DISA

When? Friday December 5, 14-16
Where? Onsite: D0073 and via zoom

Agenda
14.00-14.10 Welcome and practical information
14.10-14.55 Presentation and discussion: 
A Comparative Evaluation of AI-Generated and Human-Written Alt Text for Image Accessibility – Mexhid Ferati
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion: 
Leveraging AI to predict review helpfulness and automate assessment in higher education – Zenun Kastrati

15.50 -16.00 Sum up and plan for our upcoming seminars

Abstracts

A Comparative Evaluation of AI-Generated and Human-Written Alt Text for Image Accessibility – Mexhid Ferati
This study investigates the comparative quality of AI-generated and human-written alternative text (alt text) for images, with the goal of understanding their respective strengths, limitations, and potential for supporting digital accessibility. The study was motivated by the growing use of generative AI tools, such as ChatGPT, in content creation, and the need to evaluate their effectiveness in producing accessible image descriptions.

The study follows an experimental design assessing 15 images across five thematic categories: People, Animals, Scenery, Food, Objects. The assessment is conducted for appropriateness in five evaluation criteria: accuracy, conciseness, fluency, comprehensibility, and relevance. The compared text for each image includes an alt text manually written by a professional content creator and generated by ChatGPT using standardized prompts. A pilot survey with eleven participants tested the clarity and functionality of the evaluation process before a final survey collected data from 101 participants in Sweden, who rated pairs of AI-generated and human-written descriptions across the five evaluation parameters.

Leveraging AI to predict review helpfulness and automate assessment in higher education – Zenun Kastrati
This presentation introduces two AI-driven approaches aimed at improving teaching and assessment in higher education. The first approach focuses on predicting the helpfulness of student reviews in online course using a deep learning framework that combines textual features with course metadata and student satisfaction. The second explores the application of large language models (LLMs) for automated scoring and feedback generation. Together, these approaches demonstrate how AI can enhance feedback loops and assessment scalability, while also addressing challenges related to interpretability, rubric clarity, and task subjectivity.