Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant's Perspective
This addresses the challenge of improving student outcomes and TA efficiency in computer science education through a hybrid human-AI approach, though it is incremental as it builds on existing LLM applications.
The study integrated ChatGPT as a supplementary tool for teaching assistants in a data structures and algorithms course, using structured prompts and human oversight to enhance instruction, resulting in students scoring 16.50 points higher on average and excelling in advanced topics.
Integrating large language models (LLMs) like ChatGPT into computer science education offers transformative potential for complex courses such as data structures and algorithms (DSA). This study examines ChatGPT as a supplementary tool for teaching assistants (TAs), guided by structured prompts and human oversight, to enhance instruction and student outcomes. A controlled experiment compared traditional TA-led instruction with a hybrid approach where TAs used ChatGPT-4o and ChatGPT o1 to generate exercises, clarify concepts, and provide feedback. Structured prompts emphasized problem decomposition, real-world context, and code examples, enabling tailored support while mitigating over-reliance on AI. Results demonstrated the hybrid approach's efficacy, with students in the ChatGPT-assisted group scoring 16.50 points higher on average and excelling in advanced topics. However, ChatGPT's limitations necessitated TA verification. This framework highlights the dual role of LLMs: augmenting TA efficiency while ensuring accuracy through human oversight, offering a scalable solution for human-AI collaboration in education.