Structured AI-guided education (SAGE): A six-step pedagogy for AI orchestration and assessment assurance in higher education

Mahmoud Elkhodr; Ergun Gide; Mahmoud Elkhodr; Ergun Gide

doi:10.3934/steme.2026039

STEM Education

2026, Volume 6, Issue 5: 974-997. doi: 10.3934/steme.2026039

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Structured AI-guided education (SAGE): A six-step pedagogy for AI orchestration and assessment assurance in higher education

Mahmoud Elkhodr ^,,
Ergun Gide

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

Academic Editor: Stephen Xu

Received: 10 March 2026 Revised: 30 May 2026 Accepted: 01 July 2026 Published: 08 September 2026

Generative artificial intelligence (GenAI) has altered the conditions under which higher education assessment is expected to evidence student capability. Detection-based responses are unreliable, while prohibition alone does not prepare graduates for AI-integrated workplaces. This perspective presents Structured AI-Guided Education (SAGE), an empirically developed and iteratively evaluated six-step pedagogy for AI orchestration and assessment assurance. SAGE requires students to begin with a human baseline, evaluate AI output against authoritative disciplinary anchors, document accept/modify/reject decisions, refine the work, use AI as a bounded critic, reflect on the transferability of their judgment, and defend the reasoning behind the final artifact. Across the authors' reported program, SAGE has been directly implemented with more than 1500 students in undergraduate, postgraduate, and transnational contexts. The framework distinguishes AI permission from four observable task-level orchestration capabilities: Passive Acceptor, Selective Adapter, Balanced Integrator, and Critical Synthesiser. These profiles describe the quality of human judgment demonstrated rather than fixed learner identities. Defend is positioned as a proportionate, task-matched confirmation point within a distributed assurance architecture rather than as a single terminal examination or oral-only requirement. The paper provides the canonical account of the six-step cycle, its terminology, staged progression, required learner artifacts, comparative positioning, empirical evidence base, cross-disciplinary Defend formats, and evaluation blueprint. SAGE thereby connects institutional permission settings with disciplined AI use, visible student judgment, and proportionate assurance of individual attainment.
- generative AI,
- AI orchestration,
- assessment assurance,
- assessment design,
- academic integrity,
- AI literacy,
- higher education,
- SAGE
Citation: Mahmoud Elkhodr, Ergun Gide. Structured AI-guided education (SAGE): A six-step pedagogy for AI orchestration and assessment assurance in higher education[J]. STEM Education, 2026, 6(5): 974-997. doi: 10.3934/steme.2026039

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Abstract

Generative artificial intelligence (GenAI) has altered the conditions under which higher education assessment is expected to evidence student capability. Detection-based responses are unreliable, while prohibition alone does not prepare graduates for AI-integrated workplaces. This perspective presents Structured AI-Guided Education (SAGE), an empirically developed and iteratively evaluated six-step pedagogy for AI orchestration and assessment assurance. SAGE requires students to begin with a human baseline, evaluate AI output against authoritative disciplinary anchors, document accept/modify/reject decisions, refine the work, use AI as a bounded critic, reflect on the transferability of their judgment, and defend the reasoning behind the final artifact. Across the authors' reported program, SAGE has been directly implemented with more than 1500 students in undergraduate, postgraduate, and transnational contexts. The framework distinguishes AI permission from four observable task-level orchestration capabilities: Passive Acceptor, Selective Adapter, Balanced Integrator, and Critical Synthesiser. These profiles describe the quality of human judgment demonstrated rather than fixed learner identities. Defend is positioned as a proportionate, task-matched confirmation point within a distributed assurance architecture rather than as a single terminal examination or oral-only requirement. The paper provides the canonical account of the six-step cycle, its terminology, staged progression, required learner artifacts, comparative positioning, empirical evidence base, cross-disciplinary Defend formats, and evaluation blueprint. SAGE thereby connects institutional permission settings with disciplined AI use, visible student judgment, and proportionate assurance of individual attainment.

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