Artificial intelligence (AI) that generates new content using machine learning algorithms to build on previously created text, audio, or visual information is known as generative AI. Many people now view this sector as a “game-changer that society and industry need to be ready for” due to recent breakthroughs in the area and its previously unheard-of accessibility. For instance, Stable Diffusion and DALL-E have drawn much attention in the art world for their ability to produce works in various genres. Another generative AI technology, Amper Music, has previously been utilized to construct whole albums and generate music songs in any genre.
The most recent tool in this area is ChatGPT, which can produce textual replies that resemble human responses to various cues in several languages. To be more precise, it does so in a conversational way, allowing users to organically expand on earlier cues in the form of a continuous dialogue. For its almost unlimited value in multiple out-of-the-box applications, including creative writing, marketing, customer service, and journalism, to name a few, this tool has been dubbed an “extraordinary hit” and a “revolution in productivity.” With ChatGPT hitting one million users in only five days after its debut and surging to over 100 million monthly users in just two months, the tool’s capabilities have aroused much attention.
Despite its amazing capabilities, ethical issues have dogged generative AI. There has been continuous discussion over who owns the vast amounts of data that are available online and are used to train generative AI models. Additionally, as these tools develop, it becomes more difficult to distinguish between human and algorithmic creations. Education-related debates over academic integrity infractions by high school and university students have been prompted by ChatGPT’s capacity to produce essay writing and assignment solutions. For instance, educational districts in New York City, Los Angeles, and Baltimore have prohibited its usage in the United States.
Similarly, Australian colleges have stated that they want to resume “pen and paper” exams to discourage students from using technology to write essays. Since many instructors are worried about plagiarism, academics, including George Washington University, Rutgers University, and Appalachian State University, have decided to phase out take-home, open-book assignments completely. The use of ChatGPT to produce academic writing has also been prohibited by several conferences and publications, which is not unexpected considering that abstracts created by ChatGPT have been demonstrated to be identical to human-generated material.
However, several people have defended and even advocated ChatGPT to enhance writing production. In education, previous research has looked at the effectiveness and utility of big language models in various fields, including medical and healthcare, computer and data science, law, business, journalism and media, and language acquisition. Even though these studies found mixed results when comparing ChatGPT’s performance on standardized tests to that of students, studies that specifically compared the model’s performance to that of prior large language models all found that the task of question-answering had significantly improved.
Researchers in the past in their evaluation of ChatGPT’s performance on the US Medical Licencing Exam found that ChatGPT performed at or near the passing level on each of the test’s three phases without the need for extra specialized training or reinforcement. Similarly, others tested the ChatGPT model on the US Fundamentals of Engineering exam to assess its performance in the context of engineering. They demonstrated in their study how the model’s performance fluctuated depending on the exam’s many sections, scoring highly in some, like Professional Practise and Ethics, while scoring poorly in others, like Hydrology.
Despite these instances, a systematic investigation contrasting ChatGPT performance with that of students from different academic areas at the same university still needs to be improved in the literature. Additionally, it needs to be clarified where students and instructors stand on using this technology globally. Finally, it is uncertain if ChatGPT-generated assignment solutions are detectable. Here, researchers from New York University Abu Dhabi compare ChatGPT’s performance to that of students in 32 university-level courses from eight different fields to analyze its potential as a tool for plagiarism. They also investigate the feasibility of an obfuscation approach that may be used to avoid algorithms specially designed to detect ChatGPT-generated text.
They surveyed participants (N=1601) who were chosen from five different nations, namely Brazil, India, Japan, the United Kingdom, and the United States, to understand better the perspectives of students and educators on both the usefulness of ChatGPT as well as the ethical and normative problems that are raised with its use. They also conducted more in-depth surveys of 151 undergraduate students and 60 professors at the authors’ university to examine variations in how different fields see ChatGPT. They discovered that ChatGPT performs as well as, if not better, students in nine of the 32 courses. They also find that the present detection algorithms frequently mistakenly identify ChatGPT replies as AI-generated rather than human-generated.
To make matters worse, an obfuscation attack renders these algorithms useless, missing 95% of ChatGPT responses. Finally, there appears to be agreement among students that they will utilize ChatGPT for their academic work and among instructors that doing so will be treated as plagiarism. Given the inherent tension between these two, educational institutions must develop acceptable academic integrity regulations for generative AI generally and ChatGPT particularly. In the era of generative AI, their findings provide contemporary insights that could direct policy talks regarding educational reform.
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Aneesh Tickoo is a consulting intern at MarktechPost. He is currently pursuing his undergraduate degree in Data Science and Artificial Intelligence from the Indian Institute of Technology(IIT), Bhilai. He spends most of his time working on projects aimed at harnessing the power of machine learning. His research interest is image processing and is passionate about building solutions around it. He loves to connect with people and collaborate on interesting projects.
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