I-Corps: Translation Potential of an Artificial Intelligence (AI)-Driven Tutoring System Tailored for Coding and Programming Education

This I-Corps project is based on developing an artificial intelligence (AI)-powered tutoring system designed to enhance student learning in computer science education. Traditional tutoring methods often lack scalability and accessibility, leaving many students without the personalized support to master coding and programming concepts. This technology addresses this gap by leveraging generative AI to provide intelligent, adaptive tutoring that caters to individual student needs. By integrating AI with Science, Technology, Engineering and Mathematics (STEM) textbooks and interactive learning strategies, this system improves student engagement, comprehension, and problem-solving skills. This technology also may be extended to educational institutions, online learning platforms, and workforce training programs. The technology may support institutional education and address workforce development needs by accelerating programming skill acquisition, which may help to close the skill gap in the tech industry.

This I-Corps project utilizes experiential learning and a first-hand investigation of the industry ecosystem to assess the translation potential of an artificial intelligence (AI)-driven tutoring system that is tailored for coding and programming education. This technology leverages generative AI, large language models (LLMs), natural language processing (NLP), and machine learning (ML) to provide personalized, real-time assistance to computer science students. The system offers step-by-step guidance, explanations, and practice exercises in coding, particularly in Java, by analyzing student input and adapting to individual learning styles. In addition, the technology’s architecture enables effective engagement with students, fostering better comprehension of complex programming concepts. The goal is to enhance student learning efficiency, improve engagement, and offer a scalable solution for educators and learners alike.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Award Number: 2523923
Principal Investigator: Fazel Keshtkar
Funds Obligated: $50,000
State: NY