Date of Award:

5-2026

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Computer Science

Committee Chair(s)

Hamid Karimi

Committee

Hamid Karimi

Committee

Shuhan Yuan

Committee

John Edwards

Committee

Shah Hamdi

Committee

Angela Minichiello

Abstract

This dissertation studies how artificial intelligence, especially large language models such as GPT, can help students learn introductory computer programming when the models are used inside a carefully designed learning system. Instead of focusing on AI as a standalone tool, the dissertation follows a connected story: generating learning resources, building a tutoring platform, running a user study, and then analyzing how students behave while they program.

The work first uses prompt engineering to create a large collection of 11,700 Python exercises aligned with introductory computer science topics. Students and instructors then evaluate these exercises to check whether they are clear, relevant, and useful for learning. The dissertation next introduces CodeMasterAI, an intelligent tutoring system that presents these exercises in an interactive coding environment and provides AI-generated feedback without simply giving away the answer.

Using CodeMasterAI, the dissertation conducts a user study with 19 undergraduate students working on nine programming exercises. The platform records keystrokes, code snapshots, feedback requests, and survey responses, which makes it possible to study learning as it happens rather than only looking at final submissions. The later chapters use these data to examine cognitive load, how students use AI feedback, and how different learner profiles can be detected from programming behavior.

Overall, this work shows that large language models can be valuable tools for introductory programming education when they are paired with human evaluation, thoughtful system design, and detailed learning analytics. The dissertation provides both practical guidance for building AI-supported tutoring tools and research evidence for future personalized learning systems.

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