Bibliographic Details
| Title: |
ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI-Assisted Instructional Design |
| Authors: |
Li, Hongming, Fang, Yizirui, Zhang, Shan, Lee, Seiyon M., Wang, Yiming, Trexler, Mark, Botelho, Anthony F. |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computers and Society, K.3.1, I.2.6 |
| More Details: |
Integrating Large Language Models (LLMs) in educational technology presents unprecedented opportunities to improve instructional design (ID), yet existing approaches often prioritize automation over pedagogical rigor and human agency. This paper introduces ARCHED (AI for Responsible, Collaborative, Human-centered Education Instructional Design), a structured multi-stage framework that ensures human educators remain central in the design process while leveraging AI capabilities. Unlike traditional AI-generated instructional materials that lack transparency, ARCHED employs a cascaded workflow aligned with Bloom's taxonomy. The framework integrates specialized AI agents - one generating diverse pedagogical options and another evaluating alignment with learning objectives - while maintaining educators as primary decision-makers. This approach addresses key limitations in current AI-assisted instructional design, ensuring transparency, pedagogical foundation, and meaningful human agency. Empirical evaluations demonstrate that ARCHED enhances instructional design quality while preserving educator oversight, marking a step forward in responsible AI integration in education.
Comment: Accepted to the iRAISE Workshop at AAAI 2025. To be published in PMLR Volume 273 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2503.08931 |
| Accession Number: |
edsarx.2503.08931 |
| Database: |
arXiv |
