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

Mohamed Elawady

doi:10.1145/3754508.3754538

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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.

Original language	English
Title of host publication	UKICER '25: Proceedings of the 2025 Conference on UK and Ireland Computing Education Research
Editors	Fiona McNeill, Cristina Alexandru, Sue Sentance, Quintin Cutts
Publisher	Association for Computing Machinery (ACM)
Pages	1-1
Number of pages	1
ISBN (Print)	979-8-4007-2078-9
DOIs	https://doi.org/10.1145/3754508.3754538
Publication status	Published - 3 Sept 2025
Event	UKICER 2025: UK and Ireland Computing Education Research Conference - Edinburgh, United Kingdom Duration: 4 Sept 2025 → 5 Sept 2025

Conference

Conference	UKICER 2025: UK and Ireland Computing Education Research Conference
Country/Territory	United Kingdom
City	Edinburgh
Period	4/09/25 → 5/09/25

Keywords

Artificial Intelligence
Computer Vision
Generative AI
Diffusion Models
Coursework Assessment,
Higher Education

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3754508.3754538Licence: CC BY 4.0

Elawady-2025-Advancing-assessment-practices-in-CS-educationFinal published version, 341 KBLicence: CC BY 4.0

Cite this

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title = "Advancing assessment practices in CS education through AI-generated visual test cases",

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.",

keywords = "Artificial Intelligence, Computer Vision, Generative AI, Diffusion Models, Coursework Assessment,, Higher Education",

author = "Mohamed Elawady",

year = "2025",

month = sep,

day = "3",

doi = "10.1145/3754508.3754538",

language = "English",

isbn = "979-8-4007-2078-9",

pages = "1--1",

editor = "Fiona McNeill and Cristina Alexandru and Sue Sentance and Quintin Cutts",

booktitle = "UKICER '25: Proceedings of the 2025 Conference on UK and Ireland Computing Education Research",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "UKICER 2025: UK and Ireland Computing Education Research Conference ; Conference date: 04-09-2025 Through 05-09-2025",

}

Elawady, M 2025, Advancing assessment practices in CS education through AI-generated visual test cases. in F McNeill, C Alexandru, S Sentance & Q Cutts (eds), UKICER '25: Proceedings of the 2025 Conference on UK and Ireland Computing Education Research. Association for Computing Machinery (ACM), pp. 1-1, UKICER 2025: UK and Ireland Computing Education Research Conference, Edinburgh, United Kingdom, 4/09/25. https://doi.org/10.1145/3754508.3754538

Advancing assessment practices in CS education through AI-generated visual test cases. / Elawady, Mohamed.
UKICER '25: Proceedings of the 2025 Conference on UK and Ireland Computing Education Research. ed. / Fiona McNeill; Cristina Alexandru; Sue Sentance; Quintin Cutts. Association for Computing Machinery (ACM), 2025. p. 1-1.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AU - Elawady, Mohamed

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Y1 - 2025/9/3

N2 - 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.

AB - 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.

KW - Artificial Intelligence

KW - Computer Vision

KW - Generative AI

KW - Diffusion Models

KW - Coursework Assessment,

KW - Higher Education

U2 - 10.1145/3754508.3754538

DO - 10.1145/3754508.3754538

M3 - Conference contribution book

SN - 979-8-4007-2078-9

SP - 1

EP - 1

BT - UKICER '25: Proceedings of the 2025 Conference on UK and Ireland Computing Education Research

A2 - McNeill, Fiona

A2 - Alexandru, Cristina

A2 - Sentance, Sue

A2 - Cutts, Quintin

PB - Association for Computing Machinery (ACM)

T2 - UKICER 2025: UK and Ireland Computing Education Research Conference

Y2 - 4 September 2025 through 5 September 2025

ER -

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

Abstract

Conference

Keywords

UN SDGs

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Cite this