What happened
Faculty Focus published an essay by Roger Ochse, EdD, Professor and Honors Director Emeritus at Black Hills State University, urging instructors to ask four grading questions before fall writing assignments. Ochse argues that generative AI has exposed weaknesses in relying on final drafts as evidence of student work.
Why it matters
For educators and course designers, the practical issue is not only whether a student used AI, but whether the assignment produces enough evidence to support a fair judgment. Ochse says independent researchers have found AI detectors inconsistent across text types, behind newer models, and prone to flagging multilingual writers. His assessment frame shifts attention from post-submission suspicion to assignment design: proposals, drafts, revisions, reflections, and decision records can make learning processes more visible. Rubrics that heavily reward fluency, organization, mechanics, and polish may no longer distinguish the judgment instructors want to assess.
What to do next
Review upcoming assignments before submission dates. Add lightweight process evidence such as a proposal, rough draft with visible revision, or reflection on changes. Reweight rubrics toward defensible claims, explained choices, meaningful revision, and interpretations students can discuss. Replace simple AI-use checkboxes with brief records of prompts, outputs, selections, rejections, and reasoning. For borderline cases, Ochse recommends peer calibration with a colleague rather than relying on detector percentages. The approach is presented as instructor practice, not a guarantee.
About this briefing
Reviewed by TutorFlow Editorial. We link the primary source, preserve its publication date, and distinguish reported claims from TutorFlow analysis. Our commentary focuses on practical decisions for educators and training teams.
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