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EGenAI-DBR: a design-based framework for responsible generative AI integration in higher education
Journal article   Open access   Peer reviewed

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

Shahrzad Saremi, Mansooreh Mirzaei, Ahmad Rasti, Maryam Nooraei Abadeh, Marzieh Varposhti, Rania Shibl, Hassan Ahmed, Shaden Al Dkheel, Katie Wang and Declan Humphreys
Education Innovations: Systems and Future Learning, Vol.1(1), pp.341-373
2026
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Published Version Open Access CC BY V4.0

Abstract

Generative artificial intelligence (GenAI) Design-based research (DBR) Academic integrity Ethical integration framework Higher education Writing pedagogy
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.

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