SHAPR: Operationalising Human-AI Collaborative Research Through Structured Knowledge Generation

TL;DR

SHAPR is a structured framework that operationalizes human-AI collaborative research by integrating iterative cycles, knowledge units, and traceability to enhance transparency, reproducibility, and systematic knowledge generation.

Contribution

This paper operationalizes the SHAPR framework, introducing structured models, traceability mechanisms, and AI-executable processes for human-AI collaborative research practice.

Findings

Developed interconnected models for research activities

Introduced Structured Knowledge Units (SKUs) for knowledge reuse

Enabled traceability and transparency in AI-assisted research

Abstract

SHAPR (Solo Human-Centred and AI-Assisted Practice) is a framework for research software development that integrates human-centred decision-making with AI-assisted capabilities. While prior work introduced SHAPR as a conceptual framework, this paper focuses on its operationalisation as a structured, traceable, and knowledge-generating approach to AI-assisted research practice. We present a set of interconnected models describing how research activities are organised through iterative cycles (Explore-Build-Use-Evaluate-Learn), how artefacts evolve through development and use, and how empirical evidence is transformed into conceptual knowledge. Central to this process are Structured Knowledge Units (SKUs), which provide modular and reusable representations of insights derived from practice, supporting knowledge accumulation across cycles. The framework introduces evidence and traceability as a cross-cutting mechanism linking human decisions, AI-assisted development, and artefact evolution to enable transparency, reproducibility, and systematic refinement. SHAPR is also positioned as an AI-executable research framework, as its structured processes and documentation can be interpreted by generative AI systems to guide research workflows. Simultaneously, SHAPR supports a continuum of AI involvement, allowing researchers to balance control, learning, and automation across different contexts. Beyond individual workflows, SHAPR is conceptualised as an integrated research system combining LLM workspaces, development environments, cloud storage, and version control to support scalable, knowledge-centred research practices. Overall, SHAPR provides a practical and theoretically grounded foundation for conducting rigorous, transparent, and reproducible research in AI-assisted environments, contributing to the development of scalable and methodologically sound research practices.