Orchestrating Scaffolding AI Agents: Design Principles for Mechanism-Specific Learner Support
Journal
Lecture Notes in Computer Science
Type
Book chapter
Date Issued
2026-05-31
Author(s)
Abstract
AI agents are increasingly adopted in higher education, yet current systems handle requests uniformly, promoting cognitive offloading over sustained skill development. With the rise of orchestrated multi-agent systems, learning goals can be targeted by specialized AI agents that each address a distinct scaffolding mechanism: conceptual, procedural, strategic, or metacognitive. This study follows a Design Science Research approach to derive design requirements from 32 student interviews, iteratively refine a prototype with 22 IS experts, 6 educators, and 34 students, and computationally analyze scaffold effectiveness. We contribute three design principles: (1) multi-agent coordination through a lead orchestrator that delegates to mechanism-specific sub-agents, (2) adaptive learner profiling that enables cross-session scaffolding fading, and (3) continuous institutional knowledge integration grounding scaffolds in verified course materials. Our effectiveness analysis reveals that delivery order predicted learning behavior, positioning orchestration as a key design concern for AI-assisted educational systems.
Language
English
Keywords
Multi-Agent-Systems
Generative AI
Scaffolding
Higher Education
Design Science Research
HSG Classification
contribution to education
Refereed
Yes
Book title
Design for Better Futures: Beyond the Science of the Artificial. Completed Research. DESRIST 2026. Lecture Notes in Computer Science, vol 16606. Springer
Publisher
Springer Nature Switzerland
Volume
vol 16606.
Number
Springer
Start page
191
End page
208
Pages
17
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
DESRIST_26_paper75.pdf
Size
2.35 MB
Format
Adobe PDF
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