Publication Date

Spring 2026

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Ethel Tshukudu

Second Advisor

Melody Moh

Third Advisor

Rashmi Vishwanath Bhat

Keywords

Artificial Intelligence, Machine Learning, Computer Science Education, Conceptual Transfer, Prompt Engineering, Agentic AI, Serverless Architecture

Abstract

In recent years, machine learning (ML) has become widespread across engineering domains. Shopping recommendations, image processing, image generation, and more recently the rapid development of generative artificial intelligence (AI), especially large language models (LLMs), have increased the adoption of AI and machine learning systems. Machine learning is now considered a foundational skill in many engineering disciplines. However, teaching machine learning is challenging. The education system is still tailored more toward traditional programming than toward machine learning. When students learn machine learning after learning basic programming, they may transfer their existing programming knowledge to machine learning concepts. The goal of this project is to address that gap in computer science education by explicitly modeling lessons based on conceptual transfer and using analogies to teach machine learning concepts. This project is an AI-based learning tool that teaches machine learning concepts by mapping them onto programming concepts students already know.

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Available for download on Monday, May 24, 2027

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