About HackDKU

HackDKU is the DKU Computer Science Club’s annual interdisciplinary hackathon. Commemorating everything we learned as a community throughout the academic year, HackDKU challenges everyone to apply creative problem solving, technical expertise, and entrepreneurship to bring forth innovation that matters globally. Our mission is to present Duke Kunshan University as a hub for innovation and entrepreneurship, blending technological innovation with art, sciences, critical thinking, and creativity.

Event Details

HackDKU is more than a competition; it’s a celebration of collaborative spirit and the joy of innovation. It’s a call to action for those who seek to make a lasting impact, nurturing teams and relationships dedicated to developing solutions that endure long after the hackathon ends. Join us in this journey of cross-boundary collaboration and be part of shaping solutions that matter.

Who can participate?

HackDKU is not just an annual hackathon. It is a melting pot of ideas, innovation, and interaction that extends beyond a single campus. Set against the vibrant backdrop of Duke Kunshan University while actively engaging students and collaborators from universities across China, the event becomes a dynamic convergence of minds from diverse disciplines and backgrounds. By bridging institutional boundaries, it reinforces its core purpose of fostering open exchange, technical creativity, and cross community collaboration.

Format

The success of HackDKU 2025 made clear the breadth of new and unique ideas that could arise within the HackDKU formula. Although we’re inclusive of all kinds of projects, this made judging complicated. This year, we’re scoping HackDKU to thecentral theme of Ethics and AI, but keeping the three-track system. Participants will still have the freedom to choose a track to specialize in, but their idea must adhere to this central theme that is of Ethics and AI. Judges decide on one winner per track, based on quality of the final product, presentation quality, and how the idea fits with the hackathon theme and track of choice. HackDKU is no longer an overnight hackathon, but a week-long sprint where teams are encouraged to create as much impact as possible. This extra time gives teams the freedom to fully create what they want, and bring something special to the HackDKU stage. Common milestones (i.e. Tuesday: submit wireframe) will be mandatory to keep all teams at a steady cadence. Any vibe coding must use the Lovable platform. Credits to access Lovable will be provided on the first day of the event.

Team

Track 1-3: Each team should have 2 to 4 members.

Track 4 (Luxshare): Teams must have 3 members max, and at least one DKU student in the team.

Submission Deadline

4.18 12:00 - Final Submission Deadline (Devpost)

Prizes

Track 1-3: 2500 RMB per track

Track 4 (Luxshare): 3000, 2000, 1000 RMB for top 3 teams

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.