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.

翻译：SHAPR（单人在人机协同与AI辅助实践框架）是一种融合以人为中心的决策与AI辅助能力的研究软件开发框架。虽然先前的研究将SHAPR作为概念性框架提出，但本文聚焦于将其可操作化为一种结构化、可追溯且能生成知识的AI辅助研究实践方法。我们提出一组相互关联的模型，描述如何通过迭代循环（探索-构建-应用-评估-学习）组织研究活动，制成品通过开发与使用如何演进，以及经验证据如何转化为概念性知识。该过程的核心是结构化知识单元（SKUs），它提供从实践中获得的洞察的模块化可重用表示，支撑跨循环的知识积累。该框架引入证据与可追溯性作为跨领域机制，将人类决策、AI辅助开发及制成品演进相联结，以实现透明度、可复现性与系统性优化。SHAPR同时被定位为一种AI可执行研究框架——其结构化流程与文档可被生成式AI系统解析以引导研究工作流。与此同时，SHAPR支持AI参与度的连续谱系，允许研究者根据不同情境平衡控制、学习与自动化。在个体工作流之外，SHAPR被概念化为一个集成研究系统，整合LLM工作空间、开发环境、云存储与版本控制，支撑可扩展的、以知识为中心的研究实践。总体而言，SHAPR为在AI辅助环境中开展严谨、透明且可复现的研究提供了兼具理论依据与实践基础的基础，推动了可扩展且方法论健全的研究实践的发展。