AI-Assisted Scaffolding for Instructor-in-the-Loop Community Chat

Location

Bent Corridor, Science Center

Document Type

Poster - Open Access

Start Date

5-1-2026 12:00 PM

End Date

5-1-2026 2:00 PM

Abstract

Asynchronous student-instructor communication channels, such as email and forum systems, are vital for supporting learning but frequently suffer from latency and scalability limitations. Learning Management Systems at universities (e.g. D2L, Canvas, Blackboard) do not usually have chat functionality. To include chats, instructors often rely on community platforms such as Discord, Slack, or Piazza. While these platforms reduce response delays and lower the barrier to student inquiry, they simultaneously increase instructor workload and create unsustainable pressure for immediate responses. Existing AI-based chat support systems attempt to address this scalability challenge through automated generation; however, these often result in impersonal interactions that erode the instructor’s pedagogical intent. In this ongoing work, we introduce an AI-assisted community chat framework designed for scaffolding pedagogy and Socratic methods. Using a Retrieval-Augmented Generation (RAG) system grounded in course materials provided by instructors, our approach employs constrained prompt structures to generate guided inquiries rather than direct answers. This helps preserve the instructor’s unique pedagogical tone. The framework supports multiple bot roles operating at varying levels of scaffolding and provides a centralized instructor-facing dashboard for document ingestion, real-time intervention, and pedagogical reflection. Its modular architecture is implemented through containerized services that isolate key components of the pipeline, including document storage and preprocessing, retrieval orchestration, and vector-based semantic search using systems such as MinIO for object storage and Qdrant for embedding retrieval. This setup enables instructors to retain full control over deployment, course content, system behavior, and student-facing interactions.

Keywords:

AI, Agent, Assistant, Education

Notes

Presenters: Nam Vu and Nam Hoang

Major

Computer Science; Mathematics

Project Mentor(s)

Linh Ngo, West Chester University
Md (Amir) Amiruzzaman, West Chester University

2026

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May 1st, 12:00 PM May 1st, 2:00 PM

AI-Assisted Scaffolding for Instructor-in-the-Loop Community Chat

Bent Corridor, Science Center

Asynchronous student-instructor communication channels, such as email and forum systems, are vital for supporting learning but frequently suffer from latency and scalability limitations. Learning Management Systems at universities (e.g. D2L, Canvas, Blackboard) do not usually have chat functionality. To include chats, instructors often rely on community platforms such as Discord, Slack, or Piazza. While these platforms reduce response delays and lower the barrier to student inquiry, they simultaneously increase instructor workload and create unsustainable pressure for immediate responses. Existing AI-based chat support systems attempt to address this scalability challenge through automated generation; however, these often result in impersonal interactions that erode the instructor’s pedagogical intent. In this ongoing work, we introduce an AI-assisted community chat framework designed for scaffolding pedagogy and Socratic methods. Using a Retrieval-Augmented Generation (RAG) system grounded in course materials provided by instructors, our approach employs constrained prompt structures to generate guided inquiries rather than direct answers. This helps preserve the instructor’s unique pedagogical tone. The framework supports multiple bot roles operating at varying levels of scaffolding and provides a centralized instructor-facing dashboard for document ingestion, real-time intervention, and pedagogical reflection. Its modular architecture is implemented through containerized services that isolate key components of the pipeline, including document storage and preprocessing, retrieval orchestration, and vector-based semantic search using systems such as MinIO for object storage and Qdrant for embedding retrieval. This setup enables instructors to retain full control over deployment, course content, system behavior, and student-facing interactions.