We have noticed that students and educators are actively using various mechanisms and tools for content creation, including AI tools.
AI tools have become part of the educational process, although they have only recently appeared. Students and teachers are using them because they are very efficient, fast and have access to significant amounts of information. However, there is some risk associated with the use of AI tools.
Our company decided to create a module that would meet the needs of educational institutions, organizations and publishing houses. An educational institution, having an effective tool to counteract abuses that may arise when using ChatGPT, Bard and other AI tools, will be able to better protect students from violating the principles of academic integrity and protect the quality standards of education.
The AI content report is placed inside the interactive similarity report, which is very convenient to analyse. It is also convenient to evaluate the document on two criteria at once and leave comments related to both AI and plagiarism.
By clicking on Details in the AI Content Search section, you will be able to open the second report.
Our report traces both the AI probability ratio and the AI probability for each text fragment by colouring the fragments. Each colour represents the probability of whether the text is written by an AI or a human. The report shows a list of fragments and the AI Probability Coefficient for each fragment.
If the text is green, the probability that it is machine written is minimal, if it is red, the probability that it is machine written is maximum.
These colours cannot be changed manually, accepted or rejected. The probability that the text is machine written is checked by the modules and algorithms that are the best at the moment.
The module applied supervised learning using several models, including a modified BERT model, to predict whether content is artificial or original. The artificial intelligence was presented with millions of texts of both AI and original content and then trained to determine the difference between the two. After each training session, a large set of test data is used to evaluate whether the new model is an improvement or not.