An Instrument for Measuring Teachers' Trust in AI-Based Educational Technology

Tanya Nazaretsky, Mutlu Cukurova, Giora Alexandron

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Evidence from various domains underlines the key role that human factors, and especially, trust, play in the adoption of technology by practitioners. In the case of Artificial Intelligence (AI) driven learning analytics tools, the issue is even more complex due to practitioners' AI-specific misconceptions, myths, and fears (i.e., mass unemployment and ethical concerns). In recent years, artificial intelligence has been introduced increasingly into K-12 education. However, little research has been conducted on the trust and attitudes of K-12 teachers regarding the use and adoption of AI-based Educational Technology (EdTech). The present study introduces a new instrument to measure teachers' trust in AI-based EdTech, provides evidence of its internal structure validity, and uses it to portray secondary-level school teachers' attitudes toward AI. First, we explain the instrument items creation process based on our preliminary research and review of existing tools in other domains. Second, using Exploratory Factor Analysis we analyze the results from 132 teachers' input. The results reveal eight factors influencing teachers' trust in adopting AI-based EdTech: Perceived Benefits of AI-based EdTech, AI-based EdTech's Lack of Human Characteristics, AI-based EdTech's Perceived Lack of Transparency, Anxieties Related to Using AI-based EdTech, Self-efficacy in Using AI-based EdTech, Required Shift in Pedagogy to Adopt AI-based EdTech, Preferred Means to Increase Trust in AI-based EdTech, and AI-based EdTech vs Human Advice/Recommendation. Finally, we use the instrument to discuss 132 high-school Biology teachers' responses to the survey items and to what extent they align with the findings from the literature in relevant domains. The contribution of this research is twofold. First, it introduces a reliable instrument to investigate the role of teachers' trust in AI-based EdTech and the factors influencing it. Second, the findings from the teachers' survey can guide creators of teacher professional development courses and policymakers on improving teachers' trust in, and in turn their willingness to adopt, AI-based EdTech in K-12 education.

Original languageEnglish
Title of host publicationLAK 2022 -12th International Learning Analytics and Knowledge Conference
Pages56-66
Number of pages11
ISBN (Electronic)9781450395731
DOIs
StatePublished - 21 Mar 2022
Event12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022 - Virtual, Online, United States
Duration: 21 Mar 202225 Mar 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
Country/TerritoryUnited States
CityVirtual, Online
Period21/03/2225/03/22

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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