Imagining Computing Education Assessment after Generative AI

TL;DR

Generative AI disrupts traditional computing education assessment by undermining the reliability and signaling value of numeric grades. The paper analyzes threats to assessment and argues for ungrading and intrinsic motivation as forward-looking alternatives, situating them within historical and contemporary trends such as mastery concepts. It contends that traditional grading is weak for credentialing and motivation and often exacerbates inequities, proposing ungrading as a viable path that requires cultural and structural changes in pedagogy. The work emphasizes a learner-centered shift toward feedback-rich, intrinsically motivated learning, aiming to catalyze a community of practice for AI-informed assessment innovation in computing education.

Abstract

In the contemporary landscape of computing education, the ubiquity of Generative Artificial Intelligence has significantly disrupted traditional assessment methods, rendering them obsolete and prompting educators to seek innovative alternatives. This research paper explores the challenges posed by Generative AI in the assessment domain and the persistent attempts to circumvent its impact. Despite various efforts to devise workarounds, the academic community is yet to find a comprehensive solution. Amidst this struggle, ungrading emerges as a potential yet under-appreciated solution to the assessment dilemma. Ungrading, a pedagogical approach that involves moving away from traditional grading systems, has faced resistance due to its perceived complexity and the reluctance of educators to depart from conventional assessment practices. However, as the inadequacies of current assessment methods become increasingly evident in the face of Generative AI, the time is ripe to reconsider and embrace ungrading.