Work

Guidelines for learning design and assessment for generative artificial intelligence-integrated education: a unified view

Year: 2025

Type: article

Abstract: Purpose The rapid advancement and widespread adoption of generative artificial intelligence (GenAI) in education have significantly impacted learning, teaching and assessment practices. This development has raised critical questions about necessary changes to learning design and traditional assessment methods for a society where GenAI becomes embedded in both learning and work environments. This paper aims to investigate the extent of global consensus on learning design and assessment for GenAI-integrated learning environments. Design/methodology/approach This study’s policy analysis approach follows Nguyen et al. (2023) by mapping and analysing current policies and guidelines from intergovernmental organisations. This study conducts a comprehensive review of policies and guidelines relevant to learning design and assessment for GenAI-integrated education, highlighting key competencies, ethical principles and implementation guidelines. Findings This paper presents an integrated perspective on the key skills and competencies needed for learning with GenAI, alongside strategies for designing effective GenAI-integrated learning experiences. The study findings highlight the need to rethink conventional assessment goals and methods to capture the full range of learning gains enabled by GenAI. Originality/value While recent guidelines have begun to address GenAI’s role in education, there remains ongoing debate over the foundational principles needed to design meaningful learning and assessment in this new context. The proposed integrated framework offers practical guidance for educators and policymakers while also laying the groundwork for future research on the pedagogical and systemic impacts of GenAI integration.

Source: Information and Learning Sciences

DOI: 10.1108/ils-11-2024-0148

Authors: Andy Nguyen, Anh Thi Duong, Diep Thi Bich Nguyen, Van Thi Thanh Lai, Belle Dang