Preprint
Adapting Assessment in the Age of Generative AI: The AAM-GenAI Framework (Practice Report)
SSRN Electronic Journal, Vol.Series Paper No.28
Southern Cross University Scholarship of Learning and Teaching Research Paper Series, Elsevier
30/07/2025
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Abstract
The rapid advancement of generative artificial intelligence (GenAI) presents both challenges and opportunities for higher education, particularly in the area of assessment. Traditional assessment practices are increasingly vulnerable to misuse, as students can generate sophisticated written and visual outputs with minimal effort. In response, Southern Cross University has developed the Assessment Adaptation Model-GenAI (AAM-GenAI), a practical, pedagogically grounded framework that guides educators in designing assessments resilient to GenAI use. This paper introduces and explores the seven components of the AAM-GenAI framework: Design, Analyse, Act, Inform, Educate, Check and Evaluate. It offers a holistic strategy for fostering academic integrity, supporting deep learning, and preparing students for ethical engagement with emerging technologies. Rather than relying on detection or punitive responses, the model emphasises proactive assessment design, transparent communication and capability building. Through discussion and supporting literature, this paper provides educators and institutions with actionable strategies to ensure assessment remains authentic, meaningful and future ready in a GenAI academic landscape.
Details
- Title
- Adapting Assessment in the Age of Generative AI: The AAM-GenAI Framework (Practice Report)
- Creators
- Zachery Quince - Southern Cross UniversityJoanne Munn - Southern Cross UniversityRuth Greenaway - Southern Cross University
- Publication Details
- SSRN Electronic Journal, Vol.Series Paper No.28
- Series
- Southern Cross University Scholarship of Learning and Teaching Research Paper Series
- Publisher
- Elsevier
- Identifiers
- 991013310928802368
- Academic Unit
- Centre for Teaching and Learning; Faculty of Science and Engineering; Faculty of Health
- Language
- English
- Resource Type
- Preprint
- Local Fields
- Original Research - SoLT