Learning by Collaborating in the Age of AI: Research Mentorship for Polymathic, Workflow-Centered Science

arXiv:2610.34606v1 Announce Type: new 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.

The text implies AI is changing research production, requiring new mentorship models. The proposed model promotes interdisciplinary collaboration and AI adoption.

The proposed mentorship model aims to foster ethical, collaborative, and multidisciplinary AI research practices, potentially enhancing scientific workflows and human agency. However, the impact is mediated and gradual, relying on adoption and culture change, so implications are moderately positive but not transformative on their own.

This is a small-scale pilot program report with self-reported outcomes, not a broad societal claim. It suggests a modestly positive approach to preserving human judgment amid AI adoption, but evidence is thin and institution-specific, limiting confidence in generalized implications.

The article proposes a model for AI-augmented research and mentorship aimed at fostering essential skills and ethical considerations. It describes an initiative that shows positive growth in confidence, competence, and connection, indicating a beneficial integration of AI into academic processes. The positive impact is based on a proposed model and preliminary evaluation data.

The text describes a positive, collaborative approach to integrating AI into research. It highlights growth in key areas and interdisciplinary connection. The model proposed is designed to enhance human capabilities and ethical use of AI.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   d11d9173-b31c-4761-ab49-e8e876b5fe3b
           https://arxiv.org/abs/2610.34606
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      +24  (Favourable)
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CONFIDENCE 58%