PhD Progress Tracker

Thesis Topic

An AI-Based Methodology for Improving Programming Education

Summary of Thesis Project

Development and evaluation of an AI‑driven tutoring system that provides personalized, context‑aware hints and feedback to first‑year programming students.

Justification of Research Topic

Novice/Beginner programmers often struggle with abstract concepts and receive only generic feedback. AI can spot misunderstandings right away, give each student the specific help they need, and make learning less overwhelming—but we still don't know how well it works in early programming classes.

Hypotheses

• H1: Students who use the AI tutor will do much better in programming than students in the regular class.
• H2: The AI help will improve students’ computational thinking skills more than normal exercises.
• H3: Students in the AI‑tutor group will feel more motivated and keep working on problems longer than others.

Methodology

Experimental Study: Randomized Controlled Trial with two groups (AI tutor vs. standard lab). Data Collection: Pre‑/post‑tests for programming and Computational Thinking, system logs, motivational surveys, semi‑structured interviews.

Work Plan

• YEAR 1:
• Systematic Literature Review (SLR)
• Topic: "AI-Supported Programming Teaching Tools and Methods," "AI-Based Hints and Feedback for Novice Programmers," "The Relationship Between Programming Learning and Computational Thinking."
• Determination of SLR methodology, data collection, and analysis.
• Completion of the SLR paper and its submission to a journal.
• YEAR 2:
• Development of the first prototype of the AI-supported learning tool.
• Example: Interactive exercises covering specific fundamental programming concepts (variables, loops, conditions, etc.) and a simple AI-based hint/feedback mechanism for these exercises.
• Conducting the first pilot study of the prototype with a small group of students.
• Improvement and continued development of the prototype based on feedback.
• YEAR 3:
• Completion of a more advanced version of the AI-supported learning tool.
• Planning and preparation of materials for the main experimental study (e.g., a comparative study with experimental and control groups).
• Measurement tools: Programming achievement tests, CT tests, motivation/self-efficacy scales.
• YEAR 4:
• Focusing on thesis writing: Structuring chapters (introduction, literature review, methodology, findings, discussion, conclusion), integrating published/submitted papers into the thesis format.
• Making revisions to the thesis based on supervisor feedback.
• Completion of the thesis draft and submission to the supervisor.