This article reviews the literature surrounding the growing use of Automated Paraphrasing Tools as a threat to educational integrity. In academia there is a technological arms-race occurring between the development of tools and techniques which facilitate violations of the principles of educational integrity, including text-based plagiarism, and methods for identifying such behaviors. APTs are part of this race, as they are a rapidly developing technology which can help writers transform words, phrases, and entire sentences and paragraphs at the click of (...) a button. This article seeks to review the literature surrounding the history of APT use and the current understanding of APTs placed in the broader context of the educational integrity-technology arms race. (shrink)

Is academic integrity research presented from a positive integrity standpoint? This paper uses Natural Language Processing techniques to explore a data set of 8,507 academic integrity papers published between 1904 and 2019.Two main techniques are used to linguistically examine paper titles: bigram analysis and sentiment analysis. The analysis sees the three main bigrams used in paper titles as being “academic integrity”, “academic dishonesty” and “plagiarism detection”. When only highly cited papers are considered, negative integrity bigrams dominate positive integrity bigrams. For (...) example, the 100 most cited academic integrity papers of all time are three times more likely to have “academic dishonesty” included in their titles than “academic integrity”. Similarly, sentiment analysis sees negative sentiment outperforming positive sentiment in the most cited papers.The history of academic integrity research is seen to place the field at a disadvantage due to negative portrayals of integrity. Despite this, analysis shows that change towards positive integrity is possible. The titles of papers by the ten most prolific academic integrity researchers are found to use positive terminology in more cases that not. This suggests an approach for emerging academic integrity researchers to model themselves after. (shrink)