Socio-technical framework for AI-resilient assessment in information systems education
Author Identifier (ORCID)
Abstract
Generative Artificial Intelligence (GenAI) is challenging traditional assumptions about authorship, originality, and the demonstration of competence in higher education. In Information Systems (IS) education, AI-assisted work is increasingly embedded in both learning processes and professional practice. Institutional responses have largely focused on detection tools and academic integrity policies; however, these approaches remain reactive and technologically unstable. This paper argues that the challenge is architectural rather than behavioural. Drawing on programmatic assessment literature and socio-technical systems theory, the study reconceptualises assessment as a system of evidence production, interpretation, and judgment. The paper proposes a three-layer framework for AI-resilient assessment governance comprising: assessment design archetypes that represent different modes of capability demonstration, an AI vulnerability exposure framework that analyses structural susceptibility to AI-supported substitution, and a composite capability assurance model that integrates multiple forms of evidence. The framework contributes a conceptual foundation for redesigning assessment in AI-augmented IS education contexts.
Keywords
AI-resilient assessment, assessment design, generative artificial intelligence (GenAI), information systems education, socio-technical systems
Document Type
Conference Proceeding
Date of Publication
1-1-2026
E-ISSN
26896354
Volume
PartF1
Publication Title
PACIS 2026 Proceedings
Publisher
Association for Information Systems
School
School of Business and Law
Copyright
subscription content
Recommended Citation
Madugoda Gunaratnege, S. (2026). Socio-technical framework for AI-resilient assessment in information systems education. In PACIS 2026 Proceedings. Association for Information Systems. https://doi.org/