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Quantifying SDG curriculum alignment using a GenAI–assisted, expert validated mapping approach
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DOI：10.1108/ijshe-12-2025-1514.png)
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Abstract
En 中文
<jats:sec>
<jats:title>Purpose</jats:title>
<jats:p>This study aims to develop and apply a generative artificial intelligence (genAI)-assisted approach for mapping the alignment of university curricula with the United Nations Sustainable Development Goals (SDGs). It addresses the challenge of systematically quantifying curricular contributions to sustainability education.</jats:p>
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<jats:title>Design/methodology/approach</jats:title>
<jats:p>Using Achtenhagen’s (2012) curriculum-instruction-assessment triad to structure analysis across learning outcomes, teaching activities and assessment, and Boud and Soler’s (2016) sustainable assessment to guide evaluation of assessment relevance, 241 undergraduate course profiles from 13 programs at an Australian university were analysed. A genAI model assigned SDG relevance scores (0–1 scale) based on course learning outcomes, assessment tasks and summaries, which were subsequently reviewed and calibrated by a panel of disciplinary academic experts (n = 8).</jats:p>
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<jats:title>Findings</jats:title>
<jats:p>Results reveal clear disciplinary patterns: Health and Education programs strongly align with SDGs 3 and 4, while Science programs emphasise SDGs 9 and 11. Business programs show broader but less intense engagement with specific SDGs. Notable gaps were found for SDG 5, 6, 14 and 15. AI-generated scores showed high consistency with expert revisions, demonstrating the potential of genAI for efficient SDG curriculum mapping.</jats:p>
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<jats:title>Originality/value</jats:title>
<jats:p>This study introduces a quantitative, genAI-assisted approach to SDG curriculum mapping that is both transferable and scalable. By combining automated analysis with expert oversight, the approach offers a transparent and efficient means of benchmarking and improving sustainability integration within higher education curricula. While demonstrated within a single institutional context, the framework is designed for adaptation across settings, with expert validation mitigating potential biases associated with genAI-driven analysis.</jats:p>
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