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Digital Mindset and Sensemaking in AI Integration: A Study of Open Education Teachers, Competence and Trust in AI

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Title: Digital Mindset and Sensemaking in AI Integration: A Study of Open Education Teachers, Competence and Trust in AI
Language: English
Authors: Nan Jiang (ORCID 0009-0000-7652-7341), Na Meng (ORCID 0000-0003-2504-5371)
Source: SAGE Open. 2025 15(4).
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 22
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, Technology Uses in Education, Artificial Intelligence, Educational Technology, Technological Literacy, Teacher Attitudes, Predictor Variables, Technology Integration, Self Efficacy, Trust (Psychology), Distance Education, Open Universities, College Faculty
Geographic Terms: China
DOI: 10.1177/21582440251396497
ISSN: 2158-2440
Abstract: This study investigates how digital mindset, AI sensemaking, trust in AI, digital teaching competence, and institutional support influence AI integration among open education teachers in China. Drawing on Sensemaking Theory, the Digital Mindset Framework, and Algorithm Aversion Theory, the study employs a three-wave survey of 366 instructors in Guangdong Province and analyzes the data using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings reveal that digital mindset and AI sensemaking significantly predict AI integration, while trust in AI and digital teaching competence do not have direct or consistent mediating effects. Surprisingly, perceived institutional support negatively moderates the relationship between digital mindset and both AI sensemaking and trust, suggesting that overly directive support may undermine autonomy in open education settings. The study contributes theoretically by extending digital mindset and sensemaking frameworks to the AI-in-education context and offering a context-sensitive interpretation of trust theory. Practically, it highlights the need for autonomy-respecting, interpretive support structures and mindset-based training programs. These insights inform more effective AI adoption strategies in decentralized learning environments.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1495670
Database: ERIC
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  Data: Digital Mindset and Sensemaking in AI Integration: A Study of Open Education Teachers, Competence and Trust in AI
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  Data: <searchLink fieldCode="AR" term="%22Nan+Jiang%22">Nan Jiang</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0000-7652-7341">0009-0000-7652-7341</externalLink>)<br /><searchLink fieldCode="AR" term="%22Na+Meng%22">Na Meng</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-2504-5371">0000-0003-2504-5371</externalLink>)
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  Data: 10.1177/21582440251396497
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  Data: 2158-2440
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  Data: This study investigates how digital mindset, AI sensemaking, trust in AI, digital teaching competence, and institutional support influence AI integration among open education teachers in China. Drawing on Sensemaking Theory, the Digital Mindset Framework, and Algorithm Aversion Theory, the study employs a three-wave survey of 366 instructors in Guangdong Province and analyzes the data using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings reveal that digital mindset and AI sensemaking significantly predict AI integration, while trust in AI and digital teaching competence do not have direct or consistent mediating effects. Surprisingly, perceived institutional support negatively moderates the relationship between digital mindset and both AI sensemaking and trust, suggesting that overly directive support may undermine autonomy in open education settings. The study contributes theoretically by extending digital mindset and sensemaking frameworks to the AI-in-education context and offering a context-sensitive interpretation of trust theory. Practically, it highlights the need for autonomy-respecting, interpretive support structures and mindset-based training programs. These insights inform more effective AI adoption strategies in decentralized learning environments.
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        Value: 10.1177/21582440251396497
    Languages:
      – Text: English
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    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Technological Literacy
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      – SubjectFull: Teacher Attitudes
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      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Technology Integration
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      – SubjectFull: Self Efficacy
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      – SubjectFull: Trust (Psychology)
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      – SubjectFull: China
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