arXiv cs.CY· Tian Zheng·· 12 小时前AI 评分20
论文提出 Everyone's Backyard 模型：AI 时代科研指导应转向全流程工作流与跨学科协作
Learning by Collaborating in the Age of AI: Research Mentorship for Polymathic, Workflow-Centered Science
AI 导读
论文提出 Everyone's Backyard 跨学科科研指导模型，让学科专家、学员与社区伙伴在共享研究实践中双向互导，围绕统计思维、数据伦理、协作与 AI 向善四项能力展开。
正文
Abstract:Artificial intelligence (AI) is changing how data-driven research is produced. As AI tools make information and technical execution more accessible, mentorship must concentrate on judgment, ethics, and the design of end-to-end AI-augmented workflows. Disciplinary experts need mentoring to adopt AI while preserving their expertise and human agency, and AI-native research trainees need mentoring to become polymathic workflow developers. This chapter proposes Everyone's Backyard, an interdisciplinary model in which disciplinary experts, trainees, and community partners mentor one another in both directions and across disciplines through shared research practice in a convergence studio. The model centers on four capacities: statistical thinking, data ethics, collaboration, and AI for social good and society. We illustrate the model with the Spring 2026 aiX Faculty Fellows Program at Columbia University, which brought together 20 faculty fellows and 15 student interns from 14 schools and departments. Evaluation data show growth in faculty confidence, intern competence, and cross-disciplinary connection.
| Subjects: | Computers and Society (cs.CY) |
| MSC classes: | 97C60 |
| Cite as: | arXiv:2610.34606 [cs.CY] |
| (or arXiv:2610.34606v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34606 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tian Zheng [view email]
[v1]
Sun, 25 Oct 2026 22:10:35 UTC (737 KB)
来源：arXiv cs.CY · arxiv.org