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.

翻译：在当代计算机教育领域，生成式人工智能的普及已显著颠覆传统评估方法，使其趋于过时，并促使教育工作者寻求创新替代方案。本研究论文探讨了生成式人工智能在评估领域提出的挑战，以及规避其影响的持续尝试。尽管各方努力设计变通方案，但学术界尚未找到全面的解决之道。在此困境中，非评分制作为一种潜在但尚未被充分认识的评估困境解决方案崭露头角。非评分制作为一种脱离传统评分体系的教学方法，因其感知上的复杂性以及教育工作者对偏离传统评估实践的抵触情绪而面临阻力。然而，随着生成式人工智能面前现行评估方法的不足日益凸显，重新审视并接纳非评分制的时机已经成熟。