ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI-Assisted Instructional Design
Project Overview
The document explores the integration of generative AI in education through the ARCHED framework, which emphasizes a human-centered approach to instructional design while maintaining pedagogical integrity and human agency. It critiques current AI tools that often prioritize automation at the expense of educational effectiveness, underscoring the necessity for transparency in AI decision-making. The ARCHED framework is designed to assist educators by providing a structured, multi-stage workflow that ensures human input throughout the instructional design process, thereby enhancing collaboration between educators and AI systems. Key applications of generative AI in education include personalized learning experiences, automated content generation, and enhanced feedback mechanisms, which can lead to improved outcomes for both educators and students. The findings suggest that when implemented thoughtfully, generative AI can augment teaching practices, foster engagement, and ultimately contribute to more effective learning environments. The document advocates for a balanced approach that leverages the capabilities of AI while prioritizing educational goals and human oversight, ensuring that technology serves as a partner in the educational process rather than a replacement for human judgment and creativity.
Key Applications
ARCHED (AI for Responsible, Collaborative, Human-centered Education Instructional Design)
Context: Educational technology for K-12 and higher education settings, targeting educators involved in instructional design.
Implementation: Implemented as a web-based tool using a three-phase workflow that includes educator input for learning objectives, AI analysis for alignment with pedagogical frameworks, and assessment development.
Outcomes: Facilitates collaboration between AI and educators, ensures pedagogical rigor, generates quality learning objectives comparable to those created by experts, and enhances diversity in assessment strategies.
Challenges: Potential for resource constraints, need for familiarization with the tool, and the challenge of ensuring high-quality AI generation across various educational disciplines.
Implementation Barriers
Technical Barrier
Challenges in the hardware acquisition, server hosting, and API costs associated with implementing AI tools in education.
Proposed Solutions: Exploration of open-weight LLMs as alternatives to reduce costs and increase accessibility.
Pedagogical Barrier
AI systems often lack deep pedagogical understanding, resulting in generated content that may not align with specific educational needs.
Proposed Solutions: Implementing a structured workflow that emphasizes human oversight and collaboration, ensuring that educators maintain control over content generation.
Transparency Barrier
The opacity of AI decision-making processes can hinder educators' ability to validate the pedagogical reasoning behind generated content.
Proposed Solutions: The ARCHED framework addresses this by separating the generation and evaluation processes, providing clear insights into AI decision-making.
Project Team
Hongming Li
Researcher
Yizirui Fang
Researcher
Shan Zhang
Researcher
Seiyon M. Lee
Researcher
Yiming Wang
Researcher
Mark Trexler
Researcher
Anthony F. Botelho
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Hongming Li, Yizirui Fang, Shan Zhang, Seiyon M. Lee, Yiming Wang, Mark Trexler, Anthony F. Botelho
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai