possible battleground in the next arms race between technologies that facilitate cheating and technologies that may deter or detect cheating is artificial intelligence and machine learning. It has been reported that artificial intelligence can be used to create “passable” academic writing automatically (e.g. Abd‐Elaal et al., 2019). Automatic paraphrasing tools can potentially revise unoriginal work sufficiently to evade detection by text‐matching software (Rogerson and McCarthy, 2017). Artificial intelligence may also be used by contract cheating providers to find potential “customers” among students based, for example, on their social media posts (Amigud, 2020). By the same token, artificial intelligence and machine learning may be used to detect plagiarism, outsourcing, and machine‐written work and to verify students’ identity in online learning environments (Amigud et al., 2017). Additionally, artificial intelligence can be used to automate searches for files students inappropriately share online and have these taken down where necessary (Redden, 2021). Whether such an arms race results in a change in the prevalence of student plagiarism and cheating will depend on how fast and effectively cheating and anti‐cheating forces manage to bamboozle each other and respond in kind.
CONCLUSION
This chapter has demonstrated a downward trend in the prevalence of higher education students’ self‐reported plagiarism and cheating behavior in the 30 years 1990–2020. This downward trend is not attributable to a switch to commercial contract cheating, which appears to have remained at a steady, low prevalence in the past 30 years. However, there is evidence to suggest that text‐matching software, academic integrity and writing education, and honor codes may have contributed to the downward trend. Nonetheless, higher education teachers and administrators cannot afford to become complacent. The achievements of the past 30 years may not be permanent without continuing efforts to promote academic integrity and deal with emerging threats.
Funding Details
No funding was received to support this research.
Disclosure Statement
The author does not have any conflicts of interest associated with this chapter.
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