AI, biometric analysis, and emerging cheating detection systems: The engineering of academic integrity?

Abstract

Cheating behaviors have been construed as a continuing and somewhat vexing issue for academic institutions as they increasingly conduct educational processes online and impose metrics on instructional evaluation. Research, development, and implementation initiatives on cheating detection have gained new dimensions in the advent of artificial intelligence (AI) applications; they have also engendered special challenges in terms of their social, ethical, and cultural implications. An assortment of commercial cheating–detection systems have been injected into educational contexts with little input on the part of relevant stakeholders. This paper expands several specific cases of how systems for the detection of cheating have recently been implemented in higher education institutions in the US and UK. It investigates how such vehicles as wearable technologies, eye scanning, and keystroke capturing are being used to collect the data used for anti-cheating initiatives, often involving systems that have not gone through rigorous testing and evaluation for their validity and potential educational impacts. The paper discusses accountability- and policy-related issues concerning the outsourcing of cheating detection in institutional settings in the light of these emerging technological practices as well as student resistance against the systems involved. The cheating-detection practices can place students in a disempowered, asymmetrical position that is often at substantial variance with their cultural backgrounds.

Online assessment and proctored testing through a biometric system. Experiences and challenges of university teachers

“…Plagiarism levels have surpassed the known barriers, even with the arrival of the pandemic to the world, new information windows were opened [26].…”

“…Cheating or academic frauds are problems that educational institutions carry with them and lead to impose various forms of evaluation, including the implementation of applications such as artificial intelligence [26].…”

Exploring the Dark Side of Online Distance Learning: Cheating Behaviours, Contributing Factors, and Strategies to Enhance the Integrity of Online Assessment

“…Second, lecturers may face difficulties in creating an interactive environment between students, potentially impacting the level of engagement and discipline (Noorbehbahani et al, 2022). Last, the issue of cheating arises more prominently in online assessments, necessitating robust strategies to uphold academic integrity (Oravec, 2022).…”

“…It involves actions intended to deceive lecturers and compromises the integrity of assessments. Examples of cheating in online learning include copying content from the internet without citations, using unauthorised online resources during assessments, seeking answers from others via social media, for example, during tests, copying and pasting someone else's work, collaborating without permission, falsifying data, exploiting technical vulnerabilities, and impersonating others during online exams or submissions (McCabe et al, 2001;Oravec, 2022;Valızadeh, 2022). It is important to note that these cheating types also occur in face-to-face (traditional) educational contexts.…”

“…Notably, technology has accelerated cheating in traditional assessment contexts. Online assessment is not immune to cheating, and this remains a significant issue (Arnold, 2016;Stevens et al, 2022;Oravec, 2022;Valızadeh, 2022), with some arguing that online education makes cheating more difficult to detect (Stevens et al, 2022). Maintaining academic integrity in online assessment poses a fresh set of opportunities and challenges for stakeholders in higher education institutions (HEIs) (Bretag et al, 2014;Ellis et al, 2020;Lee & Aslam, 2023;Noorbehbahani et al, 2022;Sutherland-Smith, 2008).…”