Exploring value judgements in grading: will teachers mark down student work assisted by GenAI, and should they?

Grading is a critical aspect of higher education, connected closely with student learning, credentialing, and institutional accountability. Recently, the widespread use of generative AI (GenAI) has complicated teachers' grading practices by challenging traditional notions of what constitutes a student's own work. As teachers weigh the contributions made by GenAI with the unique insights students bring to their work, they are also making value judgements regarding what should be accounted for and prioritised in grading. This study engaged 33 university teachers in scenario-based interviews to investigate their grading practices in a time when GenAI can mediate students' work quality to varying degrees. Data show that teachers do make value judgements of student work, which extend beyond the assignment itself to encompass teachers' conjecture about who the student is (person-oriented values), what they are capable of (capability-oriented values), how they relate to others (relation-oriented values), and whether the grading decision leads to good outcomes (justice-oriented values). Many non-academic values are prioritised in the grading (e.g. honesty, diligence, trust), and there are significant variations across teachers' value judgements. We foreground validity as an important concept to help teachers navigate this complex grading landscape and call for greater transparency about how students' GenAI use will be factored into teachers' grading decisions. The study seeks to move beyond the binary debate of whether GenAI use in student work constitutes cheating, towards a nuanced investigation into the subjectivities of grading in the age of GenAI.