Collaborative AI: Managing Group Projects When Every Student Has a Writing Assistant

The old group project just got more complicated. When I ask students to collaborate on a document now, I can't easily tell whether the work represents genuine collective effort or one student pasting AI-generated text while everyone else rides along. This is the practical challenge many of us are facing, and it deserves more than suspicion—it deserves a strategy. The most effective approach I've found is separating the AI-assisted work from the individually accountable work. Have students use AI tools collaboratively during the drafting phase—they can all prompt, revise, and shape the output together on a shared screen. Then require each student to submit a separate individual reflection or analysis that demonstrates their own critical engagement with the group's output. This way, the collaboration stays collaborative while the accountability remains individual. Another practical move: build in process documentation. Ask students to maintain a shared log of their decision-making, prompt revisions, and debates about content. This becomes both a collaboration artifact and evidence of each student's contribution. When the final product matters less than the thinking behind it, the AI becomes a genuine collaborative tool rather than a shortcut around engagement. The deeper shift here is reframing what we assess in group work. Rather than policing AI use, we're better off designing assignments where the collaboration process itself is the learning outcome. Students who can effectively direct AI tools, evaluate the output critically, and integrate it into genuine group deliberation are developing exactly the skills that matter in today's workplace. That's worth grading, and it's far easier to assess than the ghost of individual effort in a shared document.