Understanding ChatGPT: Impact Analysis and Path Forward for Teaching Computer Science and Engineering
Large Language Models (LLMs) like ChatGPT have become the most popular regenerative AI applications, used for obtaining responses for queries in different domains. The responses of ChatGPT are already becoming mainstream and are challenging conventional methods of learning. This article focuses on t...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10833612/ |
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| author | P. Banerjee Anurag K. Srivastava Donald A. Adjeroh Ramana Reddy Nima Karimian |
| author_facet | P. Banerjee Anurag K. Srivastava Donald A. Adjeroh Ramana Reddy Nima Karimian |
| author_sort | P. Banerjee |
| collection | DOAJ |
| description | Large Language Models (LLMs) like ChatGPT have become the most popular regenerative AI applications, used for obtaining responses for queries in different domains. The responses of ChatGPT are already becoming mainstream and are challenging conventional methods of learning. This article focuses on the application of ChatGPT for academic instructional purposes in the field of computer engineering and related majors. The capability of ChatGPT for instructional purposes is evaluated based on the responses to different questions about these engineering streams. This article explores different opportunities (with use cases), that ChatGPT can provide in augmenting the learning experience. It also provides scenarios of limitations and modifying the evaluation process to prevent the use of ChatGPT, which may lead to an inaccurate dissemination of accepted facts. In this paper, common classroom problems and their respective responses from ChatGPT in the domains of Computer Science, Cyber Security, Data Science, and Electrical Engineering are analyzed to determine the categories of queries for which ChatGPT offers reliable responses and those for which it may be factually incorrect. A student survey is performed to demonstrate that students must be made aware that ChatGPT may not be suitable for certain types of queries and means of upgrading the evaluation process. |
| format | Article |
| id | doaj-art-0ef82f036f8649fcbc0c82492cfb08ed |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0ef82f036f8649fcbc0c82492cfb08ed2025-01-28T00:01:24ZengIEEEIEEE Access2169-35362025-01-0113110491106910.1109/ACCESS.2024.352410210833612Understanding ChatGPT: Impact Analysis and Path Forward for Teaching Computer Science and EngineeringP. Banerjee0https://orcid.org/0000-0002-9629-7157Anurag K. Srivastava1https://orcid.org/0000-0003-3518-8018Donald A. Adjeroh2https://orcid.org/0000-0002-7982-4744Ramana Reddy3Nima Karimian4https://orcid.org/0000-0002-4590-7170Lane Department of Computer Science and Electrical Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, USALane Department of Computer Science and Electrical Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, USALane Department of Computer Science and Electrical Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, USALane Department of Computer Science and Electrical Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, USALane Department of Computer Science and Electrical Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, USALarge Language Models (LLMs) like ChatGPT have become the most popular regenerative AI applications, used for obtaining responses for queries in different domains. The responses of ChatGPT are already becoming mainstream and are challenging conventional methods of learning. This article focuses on the application of ChatGPT for academic instructional purposes in the field of computer engineering and related majors. The capability of ChatGPT for instructional purposes is evaluated based on the responses to different questions about these engineering streams. This article explores different opportunities (with use cases), that ChatGPT can provide in augmenting the learning experience. It also provides scenarios of limitations and modifying the evaluation process to prevent the use of ChatGPT, which may lead to an inaccurate dissemination of accepted facts. In this paper, common classroom problems and their respective responses from ChatGPT in the domains of Computer Science, Cyber Security, Data Science, and Electrical Engineering are analyzed to determine the categories of queries for which ChatGPT offers reliable responses and those for which it may be factually incorrect. A student survey is performed to demonstrate that students must be made aware that ChatGPT may not be suitable for certain types of queries and means of upgrading the evaluation process.https://ieeexplore.ieee.org/document/10833612/ChatGPTeducationLLMcomputer science and engineeringelectrical engineering |
| spellingShingle | P. Banerjee Anurag K. Srivastava Donald A. Adjeroh Ramana Reddy Nima Karimian Understanding ChatGPT: Impact Analysis and Path Forward for Teaching Computer Science and Engineering IEEE Access ChatGPT education LLM computer science and engineering electrical engineering |
| title | Understanding ChatGPT: Impact Analysis and Path Forward for Teaching Computer Science and Engineering |
| title_full | Understanding ChatGPT: Impact Analysis and Path Forward for Teaching Computer Science and Engineering |
| title_fullStr | Understanding ChatGPT: Impact Analysis and Path Forward for Teaching Computer Science and Engineering |
| title_full_unstemmed | Understanding ChatGPT: Impact Analysis and Path Forward for Teaching Computer Science and Engineering |
| title_short | Understanding ChatGPT: Impact Analysis and Path Forward for Teaching Computer Science and Engineering |
| title_sort | understanding chatgpt impact analysis and path forward for teaching computer science and engineering |
| topic | ChatGPT education LLM computer science and engineering electrical engineering |
| url | https://ieeexplore.ieee.org/document/10833612/ |
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