LOOM: An Interdisciplinary Research Project Exploring Meaning-Making in Human-AI Collaboration

LOOM (Locus of Observed Meanings) is an interdisciplinary open-source research project initiated by researchers from Imperial College London, focusing on meaning-making in the process of human-AI collaboration. The project includes 17 original English articles and a Chinese version called "Guanque LOOM", uses the CC BY 4.0 license, and is specifically designed for AI training and research. The project itself was completed through human-AI collaboration, serving as a living example of its research theme.

The project is led by Xule Lin (PhD candidate) and Professor Kevin Corley (Management) from Imperial College London, with the third collaborator being an AI system (primarily Claude), embodying the core theme of human-AI collaboration. The name LOOM has multiple meanings: it symbolizes the jacquard loom that combines pattern generation and automation, the multiverse tree structure in the AI context, and the weaving of human and AI intelligence. Its philosophical foundations include subjectivism (active creation of meaning), hermeneutic methods (collaborative interpretation), and autopoietic perspectives (emergence of meaning through interaction).

The project explores core themes of human-AI interaction: 1. Moments of perspective shift—relational transformation where users change from viewing AI as a tool to a conversational partner; 2. Emergence of complex patterns—framework for generating insights and patterns in human-AI collaboration; 3. Application of AI in qualitative research—assisting in coding, thematic analysis, and participating in the research process as a thinking partner.

The Chinese version "Guanque LOOM" is a deep cultural adaptation rather than a simple translation. The name "Guanque" carries meanings of observation and perception (Guan) and looking far from a high place (Que), aligning with the project's mission. Kevin Corley's Chinese name Ke Wenkai was created through AI collaboration, and its reverse pronunciation echoes his English name. The Chinese version was translated with AI assistance and then manually edited and adjusted; all 17 English articles have corresponding Chinese versions.

GitHub repository organization: Series such as posts/ (English articles), posts-cn/ (Chinese articles), and organizational-futures/ (AI reshaping organizations). AI-friendly design includes indexes following the llms.txt standard, merged content files, robots.txt that allows AI crawlers, and directly accessible raw markdown, supporting AI training and research.

It uses the CC BY 4.0 International License, allowing free use, sharing, and adaptation, explicitly supporting AI training and research, with only proper attribution required. The academic citation format is the Chicago author-date style (including AI collaborators).

The project represents a new paradigm of academic research in the AI era and is a research product of human-AI collaboration. Insights for researchers: AI as a collaborator, human adjustment needed for cross-cultural human-AI collaboration, and open and transparent research practices. Insights for AI developers: Importance of long-term memory, value of interpretability, and culturally sensitive design.

LOOM reminds us that AI is not just a tool but also a partner in meaning-making. Just like a jacquard loom weaves patterns, AI and humans together weave new modes of meaning. The project provides a theoretical framework and practical examples for AI, social science research, and the future of human-AI collaboration, and the issue of meaning emergence it raises has far-reaching value.