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Generative AI in Health Education: A Curriculum Framework to Build Student Literacy, Academic Capability, and Assessment Design
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Generative AI in Health Education: A Curriculum Framework to Build Student Literacy, Academic Capability, and Assessment Design

Kellie Toohey A/Prof, Zach Quince, Kate Baltrotsky, Felicity Walker and Fiona Naumann
SSRN, Vol.Series Paper No. 35
Southern Cross University Scholarship of Learning and Teaching Research Paper Series, Elsevier
16/10/2025
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Abstract

Generative AI Curriculum Integration Health Education Higher Education
This practice paper presents the Faculty of Health at Southern Cross University's GenAI Curriculum Framework, developed to support responsible integration of GenAI across teaching, learning and assessment. The framework was developed from a scoping review to guide consistent, responsible integration of GenAI. Synthesising local policy and international scholarship, the framework defines core student and educator capabilities and assessment strategies: building student GenAI literacy (including large language model limitations and ethical use); promoting personalised, inclusive learning; fostering ethical decision making through projects and reflective portfolios; and cultivating critical evaluation skills (prompt design, bias and accuracy appraisal, and transparent AI disclosure). It prepares graduates for discipline-specific GenAI applications (clinical documentation, analytics) and supports educators via professional development and institutional policy. Assessment adopts a two-lane model combining AI-resistant tasks with AI-supported assessments within a programmatic approach to monitor competency. This approach prioritises authentic, higher-order evaluation of professional judgment and clinical reasoning that GenAI cannot replicate. The iterative framework aims to sustain academic integrity and workforce readiness in an AI-enabled health sector and calls for longitudinal research on educational outcomes and policy impact to refine implementation.

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