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

Hongming Li, Yizirui Fang, Shan Zhang, Seiyon M. Lee, Yiming Wang, Mark Trexler, Anthony F. Botelho

TL;DR

ARCHED tackles the problem of opaque, automation-first AI in instructional design by introducing a human-centered, multi-stage workflow anchored in Bloom's taxonomy. Its core innovation is the separation of learning-objective generation (LOGS) and objective analysis (OAE) to preserve educator agency while providing transparent AI reasoning. Empirical results show strong agreement with expert classifications ($\kappa_w = 0.834$) and positive expert feedback on usability and pedagogical alignment, indicating that AI-assisted design can enhance quality without sacrificing human oversight. The framework supports diverse assessment strategies and aims for scalable, accessible deployment, marking a meaningful step toward responsible AI integration in education.

Abstract

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.

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

TL;DR

ARCHED tackles the problem of opaque, automation-first AI in instructional design by introducing a human-centered, multi-stage workflow anchored in Bloom's taxonomy. Its core innovation is the separation of learning-objective generation (LOGS) and objective analysis (OAE) to preserve educator agency while providing transparent AI reasoning. Empirical results show strong agreement with expert classifications () and positive expert feedback on usability and pedagogical alignment, indicating that AI-assisted design can enhance quality without sacrificing human oversight. The framework supports diverse assessment strategies and aims for scalable, accessible deployment, marking a meaningful step toward responsible AI integration in education.

Abstract

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.

Paper Structure

This paper contains 10 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. The ARCHED Framework: A Human-Centered Approach to AI-Assisted Instructional Design
  4. Theoretical Foundation and Design Principles
  5. Framework Architecture and Implementation
  6. Technical Implementation
  7. Results
  8. Technical Evaluation
  9. Pilot User Feedback
  10. Discussion

Figures (4)

  • Figure 1: ARCHED Implementation Methodology: This shows the three-phase workflow: (a) Initial generation through LOGS with educator input, specifying parameters such as grade level, subject area, and desired Bloom's taxonomy levels (b) Collaborative refinement with OAE analysis, and (c) Assessment development based on finalized objectives. Human educators maintain control throughout the process.
  • Figure 2: Main interface showing the three-phase workflow integration with continuous educator input in the loop
  • Figure 3: OAE analysis interface providing detailed pedagogical feedback and guidance
  • Figure 4: Confusion matrix showing agreement between AI and expert classifications across Bloom's taxonomy levels