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 |
| 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. |
|---|---|
| ISSN: | 1741-5659 1758-8510 |
| DOI: | 10.1108/ITSE-12-2024-0314 |
