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From Words to Pixels: Artificial Intelligence Struggles with World Englishes

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Title: From Words to Pixels: Artificial Intelligence Struggles with World Englishes
Language: English
Authors: Flora Debora Floris (ORCID 0000-0001-8918-9695)
Source: JALT CALL Journal. 2025 21(3).
Availability: JALT CALL SIG. 1-6-1 Nishiwaseda Shinjuku-ku, Tokyo, 169-8050, Japan. e-mail: journal!jaltcall.org; Web site: https://jaltcall.org
Peer Reviewed: Y
Page Count: 28
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, College Students, Artificial Intelligence, English, Language Variation, Grammar, Intelligibility, Bias, Vocabulary, Pronunciation, Identification
Geographic Terms: Indonesia
ISSN: 1832-4215
Abstract: This study examines how DALL-E 3 interprets English descriptions written by Indonesian university students. Sixteen descriptive texts were submitted to the artificial intelligence (AI) tool, and the resulting images were compared to original photos. Most outputs showed clear mismatches. The analysis found that misinterpretations originated from two main sources: grammatical and vocabulary patterns reflecting Indonesian English and broader stylistic choices, such as the use of vague, emotional, or abstract language. The study also found that a high level of concrete detail could often mitigate the negative effects of non-standard grammar. The findings suggest that current AI tools are not yet equipped to fairly process the full range of human linguistic variation, from local English features to the stylistic patterns of human-centric writing. To support more inclusive use of AI in education, this study adapts the established concept of intelligibility into the idea of "digital intelligibility," and recommends improving training data and creating classroom space for open discussions about AI bias toward language diversity and stylistic choices.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1492452
Database: ERIC
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  Data: From Words to Pixels: Artificial Intelligence Struggles with World Englishes
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  Data: <searchLink fieldCode="AR" term="%22Flora+Debora+Floris%22">Flora Debora Floris</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8918-9695">0000-0001-8918-9695</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22JALT+CALL+Journal%22"><i>JALT CALL Journal</i></searchLink>. 2025 21(3).
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  Data: JALT CALL SIG. 1-6-1 Nishiwaseda Shinjuku-ku, Tokyo, 169-8050, Japan. e-mail: journal!jaltcall.org; Web site: https://jaltcall.org
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  Data: This study examines how DALL-E 3 interprets English descriptions written by Indonesian university students. Sixteen descriptive texts were submitted to the artificial intelligence (AI) tool, and the resulting images were compared to original photos. Most outputs showed clear mismatches. The analysis found that misinterpretations originated from two main sources: grammatical and vocabulary patterns reflecting Indonesian English and broader stylistic choices, such as the use of vague, emotional, or abstract language. The study also found that a high level of concrete detail could often mitigate the negative effects of non-standard grammar. The findings suggest that current AI tools are not yet equipped to fairly process the full range of human linguistic variation, from local English features to the stylistic patterns of human-centric writing. To support more inclusive use of AI in education, this study adapts the established concept of intelligibility into the idea of "digital intelligibility," and recommends improving training data and creating classroom space for open discussions about AI bias toward language diversity and stylistic choices.
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      – SubjectFull: College Students
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      – SubjectFull: Indonesia
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      – TitleFull: From Words to Pixels: Artificial Intelligence Struggles with World Englishes
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