Preprint
Embedding GenAI Literacy in Academic Practice: Design of a Staff Learning and Teaching Module
SSRN, Vol.Series Paper No. 34
Southern Cross University Scholarship of Learning and Teaching Research Paper Series, Elsevier
13/10/2025
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Abstract
Generative artificial intelligence (GenAI) is reshaping higher education, presenting both opportunities for innovation and challenges to academic integrity, authorship, and assessment design. While policy frameworks at national and institutional levels increasingly emphasise constructive engagement, the impact of the framework depends on the capacity of staff to interpret and apply them in day-today teaching. This paper presents the development of the GenAI in Learning and Teaching Staff Module at Southern Cross University as a case study of translating policy into practice. The module was designed to build staff capability through a scaffolded progression from foundational awareness to applied practice, combining conceptual input, hands-on activities, and ethical reflection. Its six topics address GenAI literacy, student engagement, policy integration, assessment redesign, reflective learning, and evaluative judgment. Early evaluations highlight increased staff confidence, discipline-specific adaptations, and cross-institutional knowledge exchange. The paper also critically explores tensions in embedding GenAI literacy, including equity of access, sustainability, authorship dilemmas, and ethical concerns, before considering the future trajectory of staff development. The findings suggest that structured, reflective, and policy-aligned professional learning offers a replicable model for building institutional and sector-wide capacity to engage with GenAI responsibly.
Details
- Title
- Embedding GenAI Literacy in Academic Practice: Design of a Staff Learning and Teaching Module
- Creators
- Zach Quince - Southern Cross University
- Publication Details
- SSRN, Vol.Series Paper No. 34
- Series
- Southern Cross University Scholarship of Learning and Teaching Research Paper Series
- Publisher
- Elsevier
- Identifiers
- 991013337290602368
- Academic Unit
- Centre for Teaching and Learning; Faculty of Science and Engineering
- Language
- English
- Resource Type
- Preprint
- Local Fields
- Original Research - SoLT