentic, accurate, and complete, and free of any intellectual property disputes. Bilingual submissions in Chinese and English are encouraged. The application form must be completed through the Competition's application system.

Intellectual property rights for the application form, videos, and all supporting materials remain with the submitting party. Submission of a case does not constitute a transfer of copyright. DI-IDEA reserves the right to display submitted works, compile them into collections, and organize academic publications based on them. Cases submitted to the DI-IDEA Hub for publication will be made available under an open-access CC-BY 4.0 license.

This track comprises three stages: submission, review and public announcement, and awards presentation.

• Submission: April 28-August 31, 2026
• Review and public announcement: September 1-October 31, 2026
• Awards Ceremony: November 2026 (specific dates to be announced separately)

*All times mentioned in the above schedule are in Beijing Time (GMT+8). The Organizing Committee reserves the right to adjust the schedule if necessary.

• Gold Awards: Granted across all groups, with no more than four recipients in total. Each winner will receive a trophy, a certificate, and a prize of RMB 30,000.
• Silver Awards: No more than two recipients per group. Each winner will receive a trophy, a certificate, and a prize of RMB 10,000.
• Bronze Awards: No more than eight recipients per group. Each winner will receive a certificate and a prize of RMB 5,000.
• Merit Awards: No more than ten recipients per group. Each winner will receive a certificate.

*Each recipient will be granted only the highest-level award attained, along with the corresponding prize. All prize amounts are stated in RMB before tax.

• Innovation: Whether the technological application or teaching/learning model demonstrates originality and novelty, and whether it offers new ideas or approaches worthy of broader adoption.
• Effectiveness: Whether there is evidence of measurable improvement or empirical support, and whether the submission's value has been validated in authentic teaching/learning contexts.
• Scalability: Whether the practice can be replicated and adopted across different institutions, disciplines, and cultural contexts.
• Sustainability: Whether mechanisms have been established for long-term operation and continual iteration.
• Compliance: Whether due attention has been paid to data security, ethical use of AI, and educational equity.

Email address: aiforlearning@163.com

Contact: Jiang Ying, 18601143986