Generative AI Agents for Instructional Co-design: A Sequential Agent-Based Approach Using a Low-Code/No-Code Platform

: Tolis, Dimitrios; Mystakidis, Stylianos; Hatzilygeroudis, Ioannis; Siozopoulos, Konstantinos

: Graf, Sabine; Markos, Angelos

: International Conference on Intelligent Tutoring Systems

Publisher: Springer Nature Switzerland

: 2025

Lecture Notes in Computer Science

: Generative Systems and Intelligent Tutoring Systems : 21st International Conference, ITS 2025, Alexandroupolis, Greece, June 2–6, 2025, Proceedings, Part I

: 15723

: 301

: 306

: 978-3-031-98280-4

: 978-3-031-98281-1

: 0302-9743

: 1611-3349

DOI: https://doi.org/10.1007/978-3-031-98281-1_25

: 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.