AI leads, humans lead, or collaborate? Empirical findings and the SAGE roadmap for embedding GenAI in systems analysis and design education

Mahmoud Elkhodr; Ergun Gide; Mahmoud Elkhodr; Ergun Gide

doi:10.3934/steme.2026009

STEM Education

2026, Volume 6, Issue 2: 194-229. doi: 10.3934/steme.2026009

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Research article

AI leads, humans lead, or collaborate? Empirical findings and the SAGE roadmap for embedding GenAI in systems analysis and design education

Mahmoud Elkhodr ^,,
Ergun Gide

School of Engineering and Technology, Central Queensland University, Sydney, Australia

Academic Editor: Sasha Nikolic

Received: 07 October 2025 Revised: 09 February 2026 Accepted: 23 February 2026 Published: 05 March 2026

Generative AI orchestration competencies, the abilities to evaluate, adapt, and integrate AI outputs with human judgment, remain inadequately addressed by general AI literacy frameworks. This paper applies and extends the SAGE (Structured AI-Guided Education) framework to systems analysis and design education through three experimental assessments targeting requirements synthesis, formal modelling correction, and design evaluation. In the Brisbane baseline cohort ($n = 13$ groups), 84% demonstrated selective and justified AI use (Balanced Integrator or Selective Adapter), and no group reached expert-level synthesis; the subsequent cross-campus cohorts (n = 5 groups, yielding $N = 18$ template-compliant groups analysed overall out of 21 participating groups) corroborated these patterns. Students consistently recognised accessibility needs when working with user requirements and interface designs, but almost entirely neglected them when producing system architecture diagrams, suggesting that this competency depends on continuous prompting rather than transferring automatically between tasks. When AI was used to generate formal data flow diagrams from the students' specifications, the resulting errors were not random but clustered around system boundary classification, exception handling, and data lifecycle completeness. These findings carry implications for how educators structure AI collaboration in technical curricula, particularly regarding the sequencing of human and AI contributions, the need for stage-specific scaffolding, and the role of explicit justification in developing professional judgment.
- SAGE,
- generative AI (GenAI),
- systems analysis and design education,
- AI orchestration,
- accessibility,
- data flow diagrams (DFD)
Citation: Mahmoud Elkhodr, Ergun Gide. AI leads, humans lead, or collaborate? Empirical findings and the SAGE roadmap for embedding GenAI in systems analysis and design education[J]. STEM Education, 2026, 6(2): 194-229. doi: 10.3934/steme.2026009

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Abstract

Generative AI orchestration competencies, the abilities to evaluate, adapt, and integrate AI outputs with human judgment, remain inadequately addressed by general AI literacy frameworks. This paper applies and extends the SAGE (Structured AI-Guided Education) framework to systems analysis and design education through three experimental assessments targeting requirements synthesis, formal modelling correction, and design evaluation. In the Brisbane baseline cohort ($n = 13$ groups), 84% demonstrated selective and justified AI use (Balanced Integrator or Selective Adapter), and no group reached expert-level synthesis; the subsequent cross-campus cohorts (n = 5 groups, yielding $N = 18$ template-compliant groups analysed overall out of 21 participating groups) corroborated these patterns. Students consistently recognised accessibility needs when working with user requirements and interface designs, but almost entirely neglected them when producing system architecture diagrams, suggesting that this competency depends on continuous prompting rather than transferring automatically between tasks. When AI was used to generate formal data flow diagrams from the students' specifications, the resulting errors were not random but clustered around system boundary classification, exception handling, and data lifecycle completeness. These findings carry implications for how educators structure AI collaboration in technical curricula, particularly regarding the sequencing of human and AI contributions, the need for stage-specific scaffolding, and the role of explicit justification in developing professional judgment.

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Author's biography Dr. Mahmoud Elkhodr is a Senior Lecturer in the School of Engineering and Technology at Central Queensland University, Australia. His research spans cybersecurity, Internet of Things security and management, and AI-enabled digital systems, with a particular focus on privacy-preserving architectures, trustworthy AI, and evidence-based pedagogy for emerging technologies. His recent work advances structured approaches for integrating generative AI into higher education through measurable orchestration competencies, assessment instrumentation, and cross-campus implementation. He has authored and co-authored over 100 peer-reviewed publications and contributes to curriculum innovation that aligns professional practice with rigorous student capability development; Professor Ergun Gide is a full Professor of Information and Communication Technology at Central Queensland University, Australia, and a nationally recognised recipient of multiple teaching awards. His research and academic leadership encompass intelligent information systems, digital transformation, and technology-enhanced learning, with extensive experience in curriculum design and scalable work-integrated education. He has supervised more than 30 research higher degree graduates and has led institutional initiatives that strengthen learning quality and industry relevance across diverse student cohorts. In this study, he contributed supervisory mentorship and methodological oversight supporting cross-campus implementation and quality assurance

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