The Invisible Mentor: Inferring User Actions from Screen Recordings to Recommend Better Workflows

Users of feature-rich tools like Excel often miss more efficient workflows, repeating tedious steps and making avoidable errors. Current approaches to helping them require either manual prompting, which is effortful for users, or automated logging, which is limiting for developers. We present InvisibleMentor, a system inspired by over-the-shoulder learning: it observes what users do, then shows them how to do it better. To do this, InvisibleMentor analyzes screen recordings with a vision-language model to reconstruct actions and context, then uses a large language model to generate vision-grounded task reflection, structured suggestions grounded in observed behavior. In a user study, participants found InvisibleMentor’s suggestions more clear, more relevant, and more useful than those from a prompt-based assistant, demonstrating that AI can do more than automate away work—it can help users master it.