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Towards an Operationalization of AI acceptance among Pre-service Teachers

Authors:

Margarita Chikobava,

Ralf RomeikeAuthors Info & Claims

WiPSCE '21: Proceedings of the 16th Workshop in Primary and Secondary Computing Education

Article No.: 15, Pages 1 - 2

https://doi.org/10.1145/3481312.3481349

Published: 19 October 2021 Publication History

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Abstract

Artificial Intelligence (AI) has found its popularity in many fields of our life, ranging from song recommendation to lung cancer identification. CSE can impart AI knowledge to all people (including teachers) so that they can make an informed decision about AI. Besides, AI technologies can support tailoring the education to the students’ needs or unload teachers by doing routine work for them and leaving more space for creativity. However, despite the growing potential of AI applications in education, some skepticism or even rejection can be observed. It could be especially critical for teacher education: by being a role model for pupils, teachers could multiply their attitude towards AI while transferring it to the pupils.

Although AI acceptance is broadly investigated in the marketing context, there is not much prior research on AI acceptance among teachers. Besides, AI acceptance as a term is questionable due to the heterogeneity of the AI field. We highlight the research gap, propose an instrument for the data collection, and introduce the idea of the acceptance spectrum. The proposed concept is exemplified by the case study of acceptance of the AI-supported homework assessment among pre-service teachers.

References

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Annabel Lindner and Ralf Romeike. 2019. Teachers’ perspectives on artificial intelligence. In 12th International conference on informatics in schools,“Situation, evaluation and perspectives”, ISSEP.

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Mikeladze TMeijer PVerhoeff R(2024)A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical reviewEuropean Journal of Education10.1111/ejed.1266359:3Online publication date: 14-Apr-2024
https://doi.org/10.1111/ejed.12663
Wu DZhang XWang KWu LYang W(2024)A multi-level factors model affecting teachers’ behavioral intention in AI-enabled education ecosystemEducational technology research and development10.1007/s11423-024-10419-0Online publication date: 11-Sep-2024
https://doi.org/10.1007/s11423-024-10419-0
Zhang CHu MWu WKamran FWang X(2024)Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysisEducation and Information Technologies10.1007/s10639-024-12905-730:2(2645-2672)Online publication date: 27-Jul-2024
https://doi.org/10.1007/s10639-024-12905-7

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Towards an Operationalization of AI acceptance among Pre-service Teachers

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WiPSCE '21: Proceedings of the 16th Workshop in Primary and Secondary Computing Education

October 2021

119 pages

ISBN:9781450385718

DOI:10.1145/3481312

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 19 October 2021

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WiPSCE '21

WiPSCE '21: The 16th Workshop in Primary and Secondary Computing Education

October 18 - 20, 2021

Virtual Event, Germany

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Mikeladze TMeijer PVerhoeff R(2024)A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical reviewEuropean Journal of Education10.1111/ejed.1266359:3Online publication date: 14-Apr-2024
https://doi.org/10.1111/ejed.12663
Wu DZhang XWang KWu LYang W(2024)A multi-level factors model affecting teachers’ behavioral intention in AI-enabled education ecosystemEducational technology research and development10.1007/s11423-024-10419-0Online publication date: 11-Sep-2024
https://doi.org/10.1007/s11423-024-10419-0
Zhang CHu MWu WKamran FWang X(2024)Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysisEducation and Information Technologies10.1007/s10639-024-12905-730:2(2645-2672)Online publication date: 27-Jul-2024
https://doi.org/10.1007/s10639-024-12905-7

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Technology workshops by in-service teachers for pre-service teachers

A phenomenographic approach on teacher conceptions of teaching Artificial Intelligence (AI) in K-12 schools

Examining pre-service teachers' acceptance of technology-rich learning environments: A UAE case study

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