How Context Shapes Teachers’ Use of AI Grading

Schools are beginning to deploy artificial-intelligence (AI) tools that suggest grades or feedback, yet little is known about how context and explanations shape teachers’ use of these recommendations. We study this with an anonymous, preregistered online survey experiment of K–12 teachers. Participants grade brief, realistic vignettes and are randomly assigned to (i) no AI suggestion (control), (ii) an AI grade recommendation, or (iii) the same recommendation accompanied by task/rubric context and a concise rationale (explainability). For a subset of vignettes we manipulate the accuracy of the AI suggestion, enabling tests of calibration (accepting correct and rejecting incorrect advice). Primary outcomes are AI uptake/override, accuracy-conditioned uptake, perceived trust, fairness/transparency, workload, and the between-teacher variance of assigned grades. We further explore heterogeneity by experience, subject area, and prior AI use. The design isolates whether contextual and explanatory cues help teachers discriminate between good and bad AI advice and whether such cues improve perceptions without increasing cognitive burden. Findings will inform the design and training of human-in-the-loop assessment systems that support—rather than substitute for—professional judgment.