Originlens: A Real-Time AI-Generated Text and Plagiarism Detection using Deep Learning and Augmented Reality


Henry Muyang Liu1 and Jenny Do2, 1Liberal Arts and Science Academy, United States, 2Naval Facilities Engineering Systems Command (NAVFAC), USA


ChatGPT has integrated itself into the academic space in an unprecedented timeframe, as the promise of hours of work done in seconds can outweigh the sense of honor and logic. This project determines whether a student is cheating or not by reducing the presence of human decision-making while simultaneously acting as a deterrent for future usage of AI technology. Utilizing databases such as Kaggle, we can procure several samples of human writingin conjunction with ChatGPT's API to generate artificial intelligence instances, which are then stored for usage in machine learning algorithms. Employing powerful Python libraries such as Sklearn and NLTK, we can utilize natural learning processing, the ability for computers to understand human writing, to yield an algorithm that can predict with approximately 96% certainty. The result is a probability ratio, with one side displaying the percentage chance of the sample being human-written, whereas the other displays the likelihood of AI-generated instances. Furthermore, innovation lies in integrating this algorithm with wearable augmented reality technology, allowing users to efficiently scan and assess text elements. This approach amalgamates and helps reduce the delay between text input and response, empowering users to contribute to the decision-making process in identifying academic dishonesty without any loss in efficiency. The result that is shown displays pieces of information to the user that can all play a large role when determining the possibility of cheating, granting the user a role in making the decision along with simple scanning of each text element.


Deep Learning, Augmented Reality, Data Mining, Aritificial Intelligence

Full Text  Volume 14, Number 4