Whether under the quiet hum of a university library's fluorescent lighting or the late-night glow of a professor's study, we've long understood the pursuit of knowledge as a profoundly human endeavor that involves wrestling with complex ideas to create a slow burn of discovery, leading to the reward of learning. But what happens when a new tool enters this sacred space, promising to streamline, accelerate, and even bypass the struggles that have historically defined intellectual growth? This is the crossroads where academic leaders now stand regarding generative AI. Other professions have come to such crossroads, though none in as visible a way as the world of sports. Lessons from sports could help them find a path ahead.

Just as athletes have for generations had to wrestle with the temptations posed by various pharmaceutical shortcuts to enhance their performance, students and professors alike are now finding in AI a tool that promises to enhance their intellectual "performance". For students, the temptation is clear and present. AI can effortlessly churn out essays, solve complex mathematical problems, write code, or quickly summarize vast amounts of dense texts. This is akin to doping in sports: AI offers students a shortcut to better grades. And as is also true in athletics, students who resist this shortcut are ceding an advantage to classmates who make use of it.

Recognizing the threat that AI use poses to student learning and academic integrity, university leaders are rapidly evolving their rules, developing AI detection tools, and emphasizing the importance of original thought and the ethical use of AI. Although these discussions are happening a bit late (the AI horse has already left the barn), they are crucial. Importantly, just as has been the experience regarding performance-enhancing drugs in sports, tools to detect AI use reliably are likely to lag. Thus, addressing the reality of AI's temptation requires leaders to craft thoughtful policies, practices, and messaging.

There has yet to be a sufficient conversation focused on how faculty use the tool to enhance their performance that might parallel the ongoing one regarding student use. If students are expected to produce original thoughts when demonstrating their learning, shouldn't instructors, as custodians of intellectual integrity, be held to a similar standard regarding the materials and feedback used to teach? Yet, it is easy to imagine how a professor, buried under a mountain of grading and grant proposals, might see AI as a lifeboat in a storm.

Generative AI offers professors many "performance enhancements". It can rapidly generate lesson plans, lecture outlines, quizzes, or even entire courses. AI can also help generate nuanced feedback on student papers or produce highly polished examples for class discussions. This creates for the professor the appearance of greater depth or breadth of preparation than was earned through talent alone. In an increasingly demanding academic environment, with pressures to publish, secure grants, and manage larger class sizes, the temptation to leverage AI for efficiency is immense. Just as an athlete might feel pressure to dope up to keep up, a professor might feel compelled to use AI to manage an unmanageable workload.

The ethical dilemmas here mirror those in sports. Is faculty use of generative AI the academic equivalent of a doping scandal? After all, it erodes trust and devalues the core service being provided. If a professor uses AI to grade assignments without transparency, has the authenticity of the intellectual exchange been compromised? Has the social contract between the university, the professor, and the student been violated? Can AI-generated feedback be considered as helpful as feedback genuinely coming from a professor's expertise? What truly defines 'excellence' in teaching if a significant portion of the work was outsourced to an algorithm? When a professor creates an innovative course using AI, should it be flagged with an asterisk in the schedule of classes, akin to sports records where the proper credit for performance is equivocal?

What can academic leaders learn from the long, fraught history of doping in sports?

The story of performance-enhancing drugs in sports is a cautionary tale about the human desire for an edge and the integrity of competition. As generative AI weaves itself into the fabric of academia for both students and faculty, we stand at a similar crossroads. The decisions we make now about transparency, ethical guidelines, and fostering a culture of authentic intellectual pursuit will determine whether AI becomes a true partner in elevating education or another source of ethical compromise that diminishes the very value universities seek to create.