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 (iRAISE 2025), 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.
Abstract source: Published version, iRAISE 2025, PMLR 273:94–104.
Abstract license: CC BY 4.0.
How can LLMs assist instructional design while preserving educator control and pedagogical alignment? ARCHED separates learning-objective generation from analysis, combining a Learning Objective Generation System (LOGS) with an Objective Analysis Engine (OAE). Educators specify the teaching context, inspect Bloom’s taxonomy classifications, and select or refine objectives before assessment development.
The published evaluation includes expert classification of 120 computer-science learning objectives, a comparison of 30 generated and 30 human-created objectives, and preliminary feedback from two educational experts. It examines objective quality, taxonomic alignment, and the practical workflow. This evidence concerns instructional-design support; it does not establish classroom learning gains or broad adoption outcomes. The framework is relevant to research on human-AI collaboration, transparent generation and evaluation workflows, and responsible AI in education.
The workshop took place March 3, 2025; the proceedings volume was published March 31, 2025.