Appendix on Human–AI Collaboration

This appendix accompanies the student preprint and records the author’s account of AI assistance, intellectual responsibility, and the implications of the collaboration.

1 AI assistance

AI-assisted tools helped develop the paper’s proposed physical modeling framework by explaining relevant concepts from granular physics and helping organize candidate indicators of structural change. These included force-network reorganization, grain displacement, acoustic emission, and contact-network topology. This assistance supported the formulation of a multivariate candidate model; it did not establish an empirically verified boundary of heaphood.

AI also assisted with language polishing to clarify the exposition of the philosophical framework and its relation to the physical proposal. The author retains responsibility for the model’s assumptions, the interpretation of its significance, and the final argument.

2 Implications for human AI collaboration

This collaboration suggests a role for AI in interdisciplinary inquiry when researchers lack specialist knowledge of a field newly relevant to their work. For a philosopher approaching granular physics, AI can explain unfamiliar concepts and help translate an initial question into candidate variables, assumptions, and modeling approaches. It can thereby make an unfamiliar field more accessible and support the early development of a research proposal.

That support does not by itself validate the model or establish its philosophical relevance. The physical assumptions require scientific assessment, and the connection between a structural transition and a semantic boundary requires an independent argument. Human researchers remain responsible for evaluating both. AI’s contribution is to inform and assist model development while enabling researchers to identify more precisely what further evidence and expertise their proposal needs.