# Exploring the Use of ChatGPT as a Tool for Learning and Assessment in Undergraduate Computer Science Curriculum: Opportunities and Challenges

arXiv: https://arxiv.org/abs/2304.11214 · HTML: https://mlsanity.com/paper/2304.11214/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 16 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ChatGPT: The End of Online Exam Integrity?](https://mlsanity.com/paper/2212.09292/) (2022)
- [Learning gain differences between ChatGPT and human tutor generated algebra hints](https://mlsanity.com/paper/2302.06871/) (2023)
- [ChatGPT and Software Testing Education: Promises & Perils](https://mlsanity.com/paper/2302.03287/) (2023)
- [Let's have a chat! A Conversation with ChatGPT: Technology, Applications, and Limitations](https://mlsanity.com/paper/2302.13817/) (2023)
- [A Commodity SBC-Edge Cluster for Smart Cities](https://mlsanity.com/paper/1902.06661/) (2019)
- [On Energy Efficiency and Performance Evaluation of SBC based Clusters: A Hadoop case study](https://mlsanity.com/paper/1903.06648/) (2019)

## Built on by (most cited first, 7 of 7)

- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
- [Large Language Models in Computer Science Education: A Systematic Literature Review](https://mlsanity.com/paper/2410.16349/) (2024)
- [Humans are Still Better than ChatGPT: Case of the IEEEXtreme Competition](https://mlsanity.com/paper/2305.06934/) (2023)
- [To Google or To ChatGPT? A Comparison of CS2 Students' Information Gathering Approaches and Outcomes](https://mlsanity.com/paper/2501.11935/) (2025)
- [An Auditable Pipeline for Fuzzy Full-Text Screening in Systematic Reviews: Integrating Contrastive Semantic Highlighting and LLM Judgment](https://mlsanity.com/paper/2508.15822/) (2025)
- [Automated Formative Feedback for Short-form Writing: An LLM-Driven Approach and Adoption Analysis](https://mlsanity.com/paper/2509.22734/) (2025)

## Abstract (arXiv)

The application of Artificial intelligence for teaching and learning in the academic sphere is a trending subject of interest in the computing education. ChatGPT, as an AI-based tool, provides various advantages, such as heightened student involvement, cooperation, accessibility and availability. This paper addresses the prospects and obstacles associated with utilizing ChatGPT as a tool for learning and assessment in undergraduate Computer Science curriculum in particular to teaching and learning fundamental programming courses. Students having completed the course work for a Data Structures and Algorithms (a sophomore level course) participated in this study. Two groups of students were given programming challenges to solve within a short period of time. The control group (group A) had access to text books and notes of programming courses, however no Internet access was provided. Group B students were given access to ChatGPT and were encouraged to use it to help solve the programming challenges. The challenge was conducted in a computer lab environment using PC2 environment. Each team of students address the problem by writing executable code that satisfies certain number of test cases. Student teams were scored based on their performance in terms of number of successful passed testcases. Results show that students using ChatGPT had an advantage in terms of earned scores, however there were inconsistencies and inaccuracies in the submitted code consequently affecting the overall performance. After a thorough analysis, the paper's findings indicate that incorporating AI in higher education brings about various opportunities and challenges.