Embedding Generative AI into Systems Analysis and Design Curriculum: Framework, Case Study, and Cross-Campus Empirical Evidence
Mahmoud Elkhodr, Ergun Gide
TL;DR
This paper presents SAGE, a Structured AI-Guided Education framework designed to embed generative AI into the systems analysis and design curriculum to cultivate job-ready AI orchestration skills. Grounded in assessment-as-research and adapted from cybersecurity pedagogy, SAGE employs a two-stage progression and a three-dimension competency model (analytical deconstruction, contextual application, reflective synthesis) to train students to accept, modify, or reject AI contributions across requirements, modelling, and design tasks. Across four Australian campuses with 18 groups, results reveal intermediate-level orchestration patterns, a robust accessibility U-curve that is layer-dependent, and systematic AI translation weaknesses that students learn to identify and correct. The study offers actionable pedagogical guidelines, including structured documentation, continuous layer-specific scaffolding, and pre-translation scaffolds, to support inclusive, evidence-based AI integration in systems analysis education and beyond.
Abstract
Systems analysis students increasingly use Generative AI, yet current pedagogy lacks systematic approaches for teaching responsible AI orchestration that fosters critical thinking whilst meeting educational outcomes. Students risk accepting AI suggestions blindly or uncritically without assessing alignment with user needs or contextual appropriateness. SAGE (Structured AI-Guided Education) addresses this gap by embedding GenAI into curriculum design, training students when to accept, modify, or reject AI contributions. Implementation with 18 student groups across four Australian universities revealed how orchestration skills develop. Most groups (84\%) moved beyond passive acceptance, showing selective judgment, yet none proactively identified gaps overlooked by both human and AI analysis, indicating a competency ceiling. Students strong at explaining decisions also performed well at integrating sources, and those with deep domain understanding consistently considered accessibility considerations. Accessibility awareness proved fragile. When writing requirements, 85\% of groups explicitly considered elderly users and cultural needs. Notably, 55\% of groups struggled identifying when AI misclassified system boundaries (what belongs inside versus outside the system), 45\% missed data management errors (how information is stored and updated), and 55\% overlooked missing exception handling. Three implications emerge for educators: (i) require students to document why they accepted, modified, or rejected each AI suggestion, making reasoning explicit; (ii) embed accessibility prompts at each development stage because awareness collapses without continuous scaffolding; and (iii) have students create their own specifications before using AI, then compare versions, and anchor to research or standards to identify gaps.