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Developing a Multilevel Framework for AI Integration in Technical and Engineering Higher Education: Insights from Bibliometric Analysis and Ethnographic Research

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Title: Developing a Multilevel Framework for AI Integration in Technical and Engineering Higher Education: Insights from Bibliometric Analysis and Ethnographic Research
Language: English
Authors: Behzad Abbasnejad, Sahar Soltani, Foad Taghizadeh, Ali Zare
Source: Interactive Technology and Smart Education. 2026 23(1):49-79.
Availability: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
Peer Reviewed: Y
Page Count: 31
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Technology Uses in Education, Technology Integration, Educational Technology, Career and Technical Education, Engineering Education, Educational Research, Bibliometrics, Ethnography, Cultural Context, Readiness, Evaluation Methods, Foreign Countries, College Students
Geographic Terms: Australia
DOI: 10.1108/ITSE-12-2024-0314
ISSN: 1741-5659
1758-8510
Abstract: Purpose: The rapid integration of artificial intelligence (AI) in technical and engineering higher education presents both unprecedented opportunities and significant challenges. This study investigates how disciplinary characteristics, cultural contexts and institutional readiness influence AI implementation success in higher education. Design/methodology/approach: This study analyzes AI integration in higher education through a dual methodological approach combining systematic literature review and ethnographic observations across different institutes and then proposes a multilevel integration framework that addresses implementation challenges across institutional, departmental and course-specific levels. Findings: The study identifies three distinct approaches to AI integration in assessment: AI-inclusive assessment design, case study-based resistance strategies and hybrid examination models. The bibliometric analysis reveals ChatGPT as the dominant focus in current AI education research. The analysis identifies critical dialectical tensions that shape the integration of AI within higher education assessment practices -- namely, the Authenticity-Innovation Paradox (balancing authentic assessment with AI-driven innovation), the Competency-Augmentation Dilemma (preserving core skills amid AI support) and the Scale-Customization Conflict (reconciling scalable models with personalized learning needs). The findings suggest that effective AI integration necessitates a shift from isolated individual innovations to coordinated, institution-wide strategies, conceptualized as "structured flexibility frameworks," while acknowledging significant regional and cultural variations in implementation approaches worldwide. Originality/value: This study makes several significant contributions to AI integration in technical and engineering higher education. First, it develops a comprehensive multilevel framework that links institutional strategy, departmental approaches and classroom practices, addressing the complex dynamics of AI implementation. Through ethnographic observations across multiple Australian universities, the study provides empirical evidence of successful adaptation strategies, documenting real-world outcomes. Finally, the research establishes a theoretical foundation for understanding how disciplinary and cultural factors influence AI implementation success, providing insights into why certain approaches succeed or fail in different educational contexts. This work advances both theoretical understanding and practical strategies for AI integration in diverse higher education settings.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1505850
Database: ERIC
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  Data: Developing a Multilevel Framework for AI Integration in Technical and Engineering Higher Education: Insights from Bibliometric Analysis and Ethnographic Research
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Behzad+Abbasnejad%22">Behzad Abbasnejad</searchLink><br /><searchLink fieldCode="AR" term="%22Sahar+Soltani%22">Sahar Soltani</searchLink><br /><searchLink fieldCode="AR" term="%22Foad+Taghizadeh%22">Foad Taghizadeh</searchLink><br /><searchLink fieldCode="AR" term="%22Ali+Zare%22">Ali Zare</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Interactive+Technology+and+Smart+Education%22"><i>Interactive Technology and Smart Education</i></searchLink>. 2026 23(1):49-79.
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  Data: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
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  Data: Y
– Name: Pages
  Label: Page Count
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  Data: 31
– Name: DatePubCY
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  Data: 2026
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  Data: Journal Articles<br />Information Analyses<br />Reports - Research
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  Label: Education Level
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Career+and+Technical+Education%22">Career and Technical Education</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+Education%22">Engineering Education</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Research%22">Educational Research</searchLink><br /><searchLink fieldCode="DE" term="%22Bibliometrics%22">Bibliometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnography%22">Ethnography</searchLink><br /><searchLink fieldCode="DE" term="%22Cultural+Context%22">Cultural Context</searchLink><br /><searchLink fieldCode="DE" term="%22Readiness%22">Readiness</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink>
– Name: Subject
  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22Australia%22">Australia</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1108/ITSE-12-2024-0314
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1741-5659<br />1758-8510
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: The rapid integration of artificial intelligence (AI) in technical and engineering higher education presents both unprecedented opportunities and significant challenges. This study investigates how disciplinary characteristics, cultural contexts and institutional readiness influence AI implementation success in higher education. Design/methodology/approach: This study analyzes AI integration in higher education through a dual methodological approach combining systematic literature review and ethnographic observations across different institutes and then proposes a multilevel integration framework that addresses implementation challenges across institutional, departmental and course-specific levels. Findings: The study identifies three distinct approaches to AI integration in assessment: AI-inclusive assessment design, case study-based resistance strategies and hybrid examination models. The bibliometric analysis reveals ChatGPT as the dominant focus in current AI education research. The analysis identifies critical dialectical tensions that shape the integration of AI within higher education assessment practices -- namely, the Authenticity-Innovation Paradox (balancing authentic assessment with AI-driven innovation), the Competency-Augmentation Dilemma (preserving core skills amid AI support) and the Scale-Customization Conflict (reconciling scalable models with personalized learning needs). The findings suggest that effective AI integration necessitates a shift from isolated individual innovations to coordinated, institution-wide strategies, conceptualized as "structured flexibility frameworks," while acknowledging significant regional and cultural variations in implementation approaches worldwide. Originality/value: This study makes several significant contributions to AI integration in technical and engineering higher education. First, it develops a comprehensive multilevel framework that links institutional strategy, departmental approaches and classroom practices, addressing the complex dynamics of AI implementation. Through ethnographic observations across multiple Australian universities, the study provides empirical evidence of successful adaptation strategies, documenting real-world outcomes. Finally, the research establishes a theoretical foundation for understanding how disciplinary and cultural factors influence AI implementation success, providing insights into why certain approaches succeed or fail in different educational contexts. This work advances both theoretical understanding and practical strategies for AI integration in diverse higher education settings.
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  Data: 2026
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  Data: EJ1505850
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      – Type: doi
        Value: 10.1108/ITSE-12-2024-0314
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 31
        StartPage: 49
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Technology Integration
        Type: general
      – SubjectFull: Educational Technology
        Type: general
      – SubjectFull: Career and Technical Education
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      – SubjectFull: Engineering Education
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      – SubjectFull: Educational Research
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      – SubjectFull: Bibliometrics
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      – SubjectFull: Ethnography
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      – SubjectFull: Cultural Context
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      – SubjectFull: Readiness
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      – SubjectFull: Evaluation Methods
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      – SubjectFull: Foreign Countries
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      – SubjectFull: College Students
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      – SubjectFull: Australia
        Type: general
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      – TitleFull: Developing a Multilevel Framework for AI Integration in Technical and Engineering Higher Education: Insights from Bibliometric Analysis and Ethnographic Research
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              Y: 2026
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