Special Session 5: Automated Instructional Design in the Age of AI and the Application of UML in Teaching and Learning
Brief Description:
This session focuses on paradigm innovation in Automated Instructional Design (AID) against the backdrop of the artificial intelligence era.
It aims to explore how formal modeling methods, such as the Unified Modeling Language (UML) and its educational extensions, can be integrated
with Generative Artificial Intelligence (GenAI) technologies to construct a new generation of intelligent collaborative instructional design
frameworks. The session will examine how UML serves as a visual design skeleton that works in tandem with Generative AI to address the core
challenges of “description standardization” and “executable solution development” in the implementation of Automated Instructional Design.
Specific topics include:
- Construction of UML-based instructional design meta-models
- Intelligent transformation between UML models and natural language/executable code
- Cognitive-supporting applications of UML in teaching and learning
- AI-powered UML modeling tools, explainable teaching systems, and integration of intelligent tutoring systems
We look forward to bringing together scholars from educational technology, learning sciences, software engineering, and other related fields.
Through this interdisciplinary dialogue, we hope to promote the broader application of UML in teaching and learning in the intelligent era.
This will, in turn, drive the evolution of instructional design towards a new paradigm characterized by model-driven approaches, dynamic adaptability,
and human-AI collaboration, providing theoretical foundations, practical tools, and pathways for the development of intelligent education.
Session Organizer
Assoc. Prof. Zeng Ling, South China Agricultural University, China
The topics of interest include, but are not limited to:
▪ Theoretical
Reconstruction and Domain Adaptation of UML
as an Meta-model of Instructional Design
▪ Intelligent Transformation from Natural
Language Descriptions of Instructional
Design to Formal UML Models
▪ Generative Content Deep Customization
Based on UML Model Constraints
▪ Construction of UML Design Pattern
Libraries for Complex Instructional Models
(e.g., PBL) and AI-assisted Instantiation
▪ Verification, Simulation, and Executable
Transformation Approaches for UML-based
Instructional Design Models
▪ Research on Workflow Innovation in
Collaborative Design among Teachers, UML,
and AI, as well as the Development of New
Competencies for Teachers
▪ The Construction of Interdisciplinary
Research Communities and Prospects for the
Development of an Open Ecosystem
▪ Applied research on UML as a cognitive aid
in promoting students' learning and the
development of systematic thinking skills
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Introduction of Session Organizer
Assoc. Prof. Guangtao Xu
Hangzhou Normal University, China
Bio: Dr. Guangtao Xu is an Associate Professor, Master’s
Supervisor, and Director of the Department of Educational Technology
at Jinghengyi School of Education, Hangzhou Normal University. He
also serves as a Research Fellow at the Institute for Educational
Modernization.His primary research interests include Artificial
Intelligence in Education, Learning Sciences, and Technological
Design for Learning. He has published over 50 academic papers and
authored two books. Dr. Xu has received more than 20 teaching and
research awards at various levels. He is the lead instructor of a
national-level first-class course and has guided students to win
over 50 national and provincial awards, including in the "Challenge
Cup" and National Computer Design Competition.He has led
sub-projects under China's National Key R&D Program and projects
under the National Education Science Planning. In 2014, he was
awarded the Second Prize of the Zhejiang Provincial Science and
Technology Progress Award. In 2021, he received the Second Prize of
the National Education Science Outstanding Research Achievement
Award, and in 2025, the Second Prize of the Zhejiang Provincial
Basic Education Teaching Achievement Award.
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