Advancing assessment practices in CS education through AI-generated visual test cases

Tools

Elawady, Mohamed; McNeill, Fiona and Alexandru, Cristina and Sentance, Sue and Cutts, Quintin, eds. (2025) Advancing assessment practices in CS education through AI-generated visual test cases. In: UKICER '25: Proceedings of the 2025 Conference on UK and Ireland Computing Education Research. Association for Computing Machinery (ACM), GBR, p. 1. ISBN 979-8-4007-2078-9 (https://doi.org/10.1145/3754508.3754538)

[thumbnail of Elawady-2025-Advancing-assessment-practices-in-CS-education]
Preview
 Text. Filename: Elawady-2025-Advancing-assessment-practices-in-CS-education.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (349kB)| Preview

Abstract

Recent advances in artificial intelligence (AI) technologies have enabled the generation of high-quality multimodal data, including text, audio, and visual content. These developments offer significant opportunities to improve assessment practices in computer science education, particularly within postgraduate machine learning courses. This paper investigates the integration of generative visual technologies into the assessment framework for computer vision coursework, aiming to evaluate their effectiveness in assessing student submissions through the creation of synthetic test cases.

ORCID iDs

Elawady, Mohamed ORCID logoORCID: https://orcid.org/0000-0002-4930-3825; McNeill, Fiona, Alexandru, Cristina, Sentance, Sue and Cutts, Quintin
CORE (COnnecting REpositories)