Introduction to the Special Section on Generative Artificial Intelligence (GenAI) in Learning Design: An Editorial with a Commentary
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Abstract
Generative Artificial Intelligence (GenAI) has rapidly become embedded in the professional practices of educators, learning designers, and learners, changing expectations around productivity, creativity, authorship, and expertise. This Editorial introduces the International Journal of Designs for Learning (IJDL) Special Section on Generative Artificial Intelligence in Learning Design, which features sixteen peer-reviewed Design Cases documenting how GenAI is being designed with, constrained, and interrogated across diverse learning contexts. These contexts include higher education, K–12, informal learning environments, professional learning, civic education, expressive arts, engineering, and open educational resource development. Rather than positioning GenAI as a miraculous or autonomous solution, the Design Cases foreground it as a facilitation tool, creative material, and site for critical inquiry within human-centered design practice. Across cases, GenAI functions as a co-designer, co-writer, co-critic, and provocation—supporting ideation, drafting, reflection, and iterative design—while remaining dependent on human judgment and contextual expertise. Authors explicitly address ethical tensions related to authorship, accuracy, bias, accessibility, equity, and rapid technological change. This Editorial synthesizes key themes across the collection and argues that GenAI does not replace learning designers. Instead, it makes their design work more visible, more complex, and more essential than ever.
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Victoria Abramenka-Lachheb, Boise State University
Victoria Abramenka-Lachheb, Ph.D., is a learning design scholar and educator. She is a Clinical Assistant Professor at Boise State University’s Organizational and Workplace Learning (OWPL) program. Her research focuses on the intersection of instructional/learning design, human-computer interaction, and design ethics, aiming to make technology-enhanced learning more effective and inclusive by leveraging advanced technologies like GenAI and XR. Her ongoing scholarly projects focus on design ethics, value-sensitive design, and inclusive design related to the use of advanced technologies such as GenAI and XR.
Javier Leung, University of Missouri
Javier Leung, Ph.D., is an EdHub Instructional Designer at the University of Missouri. His research focuses on data science and natural language processing methods to examine the usability of learning designs and the extraction of knowledge structures from unstructured and web analytics data sources. More about his professional background can be accessed at javierleung.com
Rajagopal Sankaranarayanan, University of Texas at Austin
Rajagopal Sankaranarayanan, Ph.D., is a Research Associate and a Lecturer at the University of Texas at Austin. His applied research interests focus on Institutional Assessment for Student Success, Strategic planning, Learning Analytics, and Instructional Design in diverse contexts.
Olgun Sadik, Indiana University Bloomington
Olgun Sadik, Ph.D., is a senior lecturer in the Intelligent Systems Engineering Department at Indiana University Bloomington. He is interested in exploring emerging learning technologies and their impact on teaching and students’ learning in computing and engineering education.
Ahmed Lachheb, University of Kansas
Ahmed Lachheb, Ph.D., is a design scholar, practitioner, and educator. He is an Associate Teaching Professor of Learning Design at the University of Kansas School of Education and Human Sciences, Department of Curriculum and Teaching. He is the Managing Executive Editor of the International Journal of Designs for Learning (IJDL) and a member of the Advisory Board of the AECT Center of Excellence in Publishing. His research interests include design practice, designers’ design knowledge and actions, design theory, and design pedagogy.

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