# Perception, performance, and detectability of conversational artificial intelligence across 32 university courses

arXiv: https://arxiv.org/abs/2305.13934 · HTML: https://mlsanity.com/paper/2305.13934/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 59 references that are in the map.
- First posted on arXiv 7 May 2023.

## Builds on (most cited first, 2 of 2)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Performance of ChatGPT on the US Fundamentals of Engineering Exam: Comprehensive Assessment of Proficiency and Potential Implications for Professional Environmental Engineering Practice](https://mlsanity.com/paper/2304.12198/) (2023)

## Built on by (most cited first, 12 of 12)

- [OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated Examples](https://mlsanity.com/paper/2307.11729/) (2023)
- [HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis](https://mlsanity.com/paper/2305.18226/) (2023)
- [The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?](https://mlsanity.com/paper/2305.07970/) (2023)
- [Ethical ChatGPT: Concerns, Challenges, and Commandments](https://mlsanity.com/paper/2305.10646/) (2023)
- [Evaluating Decision Models for Text Annotation in Computational Social Science](https://mlsanity.com/paper/2609.24574/) (2026)
- [Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text](https://mlsanity.com/paper/2309.07689/) (2023)
- [Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies](https://mlsanity.com/paper/2408.06872/) (2024)
- [Exploring the Potential of Generative AI for the World Wide Web](https://mlsanity.com/paper/2310.17370/) (2023)
- [AIDetection: A Generative AI Detection Tool for Educators Using Syntactic Matching of Common ASCII Characters As Potential 'AI Traces' Within Users' Internet Browser](https://mlsanity.com/paper/2503.16503/) (2025)
- [EduBot -- Can LLMs Solve Personalized Learning and Programming Assignments?](https://mlsanity.com/paper/2504.17824/) (2025)
- [Can the current trends of AI handle a full course of mathematics?](https://mlsanity.com/paper/2507.21664/) (2025)
- [Who Is Lagging Behind: Profiling Student Behaviors with Graph-Level Encoding in Curriculum-Based Online Learning Systems](https://mlsanity.com/paper/2508.18925/) (2025)

## Abstract (arXiv)

The emergence of large language models has led to the development of powerful tools such as ChatGPT that can produce text indistinguishable from human-generated work. With the increasing accessibility of such technology, students across the globe may utilize it to help with their school work -- a possibility that has sparked discussions on the integrity of student evaluations in the age of artificial intelligence (AI). To date, it is unclear how such tools perform compared to students on university-level courses. Further, students' perspectives regarding the use of such tools, and educators' perspectives on treating their use as plagiarism, remain unknown. Here, we compare the performance of ChatGPT against students on 32 university-level courses. We also assess the degree to which its use can be detected by two classifiers designed specifically for this purpose. Additionally, we conduct a survey across five countries, as well as a more in-depth survey at the authors' institution, to discern students' and educators' perceptions of ChatGPT's use. We find that ChatGPT's performance is comparable, if not superior, to that of students in many courses. Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection. Finally, we find an emerging consensus among students to use the tool, and among educators to treat this as plagiarism. Our findings offer insights that could guide policy discussions addressing the integration of AI into educational frameworks.