Development of artificial intelligence competences for teachers in the context of Vietnam

To evaluate the necessity of enhancing artificial intelligence (AI) capacity, this article proposes a strategy for developing AI competencies among teachers in the Vietnamese context. A field survey consisting of open-ended questions was conducted with 285 individuals (94 teachers and 191 pedagogical students). A 5-point Likert scale was utilized for analysis, alongside semistructured interviews. The findings indicate that the demand for developing teachers' AI capacity is pressing. However, teachers have not yet had opportunities to attend training and professional development courses. The construction and development of AI capacity for teachers in Vietnam are currently in the preliminary stages. There are various opinions regarding the level of proficiency aligned with UNESCO's AI Competency Framework. Most respondents suggest that teachers need competence at the levels of acquiring and deepening, whereas the creation level should be left to AI specialists, allowing teachers to focus on professional development. The results also reveal that training and professional development in AI capacity for teachers represent a potential market that requires timely attention from educational authorities and teacher training institutions. Nevertheless, as program content, methodologies, and supporting conditions for AI capacity development are just in their infancy, training strategies must closely follow teachers' actual needs, adopting specific steps to avoid overburdening or overwhelming them.

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Publication Details

Journal
Multidisciplinary Science Journal
Published
2025-05-06
DOI
https://doi.org/10.31893/multiscience.2025605
Primary Topic
Digital literacy in education
Type
article
Field-Weighted Citation Impact
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article

Development of artificial intelligence competences for teachers in the context of Vietnam

Le Khanh Tuan, Bui Thi Hanh, Phan Thi Thanh Huong, D.T. Ha et al.
Multidisciplinary Science Journal
Digital literacy in education
article

Development of artificial intelligence competences for teachers in the context of Vietnam

Le Khanh Tuan, Bui Thi Hanh, Phan Thi Thanh Huong, D.T. Ha, Dang Thi Thu Lieu
article en

Abstract

To evaluate the necessity of enhancing artificial intelligence (AI) capacity, this article proposes a strategy for developing AI competencies among teachers in the Vietnamese context. A field survey consisting of open-ended questions was conducted with 285 individuals (94 teachers and 191 pedagogical students). A 5-point Likert scale was utilized for analysis, alongside semistructured interviews. The findings indicate that the demand for developing teachers' AI capacity is pressing. However, teachers have not yet had opportunities to attend training and professional development courses. The construction and development of AI capacity for teachers in Vietnam are currently in the preliminary stages. There are various opinions regarding the level of proficiency aligned with UNESCO's AI Competency Framework. Most respondents suggest that teachers need competence at the levels of acquiring and deepening, whereas the creation level should be left to AI specialists, allowing teachers to focus on professional development. The results also reveal that training and professional development in AI capacity for teachers represent a potential market that requires timely attention from educational authorities and teacher training institutions. Nevertheless, as program content, methodologies, and supporting conditions for AI capacity development are just in their infancy, training strategies must closely follow teachers' actual needs, adopting specific steps to avoid overburdening or overwhelming them.

Multidisciplinary Science JournalVol. 7(11)
The Vietnam National Institute of Educational Sciences (VN), Dong Thap University (VN), Saigon University (VN)
Openalex Percentile: Top 74%
Digital literacy in education
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Development of artificial intelligence competences for teachers in the context of Vietnam — Le Khanh Tuan, Bui Thi Hanh, et al. · Multidisciplinary Science Journal (2025) | TGRS Research Map | TGRS