Chahna Gonsalves: the rise of interpretive load

When is ‘transparency’ not transparent? When it comes in the form of policy that carries different levels of risk for staff and students. For Chahna Gonsalves, the biggest issue with institutional AI policies is that it adds what she terms ‘interpretive load’ to students: the idea that they have to weigh up the risk of declaring their uses of generative AI in an environment that doesn’t adequately or explicitly set clear boundaries. For AI policies to be useful, they have to speak in pedagogical language to everyone, rather than punitive. It’s the quality of our feedback and the clarity of our expectations that counts, and staying true to the criteria we say we’re assessing. We’re looking for effort from our students so perhaps we can ensure that our own effort is clear too, in the form of understanding what these tools are capable of. And they are capable of much, but perhaps we need to embrace slow learning and failure as our dominant pedagogies in this new fast-paced world of instant gratification. Let’s all give ourselves a break and take the time to think!The resources we mentionedIllingworth, S. (2026). What UK university AI policies actually do: A study of 96 institutions. Available from: https://www.hepi.ac.uk/reports/what-uk-university-ai-policies-actually-do-a-study-of-96-institutions/ And the publication we talked aboutGonsalves, C. (2026). The transparency trap: Generative AI and the rise of interpretive load. Review of Education 14(1), https://doi.org/10.1002/rev3.70139