ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI Assisted Instructional Design

Hongming Li, Yizirui Fang, Shan Zhang, Seiyong M. Lee, Yiming Wang, Mark Trexler, Anthony F. Botelho
Proceedings of the Innovation and Responsibility in AI-Supported Education Workshop, PMLR 273:94-104, 2025.

Abstract

Integrating Large Language Models (LLMs) in educational technology reveals unprecedented opportunities to improve instructional design (ID), yet current approaches often prioritize automation over pedagogical rigor and human agency. This paper introduces ARCHED (AI for Responsible, Collaborative, Human-centered Education Instructional Design), a framework that implements a structured multi-stage workflow between educators and AI. Unlike existing tools that generate complete instructional materials autonomously, ARCHED cascades the development into distinct stages, from learning objective formulation to assessment design, each guided by Bloom’s taxonomy and enhanced by LLMs. This framework employs multiple specialized AI components that work in concert: one generating diverse pedagogical options, another evaluating their alignment with learning objectives while maintaining human educators as primary decision-makers. ARCHED addresses critical gaps in current AI-assisted instructional design regarding transparency, pedagogical foundation, and meaningful human agency through this approach. This research advances the responsible integration of AI in education by providing a concrete, theoretically grounded framework that prioritizes human expertise and educational accountability.

Cite this Paper

BibTeX
@InProceedings{pmlr-v273-li25a, title = {ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI Assisted Instructional Design}, author = {Li, Hongming and Fang, Yizirui and Zhang, Shan and Lee, Seiyong M. and Wang, Yiming and Trexler, Mark and Botelho, Anthony F.}, booktitle = {Proceedings of the Innovation and Responsibility in AI-Supported Education Workshop}, pages = {94--104}, year = {2025}, editor = {Wang, Zichao and Woodhead, Simon and Ananda, Muktha and Mallick, Debshila Basu and Sharpnack, James and Burstein, Jill}, volume = {273}, series = {Proceedings of Machine Learning Research}, month = {03 Mar}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v273/main/assets/li25a/li25a.pdf}, url = {https://proceedings.mlr.press/v273/li25a.html}, abstract = {Integrating Large Language Models (LLMs) in educational technology reveals unprecedented opportunities to improve instructional design (ID), yet current approaches often prioritize automation over pedagogical rigor and human agency. This paper introduces ARCHED (AI for Responsible, Collaborative, Human-centered Education Instructional Design), a framework that implements a structured multi-stage workflow between educators and AI. Unlike existing tools that generate complete instructional materials autonomously, ARCHED cascades the development into distinct stages, from learning objective formulation to assessment design, each guided by Bloom’s taxonomy and enhanced by LLMs. This framework employs multiple specialized AI components that work in concert: one generating diverse pedagogical options, another evaluating their alignment with learning objectives while maintaining human educators as primary decision-makers. ARCHED addresses critical gaps in current AI-assisted instructional design regarding transparency, pedagogical foundation, and meaningful human agency through this approach. This research advances the responsible integration of AI in education by providing a concrete, theoretically grounded framework that prioritizes human expertise and educational accountability.} }
Endnote
%0 Conference Paper %T ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI Assisted Instructional Design %A Hongming Li %A Yizirui Fang %A Shan Zhang %A Seiyong M. Lee %A Yiming Wang %A Mark Trexler %A Anthony F. Botelho %B Proceedings of the Innovation and Responsibility in AI-Supported Education Workshop %C Proceedings of Machine Learning Research %D 2025 %E Zichao Wang %E Simon Woodhead %E Muktha Ananda %E Debshila Basu Mallick %E James Sharpnack %E Jill Burstein %F pmlr-v273-li25a %I PMLR %P 94--104 %U https://proceedings.mlr.press/v273/li25a.html %V 273 %X Integrating Large Language Models (LLMs) in educational technology reveals unprecedented opportunities to improve instructional design (ID), yet current approaches often prioritize automation over pedagogical rigor and human agency. This paper introduces ARCHED (AI for Responsible, Collaborative, Human-centered Education Instructional Design), a framework that implements a structured multi-stage workflow between educators and AI. Unlike existing tools that generate complete instructional materials autonomously, ARCHED cascades the development into distinct stages, from learning objective formulation to assessment design, each guided by Bloom’s taxonomy and enhanced by LLMs. This framework employs multiple specialized AI components that work in concert: one generating diverse pedagogical options, another evaluating their alignment with learning objectives while maintaining human educators as primary decision-makers. ARCHED addresses critical gaps in current AI-assisted instructional design regarding transparency, pedagogical foundation, and meaningful human agency through this approach. This research advances the responsible integration of AI in education by providing a concrete, theoretically grounded framework that prioritizes human expertise and educational accountability.
APA
Li, H., Fang, Y., Zhang, S., Lee, S.M., Wang, Y., Trexler, M. & Botelho, A.F.. (2025). ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI Assisted Instructional Design. Proceedings of the Innovation and Responsibility in AI-Supported Education Workshop, in Proceedings of Machine Learning Research 273:94-104 Available from https://proceedings.mlr.press/v273/li25a.html.

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