Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Harnessing the power of images in CALL: AI image generation for context specific visual aids in less commonly taught languages

Xu, Liang orcid logoORCID: 0000-0002-2619-1883, Uí Dhonnchadha, Elaine orcid logoORCID: 0000-0003-3448-4288 and Ward, Monica orcid logoORCID: 0000-0001-7327-1395 (2023) Harnessing the power of images in CALL: AI image generation for context specific visual aids in less commonly taught languages. In: EUROCALL 2023: CALL for all Languages, 15-18 August 2023, Reykjavik, Iceland.

Abstract
This paper explores the application of AI image generation in Computer-Assisted Language Learning (CALL) for Less Commonly Taught Languages (LCTLs). It delves into the potential of text to image generation models in creating context specific visual aids to enhance comprehension and engagement among learners. The integration of AI generated images in a language learning game, Cipher, is discussed, showcasing the benefits and challenges encountered. Learner feedback indicates positive inclinations towards the AI generated images, but also highlights the need for meticulous selection to address biases and stereotypes. Overall, this approach shows promise in creating culturally relevant CALL resources and improving language learning experiences for learners of LCTLs.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:CALL, less commonly taught languages, AI image generation, educational games
Subjects:Computer Science > Artificial intelligence
Computer Science > Interactive computer systems
Computer Science > Multimedia systems
Humanities > Irish language
Humanities > Language
Social Sciences > Educational technology
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Research Institutes and Centres > d-real
Published in: CALL for all Languages - EUROCALL 2023, Short Papers. . Editorial Universitat Politècnica de València.
Publisher:Editorial Universitat Politècnica de València
Official URL:https://riunet.upv.es/server/api/core/bitstreams/0...
Funders:Science Foundation Ireland
ID Code:31983
Deposited On:11 Dec 2025 11:25 by Liang Xu . Last Modified 11 Dec 2025 11:25
Documents

Full text available as:

[thumbnail of XuUiWard - Harnessing the power of images in CALL AI image generation for context specific visual....pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Creative Commons: Attribution-Noncommercial-Share Alike 4.0
1MB
Metrics

Altmetric Badge

Dimensions Badge

Downloads

Downloads

Downloads per month over past year

Archive Staff Only: edit this record