EGenAI-DBR: a design-based framework for responsible generative AI integration in higher education

Purpose This study introduces the EGenAI-DBR framework to help educators integrate GenAI responsibly in higher education. It addresses the tension between AI's pedagogical potential and academic integrity by providing educators with a practical, ethics-centred roadmap through the Conceptual–Strategic Integration Matrix (CSIM). Design/methodology/approach This study employs a Design-Based Research (DBR) methodology structured across five layers: conceptual, structural, operational, analytical and ethical. A two-phase mixed-methods design was used – a baseline survey (N = 128) via Prolific and a classroom pilot (n = 25) at a Middle Eastern institution using Google Suite and Moodle. Findings Students entered with high GenAI familiarity (M = 4.32/5). An intention–behaviour gap was identified: 62.5% recognised uncited AI use as misconduct, yet only 58.6% consistently cited it. Clear institutional guidelines were positively associated with student confidence (M = 4.37) and stronger intentions for continued responsible GenAI use (r = 0.34, p < 0.001). Originality/value EGenAI-DBR represents the first empirically tested synthesis of DBR, academic integrity and GenAI integration, operationalised through the CSIM. Unlike prohibition or detection-based approaches, it embeds ethical reasoning directly into course design, offering an institutionally accessible model applicable across diverse higher education contexts.