Navigating the AI contradiction in higher education: Conceptual framework for student learning, authentic assessment, graduate employability and institutional governance

Abstract

The rise of Generative Artificial Intelligence (GenAI) presents new challenges to conventional notions of assessment validity, institutional governance and graduate employability. The central problem Higher Education Institutions (HEIs) face in this conceptual paper is not that students use GenAI to complete syllabus required assessments, but that syllabus required assessments need to evolve into AI literacy education to maintain students’ critical thinking when using GenAI. In doing so, students retain and expand their critical thinking capabilities with academic integrity upon graduation.   This conceptual paper attempts to resolve this assessment validity crisis by examining the underlying conflicts among students, employers and HEIs. To achieve this, the paper utilizes a conceptual literature synthesis incorporating quantitative, qualitative and mixed-methods research traditions. As an outcome of this study, this paper proposes a two-pillar conceptual framework consisting of the Three-Tier Pedagogical Intervention Pipeline and the Field- embedded Empirical Project Based Learning (PBL) model. Within this framework, student project curricula are developed across three empirical methodological pathways: Quantitative (utilizing SPSS, SmartPLS and AMOS), Qualitative (incorporating Focus Group Discussions and interviews) and Mixed-Methods as a mechanism to restore assessment authenticity, embed AI literacy and develop workplace readiness. This paper provides strategic recommendations for HEI administrators, accreditation agencies and educators to redesign assessment frameworks applicable to higher education using human-centric, process-oriented models.