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The rapid advancement of artificial intelligence has created new educational opportunities, but it has also widened existing gaps for students from low-income backgrounds who often lack early exposure to AI literacy and computational thinking. Without intentional intervention, these students risk being left behind in an increasingly technology-driven learning landscape. 

 

This research project, led by Dr Katherine Guanji Yuan from the National Institute of Education (NIE), was designed to address this inequity through an innovative AI-Coding Hackathon model for low-socioeconomic-status Indian students. Supported by the SINDA Research Fund, the project aimed to develop and test a research-informed, Knowledge Building–guided programme that emphasised collaboration, reflection, and inclusive participation, while building foundational AI competencies. 

 

In the short term, the grant enabled the successful prototyping, implementation, and evaluation of the hackathon model. Findings from the pilot demonstrated improvements in students’ AI literacy, metacognitive reflection, and collaborative skills. The project also generated rigorous mixed-methods data and a validated research protocol. Academic outputs included an Artificial Intelligence in Education (AIED) 2025 long paper, a dissemination workshop, and an NTU/NIE news feature, contributing to the growing body of research on equitable AI education. 

 

Beyond the initial study, the project laid the foundation for a larger Education Research Funding Programme (ERFP) Programmatic Grant, which will scale the hackathon model across multiple cohorts over several years. Insights from the research are also informing future teacher-training modules and the design of inclusive, community-grounded AI learning environments. Support from the SINDA Research Fund has enabled this work to advance both academic knowledge and practical pathways for equitable, future-ready learning among underserved students.