Large Language Models, such as ChatGPT, Google Bard, Copilot, and other artificial intelligence (AI) systems, possess the capability to generate code based on natural language descriptions. Previous research on AI code generators has primarily focused on assessing their usability for novice programmers and highlighting their advantages for curriculum development in educational settings. Although these studies have yielded valuable insights, there remains considerable gaps in learning sciences research with respect to the potential benefits for novice programmers who face challenges and the inherent limitations with relying extensively on code generated by these tools. Based on information processing theories related to skill acquisition and program comprehension in introductory programming, this research aims to gain insights into learning, transfer, and performance of students using AI code generators. Accepting: PhD studentsExpress your interest in working with Dr. Eric Poitras.When contacting us about your interest in a particular professor or fellowship, please include your:Research interestsPast research experienceRecent CVDalhousie's Faculty of Computer Science offers competitive funding to qualified graduate students and is committed to promoting excellence in research and teaching.