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Generative AI Agents for Instructional Co-design: A Sequential Agent-Based Approach Using a Low-Code/No-Code Platform
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DOI：10.1007/978-3-031-98281-1_25.png)
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Abstract
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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.
Keywords:
Generative AI
AI Agents
Low-Code/No-Code
Instructional Design
Citizen Development
Human-In-The-Loop
AI in Education
Flowise
LangChain
LangGraph
Fact-checking
Bias detection
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