Evaluation of AI generated illustrations for learning. A Practical Framework for BA Theses

Generative Artificial Intelligence (GenAI) increasingly supports the creation of visual learning materials, yet standardized evaluation methods for AI-generated educational illustrations remain limited. This working paper presents a practical quality framework developed specifically for bachelor's thesis projects supervised by the authors. We introduce four core mandatory criteria—Conceptual Representation, Visual Correctness, Legibility & Labeling, and Overall Educational Usability—each rated on a three-point scale (poor, moderate, excellent). Optional supplementary criteria allow researchers to adapt the framework to their specific domain and research focus. The document further outlines procedures for defining target audiences and learning objectives, offers guidance on effective prompt engineering, and specifies a standardised documentation protocol using structured tables with mandatory metadata fields to ensure reproducibility and cross-project comparability. Recommendations for open licensing and publication are included.

Authors

Institutions

Publication Details

Journal
TUGraz OPEN Library (Graz University of Technology)
Published
2026-07-30
DOI
https://doi.org/10.3217/d4f2r-mft21
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation of AI generated illustrations for learning. A Practical Framework for BA Theses

Sandra Schön, Benedikt Brünner, Martin Ebner
TUGraz OPEN Library (Graz University of Technology)
Multimodal Machine Learning Applications
article

Evaluation of AI generated illustrations for learning. A Practical Framework for BA Theses

Sandra Schön, Benedikt Brünner, Martin Ebner
article en

Abstract

Generative Artificial Intelligence (GenAI) increasingly supports the creation of visual learning materials, yet standardized evaluation methods for AI-generated educational illustrations remain limited. This working paper presents a practical quality framework developed specifically for bachelor's thesis projects supervised by the authors. We introduce four core mandatory criteria—Conceptual Representation, Visual Correctness, Legibility & Labeling, and Overall Educational Usability—each rated on a three-point scale (poor, moderate, excellent). Optional supplementary criteria allow researchers to adapt the framework to their specific domain and research focus. The document further outlines procedures for defining target audiences and learning objectives, offers guidance on effective prompt engineering, and specifies a standardised documentation protocol using structured tables with mandatory metadata fields to ensure reproducibility and cross-project comparability. Recommendations for open licensing and publication are included.

TUGraz OPEN Library (Graz University of Technology)
Graz University of Technology (AT)
No poverty
Openalex Percentile: Top 9%
Multimodal Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.