Cultural and Digital Readiness for AI-Integrated Higher Education: A C-TAM-Informed Curriculum Design Framework

Abstract

Generative artificial intelligence (GenAI) requires higher education institutions to redesign curricula, assessments, and student support rather than merely authorize or prohibit a new tool. This article develops a Cultural-Technology Acceptance Model (C-TAM) as a theory-building curriculum framework. It integrates current studies of university students’ GenAI use with established technology-acceptance research, cross-cultural scholarship, AI-literacy frameworks, backward design, constructive alignment, and assessment reform. The revision explicitly limits the role of pre-GenAI evidence. Programme for International Student Assessment (PISA) 2018 and World Values Survey (WVS) Wave 7 indicators are treated as contextual evidence about the digital and cultural readiness pipeline entering higher education; they are not treated as measures of GenAI acceptance, and no cross-national structural equation model is estimated from them. The synthesis identifies five mechanisms that curriculum designers should anticipate: perceived usefulness, perceived ease of use and AI competence, attitudes shaped by trust and ethical risk, social and institutional norms, and culturally patterned expectations about uncertainty and authority. These mechanisms are translated through backward design into aligned learning outcomes, assessment evidence, pedagogy, and support. An AI-Ready Curriculum Reconstruction Matrix specifies adaptations for students who combine high uncertainty avoidance with high power-distance expectations, including explicit permissions, low-stakes rehearsal, modelled verification, multiple participation routes, process evidence, and equitable access. The article contributes a bounded and testable framework for culturally responsive AI integration while providing a transparent protocol for future direct, multilevel, cross-cultural validation.