College instructors face a defining challenge as the fall semester begins: distinguishing authentic student work from AI-generated submissions. A veteran writing professor with forty years of classroom experience argues that faculty are asking the wrong initial question when suspicious work arrives.
The instinctive reaction when encountering polished, grammatically flawless writing is to suspect artificial intelligence. But according to the Faculty Focus article, instructors should reframe their approach entirely. Rather than starting with "Did the student write this?" educators should consider four foundational questions that address deeper assessment concerns.
This reorientation matters because the AI detection question, while understandable, deflects from the real pedagogical problems instructors face. Focusing solely on authorship authenticity leaves unexamined whether an assignment actually measures student learning, whether students understand the course content, and whether the grading criteria themselves remain valid in an age of generative AI tools.
The shift reflects a broader conversation in higher education about how institutions should respond to large language models like ChatGPT and Claude. Rather than treating AI as an adversary to outsmart through detection tools and honor codes, some educators argue for redesigning assignments and assessment practices altogether.
The four questions approach represents what some call "assignment redesign" strategy. This method encourages instructors to reconsider what skills matter most, how to structure work that requires genuine learning, and which components of coursework genuinely require human judgment. For example, an essay assignment that asks students to synthesize sources might become less effective when AI can generate plausible syntheses. But an assignment requiring students to defend their own position using specific evidence from their discipline often resists AI shortcutting because it demands disciplinary expertise and original reasoning.
This perspective aligns with recommendations from organizations like the American Association of University Professors and the Educause Research Center, which have urged institutions to focus less on policing AI use and more on pedagogical adaptation.
The timing is deliberate. Late September marks the first major assignment deadline in most courses, making it the moment instructors confront the gap between their original assignment design and the reality of AI tools in students' hands. Some professors have responded by moving toward open-note exams, oral defenses of written work, in-class writing, collaborative projects with documented contributions, and assignments requiring students to explain their reasoning process.
Others have integrated AI literacy into their courses, teaching students when these tools are appropriate, how to use them responsibly, and how to recognize their limitations and errors.
The Faculty Focus recommendation represents a practical pivot. Before assuming a student used AI, instructors should ask whether the assignment itself incentivizes authentic learning. This approach respects student agency while protecting academic integrity more effectively than detection tools alone.
As institutions enter fall semester, the question is no longer whether to detect AI. The question is how to teach in a world where students have access to it.