Tracks Introduction
Track 1: FinTech
Design an ethical, AI-driven solution to a real problem in finance, focusing on transparency, fairness, and responsible decision-making rather than only optimization or profit.
Identify a concrete pain point affecting users such as individuals, institutions, or underserved populations, explain why current systems fail, and propose a feasible solution that improves outcomes like access, risk management, or financial literacy.
Strong projects critically address issues such as bias, opacity, data privacy, and systemic inequality, ensuring that AI is used in a trustworthy and accountable way. Submissions should clearly communicate the problem, solution, innovation, and ethical implications, and will be judged on real-world relevance, responsible AI use, feasibility, and clarity.
Ex. Identification of financial fraud gangs / abnormal transactions / money laundering
Track 2: HealthTech
Design an ethical, AI-enabled solution to a real problem in healthcare that improves patient outcomes, access, or system efficiency while prioritizing safety, privacy, and equity.
Identify a specific pain point affecting patients, providers, or public health systems, explain why existing approaches fall short, and propose a feasible solution that is clinically relevant and practically deployable. Strong projects address challenges such as bias in medical data, transparency of decision-making, patient consent, and unequal access to care, ensuring AI is used responsibly and does not introduce harm.
Submissions should clearly present the problem, solution, innovation, and ethical considerations, and will be judged on impact, feasibility, responsible AI use, and clarity.
Track 3: EdTech
Design an innovative, impactful, and ethical solution to a real educational problem with AI; focusing on strong problem–solution fit, meaningful differentiation, and responsible design.
Propose a feasible and actionable solution that improves learning outcomes, accessibility, or system efficiency while going beyond incremental improvements.
You are encouraged to explore domains such as higher education, assessment and evaluation, K–12 learning, language learning, or other areas, with particularly strong projects rethinking how learning is measured and experienced rather than just optimizing existing systems.
Your solution must also evaluate effectiveness, risks, and broader societal impact, addressing fairness, transparency, privacy, and educational integrity.
Track 4: Luxshare - AI for Smart Manufacturing
Luxshare invites participants to explore how AI can address real-world challenges in smart manufacturing. Teams are encouraged to develop innovative solutions in the following areas:
AD (Automation & Digitalization)
Use AI to improve production line automation, digital twin systems, and intelligent manufacturing processes.
TE (Test Engineering)
Apply AI to optimize product testing, detect anomalies, improve yield, and enhance quality assurance.
MLB (Mainboard Business Unit)
Leverage AI to support PCBA manufacturing, including SMT, assembly, testing, repair, and process optimization.
IT (Information Technology)
Develop AI solutions for factory information systems, data management, cybersecurity, and digital infrastructure.
Teams are encouraged to use technologies such as machine learning, computer vision, data analytics, digital twins, and large language models to improve efficiency, quality, and decision-making in manufacturing.
Tools and Technologies
Inspiration
Contact Us & Support Channels
zhonghan.dai@dukekunshan.edu.cn
Midweek Mentor Sessions
Dennis (Zhimin) Huang
- Industry Expert
- Shenzhen Overseas Chinese Friendship Association
- Entrepreneurship
Kevin (Kaifan) Xi
- Distinguished Entrepreneur
- Zhuhai Astrosnow Software Co., Ltd.
- R&D, Project management, AI
Luxshare
- Two mentor sessions for Track 4 (Luxshare) only