Diana Kozachek

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M.A.
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Kozachek
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Diana
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diana.kozachek@unisg.ch
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    Item type:Publication,
    Orchestrating Scaffolding AI Agents: Design Principles for Mechanism-Specific Learner Support
    (Springer Nature Switzerland, 2026-05-31)
    Kozachek, Diana  
    ;
    Janson, Andreas  
    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.
    Type:Book chapter
    Journal:Lecture Notes in Computer Science
    Volume:vol 16606.
    Issue:Springer
    URL:https://link.springer.com/chapter/10.1007/978-3-032-28313-9_11
    DOI:10.1007/978-3-032-28313-9_11
    URI:https://alexandria.unisg.ch/handle/20.500.14171/127284