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CC BY V4.0, Open Access
CC BY V4.0, Open
Conference paper
Towards scalable curriculum mapping: Comparing human and GenAI alignment of CLOs to professional standards
2025: ASCILITE 2025 Conference Proceedings, pp.374-379
ASCILITE Publications
2025 ASCILITE Future-Focused: Educating in an Era of Continuous Change, 42nd (Adelaide, Australia, 30/11/2025–04/12/2025)
28/11/2025
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Abstract
Curriculum mapping is a critical component of accreditation and continuous improvement in education. However, aligning Course Learning Outcomes (CLOs) to professional standards such as Engineers Australia’s Stage 1 Competency Standard remains time-consuming and labour-intensive. This study evaluates the potential of generative AI (GenAI) to support curriculum mapping by comparing automated outputs with expert human judgement. A stratified sample of 141 (10%) first-year CLOs from a national dataset was analysed using both manual review and GenAI (ChatGPT 4o). Each CLO was mapped to the 16 EA Stage 1 Competencies, and outcomes were classified as Match, Manual Only, GenAI Only, or Neither. Overall agreement was 81.2% with particularly strong alignment in professional and personal attributes. Mismatches were most common in technical competencies, where GenAI over-mapped based on keywords or under-mapped due to limited contextual understanding. These findings suggest GenAI can support curriculum review at scale but requires expert oversight for nuanced or discipline-specific outcomes. The work in progress study contributes to the growing literature on AI in education and offers practical insights into hybrid approaches for accreditation and curriculum design.
Details
- Title
- Towards scalable curriculum mapping: Comparing human and GenAI alignment of CLOs to professional standards
- Creators
- Zachery Quince - Southern Cross University
- Publication Details
- 2025: ASCILITE 2025 Conference Proceedings, pp.374-379
- Conference
- 2025 ASCILITE Future-Focused: Educating in an Era of Continuous Change, 42nd (Adelaide, Australia, 30/11/2025–04/12/2025)
- Publisher
- ASCILITE Publications
- Number of pages
- 5
- Identifiers
- 991013334228102368
- Copyright
- © Quince, Z. 2025.
- Academic Unit
- Centre for Teaching and Learning; Faculty of Science and Engineering
- Language
- English
- Resource Type
- Conference paper
- Local Fields
- Evidence Based Practice - SoLT