Paving paths of inquiry-led AI integration for future-ready learners

Abstract

This chapter presents an inquiry-led model for integrating Generative AI (genAI) into information systems education that develops students’ technical competence alongside critical judgement, validation rigour, and collaborative practice. Drawing on two course iterations across undergraduate and postgraduate cohorts, I trace a learning arc from dependency to collaboration to integration, showing how scaffolded prompting, systematic verification, and team-based workflows position AI as a partner rather than a proxy. The design combines early AI-literacy foundations (prompt engineering and validation protocols), structured practice through Google Colab notebooks (manual first, then AI-assisted) and assessments that evidence human oversight, error-checking, and reflective justification. A reflective practitioner methodology is used, supported by student observations, to surface five actionable insights: (1) prompting as a form of technical literacy, (2) validation as a core competency, (3) AI as facilitator of learning, (4) the scaffolding–independence paradox, and (5) AI’s role in team learning. I map these to the University of Queensland graduate attributes and discuss tensions (deskilling risks, integrity expectations, uneven readiness) with pragmatic mitigations. The chapter concludes by offering practical resources including prompting guidelines, a validation checklist, and a simplified rubric for responsible AI use and identifies future directions such as earlier scaffolding, stronger alignment between assessment and AI literacies, and mid-course monitoring. Together, these strategies aim to support learners worldwide to build confidence, act ethically, and develop the skills needed for success in an AI-enabled future.

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