Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant's Perspective
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917941842280448 |
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| author | Jamie, Pooriya Hajihashemi, Reyhaneh Alipour, Sharareh |
| author_facet | Jamie, Pooriya Hajihashemi, Reyhaneh Alipour, Sharareh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08899 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant's Perspective Jamie, Pooriya Hajihashemi, Reyhaneh Alipour, Sharareh Human-Computer Interaction Artificial Intelligence Data Structures and Algorithms K.3.2; I.2.6 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. |
| title | Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant's Perspective |
| topic | Human-Computer Interaction Artificial Intelligence Data Structures and Algorithms K.3.2; I.2.6 |
| url | https://arxiv.org/abs/2410.08899 |