My Approach to AI
I view AI as a tool that should augment human thinking rather than replace it. In my research, teaching, and professional development work, I focus on helping people develop the knowledge, skills, and judgment needed to use AI effectively and ethically. I believe humans remain responsible for evaluating information, making decisions, and taking accountability for outcomes, while AI can support efficiency, communication, and knowledge work.
AI-Assisted Peer Review Workflow
One example of this approach is the Peer Review Comment Refinement Workflow (PRCRW) that I developed and documented on GitHub. Academic peer review often requires reviewers to improve the clarity, professionalism, and constructiveness of their comments while preserving their original intent. To improve efficiency, I designed and tested a reusable workflow that includes structured prompts and clear procedures for refining peer review comments. By documenting the workflow publicly, other researchers can reuse the prompts instead of creating new ones from scratch, making the process more efficient, transparent, and reproducible.
https://github.com/ruiping935/peer-review-comment-refinement-workflow
Human in the Loop
One experience that reinforced the importance of human oversight occurred when I experimented with using AI to review academic manuscripts. While AI could identify some obvious issues and improve efficiency, I found that it sometimes missed important methodological concerns and occasionally generated vague or misleading suggestions. In several cases, the AI overlooked issues that would have been apparent to a researcher with domain expertise. This experience reminded me that AI can support scholarly work, but it cannot replace expert judgment. As a result, I designed my workflow so that reviewers first develop their own evaluation and recommendations, and then use AI only to refine the clarity, organization, and professionalism of their comments.
Connecting to AI Leadership Principles
The Peer Review Comment Refinement Workflow demonstrates several AI Leadership principles. It improves efficiency by providing a reusable protocol and tested prompts that researchers can apply across multiple peer reviews. It keeps humans in the loop by requiring reviewers to develop their own evaluation and recommendations before using AI to refine communication. It operationalizes ethics through transparency, accountability, and human verification of AI-generated outputs. Finally, it promotes agency and adaptability by openly sharing the workflow on GitHub, allowing other researchers to reuse, modify, and improve the process for their own contexts.
AI Leadership in Education
My interest in AI extends beyond research workflows. I have designed and delivered professional development workshops that help teachers develop the knowledge, skills, and judgment needed to use AI effectively and ethically in classroom assessment. Across both my research and teaching, I see AI leadership as creating systems and learning experiences that combine efficiency, transparency, human oversight, and responsible use. My goal is not simply to use AI, but to help others understand when, how, and why AI should be used.
Ruiping Huang 