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Xueqiao Zhang; Chao Zhang; Jianwen Sun; Jun Xiao; Yi Yang; Yawei Luo – IEEE Transactions on Learning Technologies, 2025
Large language models (LLMs) have significantly advanced smart education in the artificial general intelligence era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: 1) customized generation: generating niche-targeted teaching content based on…
Descriptors: Artificial Intelligence, Instructional Design, Technology Uses in Education, Cognitive Ability
Aidan Doyle; Pragnya Sridhar; Arav Agarwal; Jaromir Savelka; Majd Sakr – Journal of Computer Assisted Learning, 2025
Background: In computing education, educators are constantly faced with the challenge of developing new curricula, including learning objectives (LOs), while ensuring that existing courses remain relevant. Large language models (LLMs) were shown to successfully generate a wide spectrum of natural language artefacts in computing education.…
Descriptors: Computer Science Education, Artificial Intelligence, Learning Objectives, Curriculum Development
Andrea Hicks; Wissam Kontar – Journal of Civil Engineering Education, 2024
Engineering programs must produce graduates who are able to consider multicriteria decisions including ethical implications during engineering practice. Teaching students in a meaningful manner to consider these multifaceted decisions was investigated through the usage of disasters and primary coverage of infrastructure disasters. Students…
Descriptors: Engineering Education, Emergency Programs, Natural Disasters, Ethics
Tianjiao Zhao; Jiayi Jia; Tianfei Zhu; Junyu Yang – International Journal of Technology and Design Education, 2024
Designers are always pursuing design with suitable emotions. Effective emotional fusion not only produces a good user experience but also extends the product lifecycle. The decoding of design emotion and the use of design emotion language should run through the entire design process. In this study, we propose a new emotion-embedded design flow…
Descriptors: Psychological Patterns, Design, Artificial Intelligence, Databases
Ronak R. Mohanty; Peter Selly; Lindsey Brenner; Shantanu Vyas; Cassidy R. Nelson; Jason B. Moats; Joseph L. Gabbard; Ranjana K. Mehta – IEEE Transactions on Learning Technologies, 2025
Immersive extended reality (XR) technologies, including augmented reality (AR), virtual reality, and mixed reality, are transforming the landscape of education and training through experiences that promote skill acquisition and enhance memory retention. These technologies have notably improved decision making and situational awareness in public…
Descriptors: Technology Integration, Artificial Intelligence, Safety Education, Instructional Design
Chahna Gonsalves – Journal of Learning Development in Higher Education, 2025
Generative AI (GenAI) is transforming higher education. It has already challenged the validity of traditional assessment methods and revealed concerns about the authenticity and reliability of conventional approaches. This opinion piece proposes an expanded theoretical framework for contextual learning, incorporating practical, situational,…
Descriptors: Artificial Intelligence, Higher Education, Evaluation Methods, Technology Uses in Education
Gerti Pishtari; María Jesús Rodríguez-Triana; Luis P. Prieto; Adolfo Ruiz-Calleja; Terje Väljataga – Journal of Computer Assisted Learning, 2024
Background: In the field of Learning Design, it is common that researchers analyse manually design artefacts created by practitioners, using pedagogically-grounded approaches (e.g., Bloom's Taxonomy), both to understand and later to support practitioners' design practices. Automatizing these high-level pedagogically-grounded analyses would enable…
Descriptors: Electronic Learning, Instructional Design, Active Learning, Inquiry
Wenqiang Dai; Qiongyao Liu; Yuemin Gao; Wenjing Liu; Shaojuan Ouyang – International Journal of Web-Based Learning and Teaching Technologies, 2025
The traditional curriculum design methods suffer from issues like outdated content and limited instructional approaches. To address these, this article proposes an optimized curriculum design system that integrates CAD and neural network models. This system enables intelligent curriculum content generation, introduces CAD-assisted instructional…
Descriptors: Curriculum Design, Computer Assisted Design, Artificial Intelligence, Computer Uses in Education
Miroslava Petrova; Claas Kuhnen – Design and Technology Education, 2025
Integration of Artificial Intelligence (AI) in the design process is a growing area of research interest. Three years after its public launch in 2022, AI has already established itself as the most disruptive tool revolutionizing how designers conceptualize, iterate and innovate. As AI technologies continue to evolve, it is pertinent that design…
Descriptors: Artificial Intelligence, Computer Software, International Cooperation, Institutional Cooperation
Giulia Cosentino; Jacqueline Anton; Kshitij Sharma; Mirko Gelsomini; Michail Giannakos; Dor Abrahamson – British Journal of Educational Technology, 2025
As AI increasingly enters classrooms, educational designers have begun investigating students' learning processes vis-à-vis simultaneous feedback from active sources--AI and the teacher. Nevertheless, there is a need to delve into a more comprehensive understanding of the orchestration of interactions between teachers and AI systems in educational…
Descriptors: Artificial Intelligence, Learning Processes, Instructional Design, Design
Amanda Konet; Ian Thomas; Gerald Gartlehner; Leila Kahwati; Rainer Hilscher; Shannon Kugley; Karen Crotty; Meera Viswanathan; Robert Chew – Research Synthesis Methods, 2024
Accurate data extraction is a key component of evidence synthesis and critical to valid results. The advent of publicly available large language models (LLMs) has generated interest in these tools for evidence synthesis and created uncertainty about the choice of LLM. We compare the performance of two widely available LLMs (Claude 2 and GPT-4) for…
Descriptors: Data Collection, Artificial Intelligence, Computer Software, Computer System Design
Emmanuel Dumbuya – Online Submission, 2025
Artificial Intelligence (AI) is revolutionizing industries, yet its integration into education remains underutilized. This paper advocates for embedding AI tools in curriculum design to personalize learning experiences, address diverse student needs, and equip learners with future-ready skills. By leveraging AI's capabilities, educators can create…
Descriptors: Artificial Intelligence, Curriculum Design, Individualized Instruction, Technology Uses in Education
Jeffrey A. Greene; Helen Crompton – TechTrends: Linking Research and Practice to Improve Learning, 2025
The increasing ubiquity of digital technologies in the twenty-first century has led to calls for education reform focused on digital literacy, but what exactly does this term mean? The concept of digital literacy has evolved much since its evolution from media and new literacies scholarship, resulting in a myriad of definitions. Previous attempts…
Descriptors: Digital Literacy, Definitions, Educational Policy, Instructional Design
Stefanie Panke – Journal of Teacher Education, 2025
The autoethnographic study investigates the transformative impact of generative AI on educational research, instructional design, and teaching practices over a 5-month period (May-October 2024). By integrating AI tools into every phase of the research process, the study examines AI's role as both a research partner and a subject of inquiry. Field…
Descriptors: Ethnography, Artificial Intelligence, Computer Software, Instructional Design
Davy Tsz Kit Ng; Jiahong Su; Samuel Kai Wah Chu – Education and Information Technologies, 2024
Artificial intelligence (AI) education has gained popularity, and educators are developing activities to enhance students' AI literacy and promote collaboration in problem-solving. While current approaches using simulations and games can improve students' AI knowledge, they may not adequately prepare them for higher-level cognitive tasks. Only a…
Descriptors: Artificial Intelligence, Technological Literacy, Secondary School Students, Case Studies