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Most GenAI policies in education are binary: allowed or banned, applied the same way to every student. We argue this ignores the developmental nature of learning, and propose adaptive boundaries: GenAI access that co-evolves with a student’s demonstrated competency, negotiated among students, teachers, and the GenAI system itself.
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How we developed and evaluated an AutoML system that helps novices build their own machine learning models, and distilled four design principles to improve trust, understanding, and usability based on a 24-participant user study.
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How we investigated the impact of VR and haptic feedback technologies on online shopping experiences and consumer decision-making processes in e-commerce environments.
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How we developed a cascaded transformer architecture coupled with a Mixture-of-Agents system to improve automated grading accuracy by over 50% for incorrect answers while maintaining transparency for teaching staff.
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How we designed INSIGHT, a modular proof-of-concept that combines AI tools to assist teaching staff and students with exercises while using keyword analysis to build dynamic FAQs and provide insights for personalized face-to-face support.
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How I explored the applications of Large Language Models in education and built a prototype that demonstrates AI tools for supporting both teachers and students while maintaining meaningful interaction.