One of the missing elements in the broad discussion about artificial intelligence, teaching, and student work is “grading”.
When students write or present an analysis, professors and instructors are usually looking for specific content based on the assignment brief. However, there is no single correct way to demonstrate reasoning, understanding, or judgement. This has always made grading more complex than simply checking whether students included the required points. In the age of generative AI, that complexity has become even more pronounced.
I have been an adjunct professor at a large college for more than seven years. During that time, I have had the privilege of teaching several business management courses, ranging from 100-level introductory courses to 400-level capstone courses.
Over the years, I have watched these courses evolve, and I have contributed to content revisions designed to meet changing academic and professional expectations. Course materials have been revised, and rubrics have been updated. Yet what has not evolved at the same pace is the way many instructors approach grading in an environment shaped by AI.
From my experience, and from conversations with other instructors across various platforms, many of us were not fully prepared for the sudden presence of AI-generated writing in student papers. Some instructors have made significant progress in understanding this shift, while others are still trying to get their arms around it.
