Imagining Computing Education Assessment after Generative AI

In the contemporary landscape of computing education, the ubiquity ofGenerative Artificial Intelligence has significantly disrupted traditionalassessment methods, rendering them obsolete and prompting educators to seekinnovative alternatives. This research paper explores the challenges posed byGenerative AI in the assessment domain and the persistent attempts tocircumvent its impact. Despite various efforts to devise workarounds, theacademic community is yet to find a comprehensive solution. Amidst thisstruggle, ungrading emerges as a potential yet under-appreciated solution tothe assessment dilemma. Ungrading, a pedagogical approach that involves movingaway from traditional grading systems, has faced resistance due to itsperceived complexity and the reluctance of educators to depart fromconventional assessment practices. However, as the inadequacies of currentassessment methods become increasingly evident in the face of Generative AI,the time is ripe to reconsider and embrace ungrading.