A4 Refereed article in a conference publication
Generative AI Agents for Instructional Co-design: A Sequential Agent-Based Approach Using a Low-Code/No-Code Platform
Authors: Tolis, Dimitrios; Mystakidis, Stylianos; Hatzilygeroudis, Ioannis; Siozopoulos, Konstantinos
Editors: Graf, Sabine; Markos, Angelos
Conference name: International Conference on Intelligent Tutoring Systems
Publisher: Springer Nature Switzerland
Publication year: 2025
Journal: Lecture Notes in Computer Science
Book title : Generative Systems and Intelligent Tutoring Systems : 21st International Conference, ITS 2025, Alexandroupolis, Greece, June 2–6, 2025, Proceedings, Part I
Volume: 15723
First page : 301
Last page: 306
ISBN: 978-3-031-98280-4
eISBN: 978-3-031-98281-1
ISSN: 0302-9743
eISSN: 1611-3349
DOI: https://doi.org/10.1007/978-3-031-98281-1_25
Publication's open availability at the time of reporting: No Open Access
Publication channel's open availability : No Open Access publication channel
Web address : https://doi.org/10.1007/978-3-031-98281-1_25
This paper explores how a Low-Code/No-Code (LCNC) platform can be used by non-technical users, such as educators, to design and deploy a Sequential Agent-Based Generative AI System to facilitate instructional design. The system deploys an LLM-based sequential workflow of AI agents to support educators in the first three stages of the ADDIE instructional design model: Analysis, Design, and Development. It follows a co-design, Human-In-The-Loop (HITL) approach, where AI agents guide instructional designers on needs analysis, content validation and generation, while allowing user intervention. The system also explores the role of self-checking agents for fact-checking, bias detection, and instructional quality review, based on specific prompts. However, the identified potential remains theoretical, requiring empirical validation through user testing to assess usability, effectiveness, and adoption by non-IT users.
