Contextual assessment design in the age of generative AI

Generative AI (GenAI) is transforming higher education. It has already challenged the validity of traditional assessment methods and revealed concerns about the authenticity and reliability of conventional approaches. This opinion piece proposes an expanded theoretical framework for contextual learning, incorporating practical, situational, experiential, interdisciplinary, and ethical dimensions to address the limitations of authentic assessment in the face of GenAI’s rapid advancements. Whilst the framework is primarily theoretical and not yet empirically validated, it offers a new way of thinking about assessment design, providing educators with a vocabulary and conceptual tools to create more resilient and effective assessments. Learning Developers, who often lead initiatives, engage in collaborative efforts, and support both educators and students, are particularly well-positioned to implement and advocate for these strategies. By integrating diverse contexts into assessment design, this framework aims to promote higher-order thinking and real-world applications, making assessments more adaptable to the complexities of modern professional environments. Future research should explore the empirical validation of this framework across disciplines to ensure its broader applicability and impact on student engagement, learning outcomes, and academic integrity.