As faculty prepare their assessments for the new academic year, many are facing an artificial intelligence\u2013powered academic integrity conundrum.<\/p>\n
First, there\u2019s the undeniable reality that access to AI tools have made large-scale cheating easier than ever. Autonomous AI agents have shown that they can complete\u2014and ace\u2014entire online courses<\/a> without any help from the student enrolled in the class. Faculty at Brown <\/a>and Alcorn State Universities<\/a> went viral this summer after setting traps that indicated the majority of their students used AI to complete major assessments. And according to one recent national survey<\/a>, 73\u00a0percent of faculty say they have personally dealt with academic integrity issues involving AI.<\/p>\n At the same time, it\u2019s becoming increasingly difficult to hold students accountable for suspected unauthorized AI use. Numerous studies<\/a> have shown that AI detectors\u2014such as those developed by OpenAI, Writer, Copyleaks, GPTZero and CrossPlag\u2014exhibit inconsistencies, produce false positives when evaluating human-generated writing and are biased against non-native English writers<\/a>. In some cases, those false positives have resulted in accused students filing lawsuits against their universities<\/a>.<\/p>\n That reality has prompted a growing number of institutions\u2014including Yale, Vanderbilt, Johns Hopkins and Indiana Universities\u2014to enact academic integrity policies that ban or discourage faculty reliance on AI-detection programs as the sole evidence of alleged cheating. At least a dozen universities,<\/a> including Northwestern, Georgetown and New York Universities, have also disabled Turnitin\u2019s AI detection software, which was found to have a much higher false-positive rate<\/a> than the company originally suggested.<\/p>\n \u201cSome tools claim to detect whether a student used AI, but these services are highly unreliable,\u201d reads the newly updated Faculty AI Playbook<\/a> at Indiana\u2019s Kelley School of Business, which prohibits the use of AI detectors. \u201cInstead of trying to \u2018catch\u2019 AI use, focus on designing assignments that encourage process, reasoning, and authentic engagement.\u201d<\/p>\n For some universities, the turn away from AI detectors is also motivated by the fact that students are increasingly deploying AI to game the detectors.<\/p>\n \u201cEvidence also shows that \u2018humanizing\u2019 programs are very successful at reducing or eliminating the percentage of AI usage that is successfully detected in generative AI-produced writing,\u201d Jennifer Frederick, executive director of Yale\u2019s Poorvu Center for Teaching and Learning and her colleague Alfred Guy, director of the center\u2019s undergraduate writing and tutoring programs, wrote in a joint email to Inside Higher Ed. \u201cWe want to avoid the inevitable cat and mouse game created by AI detection tools: As the tools get better at \u2018catching\u2019 AI generated text, so do methods to evade detection. This becomes a technical exercise rather than a learning event.\u201d<\/p>\n \u2018Difficult, Delicate Moment\u2019<\/p>\n So where does this leave faculty members who want to ensure that students are learning and not offloading thinking to a machine?<\/p>\n \u201cIt\u2019s a difficult, delicate moment right now. And I\u2019m not sure we have all the answers,\u201d Kevin Yee, director of the Faculty Center for Teaching & Learning at the University of Central Florida, told Inside Higher Ed. \u201cIf faculty had an AI detector that was reliable, that\u2019s what they would use. But many are reluctantly moving in the direction of assignment redesign because they are at wit\u2019s end\u201d over AI-powered cheating.<\/p>\n From what he\u2019s observed, about half of faculty want to make as few changes as possible to AI-proof their assessments. But staying the course may be a viable option only for those who teach in-person classes with fewer than 30 students. In a small environment, \u201cthey can actually succeed in convincing students not to take these shortcuts, establishing strong relationships and convincing students that the learning is really what matters here,\u201d he said. \u201cIn larger classes, there\u2019s less of a personal connection between students and faculty, so faculty need to redesign their assessments to be more AI-resilient or a completely kind of deliverable that AI can\u2019t do, such as a blue book exam or an in-person oral exam.\u201d<\/p>\n