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Perception, performance, and detectability of conversational artificial intelligence across 32 university courses.
Hazem Ibrahim1, Fengyuan Liu1, Rohail Asim1
1Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE.
Scientific Reports
|August 24, 2023
Summary
Large language models like ChatGPT perform comparably to university students across many courses. Current AI detectors struggle to identify ChatGPT use, and students increasingly adopt it while educators view it as plagiarism.
Area of Science:
- Artificial Intelligence in Education
- Academic Integrity
- Natural Language Processing
Background:
- Large language models (LLMs) like ChatGPT can generate human-like text, raising concerns about academic integrity.
- The impact of LLMs on student evaluation and the perspectives of students and educators are largely unknown.
Purpose of the Study:
- To compare ChatGPT's performance against university students across diverse disciplines.
- To evaluate the detectability of ChatGPT-generated text by AI classifiers.
- To survey student and educator perceptions on using ChatGPT for academic work.
Main Methods:
- Performance comparison of ChatGPT versus students on 32 university-level courses.
- Assessment of AI-text classifiers' ability to detect ChatGPT output.
- Global and institutional surveys on student and educator attitudes towards ChatGPT use.
Main Results:
- ChatGPT's performance is on par with or exceeds student performance in numerous courses.
- AI-text classifiers are unreliable for detecting ChatGPT use, with false positives and easy evasion through text editing.
- A growing student consensus favors ChatGPT use, while educators increasingly consider it plagiarism.
Conclusions:
- ChatGPT presents a significant challenge to traditional academic integrity frameworks.
- Current detection methods are insufficient, necessitating policy discussions on AI integration in education.
- Understanding and addressing the evolving landscape of AI in academia is crucial.
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