EGenAI-DBR: a design-based framework for responsible generative AI integration in higher education
TL;DR
The EGenAI-DBR framework represents the first empirically tested synthesis of DBR, academic integrity and GenAI integration, operationalised through the CSIM, offering an institutionally accessible model applicable across diverse higher education contexts.
Abstract
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). 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. 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). 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.