GenAI is playing an increasingly important role in computing courses at all levels, offering new opportunities to support teaching and learning. However, using GenAI effectively raises important concerns regarding trust, academic integrity, and broader social and ethical dimensions. This Working Group was formed to report on the current state of the art in using GenAI in upper-level computing courses to aid educators. The working group will undertake a methodological review of published work and solicit input from the computing educational community as part of the report.

The growing use of Generative AI (GenAI) necessitates a re-evaluation of the design of programming assessments. This two-phase study examines the effects of GenAI integration into an introductory database unit, analysing student performance and problem-solving strategies through their interactions with GenAI during an SQL programming test. In the first phase of the study, 1,304 students participated in the practical test without prior integration of GenAI into the learning activities. The findings indicate that GenAI enhances performance for high-achieving students but is less effective for those struggling with fundamental concepts. The second phase will embed GenAI into lesson plans, allowing a direct comparison of its impact on student learning and assessment outcomes. This research aims to inform the best practices for GenAI-integrated assessments, with a focus on database education.