Developing and validating the scale of language teachers’ design thinking competency in artificial intelligence language teaching (LTDTAILT)
Design thinking (DT) is one of the critical 21st-century digital skills that allows individuals to solve real-world problems by redesigning their problem procedure to achieve the desired outcome. Nowadays, cultivating DT within any educational program is becoming increasingly important for teachers...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X25000608 |
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| Summary: | Design thinking (DT) is one of the critical 21st-century digital skills that allows individuals to solve real-world problems by redesigning their problem procedure to achieve the desired outcome. Nowadays, cultivating DT within any educational program is becoming increasingly important for teachers since Artificial Intelligence (AI) and chatbots are available to all students. However, teachers themselves need to possess DT competency to effectively cultivate it in their students, particularly in humanistic fields like applied linguistics, Computer Assisted Language Learning (CALL), and Artificial Intelligence Language Teaching (AILT). For this reason, the researcher in the current study developed a new conceptual framework and a scale specifically designed for the field of AILT, known as Language Teachers’ Design Thinking Competence in Artificial Intelligence Language Teaching (LTDTAILT). The researcher generated the items both deductively and inductively, evaluating their face and content validity through the Delphi methodology and a cognitive review. Having piloted the scale, the researcher distributed it to 273 in-service English as a Foreign Language (EFL) teachers in Spain who had experience teaching languages using AI. The validation process, which included the Rasch-Andrich rating scale model (RSM), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA), confirmed that the theoretical framework and its scale were well-aligned and validated to study context and the field, comprising five components: empathy, define, ideate, prototype, and test, with a total of 17 items. Consequently, the researcher established a new theoretical framework applicable to the fields of artificial intelligence in general and specifically to CALL and AILT. Furthermore, this framework provides a pathway for English teachers and researchers to stay current with developments in other fields that integrate 21st-century digital skills and competencies through the application of artificial intelligence. |
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| ISSN: | 2666-920X |