Meet the 2024 winners
This year’s top teams addressed access to education, income, and basic necessities like clothing for communities in need. Read more below on how the 2024 Global Challenge winners applied watsonx from IBM to address these issues of “equitable access.
Grand Prize: GoBang
Participant location: United States
Teachers in rural or under-resourced schools are often required to teach subjects they were not trained in. They have fewer colleagues to consult for help and less access to proven classroom materials. Despite the abundance of online resources, it is difficult for these teachers to navigate and organize all the information needed to build effective lesson plans, leaving children in these communities at a disadvantage. GoBang is a team of five Taiwanese students pursuing their education in the United States who are hoping to address educational inequality, especially for under-resourced schools in their home country. They felt that empowering teachers was key; an AI experience tailored to the way they work would be easier for teachers to adopt and could significantly improve their ability to effectively teach students. They interviewed elementary school teachers in Taiwan and worked with them on features of their solution. Their application, called Rita, provides educators with a customizable AI dashboard for class planning. Teachers can use Rita to generate semester-long lesson plans based on specific textbooks, get recommendations for key learning objectives, search verified videos relevant to the subject being taught, or generate worksheets and questions to test student understanding. In future iterations, the team plans to continue to improve the user interface based on teacher feedback, streamline responses and performance, and add additional features such as a worksheet creation wizard. They also hope to partner with textbook publishers to expand the solution’s capabilities and explore open-source opportunities. As the top-scoring university team, GoBang is also the recipient of the university grant for their schools: Georgia Institute of Technology and University of Michigan.
As the top-scoring university team, GoBang is also the recipient of the university grant for their schools: Georgia Institute of Technology and University of Michigan.
Key tech used in their solution:
First runner-up: Kind Threads
Participant location: India and United States
The UN International Covenant on Economic, Social and Cultural Rights (ICESCR)lists access to adequate clothing as a fundamental human right. Lack of adequate clothing can in turn impact access to other basic needs, as millions around the world do not have the clothing they need to go to school or attend a job interview, as examples. Meanwhile there is an environmental crisis as 92 million tonnes of discarded garments end up in landfills annually, contributing to environmental pollution and carbon emissions. Kind Threads is an AI-driven platform designed to help local communities take action to improve clothing and income access while also reducing clothing waste. Users can upload a photo of any clothing item, and with the help of IBM watsonx.ai for image recognition, the app will identify and categorize the item, assess its wearability, and provide data-driven recommendations on how it can be reused, recycled, or donated based on its condition. Those with clothing are encouraged to donate their items on the platform as the gamified experience displays the positive environmental impact of their contributions. The app also creates micro-economic opportunities within the local community, as individuals can sign up to pickup and deliver donated clothing to recycling centers to earn money. Others in the community can benefit from access to clothing that has been donated through the solution. The Kind Threads team from Persistent looks to expand their solution in the future, adding support for more languages, further integrating it with social apps like WhatsApp and Facebook Messenger, and partnering with larger recycling organizations or clothing brands.
Key tech used in their solution:
Second runner-up: T-chai
Participant location: United Kingdom
Research shows that students who receive personalized support in their learning journey outperform 98% of those who do not. However, teacher and tutor shortages, particularly in isolated, low-income, or underserved communities, combined with the high cost of tutoring services means families are often left on their own to find the right educational resources to help their children succeed. Online education is abundant, but how can parents find lessons best suited for their child? T-chai is an AI-powered homework tutor based on watsonx.ai and the IBM Granite Large Language Model (LLM) that adapts the level and complexity of its responses to match the student’s age and profile. T-chai can also assist parents in understanding their child's curriculum, finding age-appropriate educational resources, creating practice tests, and providing effective memorization techniques. The platform is optimized for both computers and smartphones, recognizing that not all families have access to home computers. In the future, Team T-chai hopes to develop a standalone app version, as well as additional lessons on specific topics for each age group, a dashboard for students and parents to track their progress, and gamification features to encourage students to discuss what they learned in school during the day and reinforce their learning experience. They also hope to forge partnerships with schools and libraries to extend T-chai's reach to students in remote locations who may lack personal devices or reliable home internet access.
Key tech used in their solution:
2024 AI Knowledge Challenge winner
Arnav Shukla, an undergraduate student at VIT Bhopal University in India, won the 2024 AI Knowledge Challenge. Arnav finished six hands-on generative AI courses with IBM watsonx for the chance to take the AI Knowledge Challenge Quiz, and was the first to score 100% on this final quiz. Arnav won $1,000 USD for winning this fun mini Call for Code challenge.
