Research software has become a central vehicle for inquiry and learning in many Higher Degree Research (HDR) contexts, where solo researchers increasingly develop software-based artefacts as part of their research methodology. At the same time, generative artificial intelligence is reshaping development practice, offering powerful forms of assistance while introducing new challenges for accountability, reflection, and methodological rigour. Although Action Design Research (ADR) provides a well-established foundation for studying and constructing socio-technical artefacts, it offers limited guidance on how its principles can be operationalised in the day-to-day practice of solo, AI-assisted research software development. This paper proposes the SHAPR framework (Solo, Human-centred, AI-assisted PRactice) as a practice-level operational framework that complements ADR by translating its high-level principles into actionable guidance for contemporary research contexts. SHAPR supports the enactment of ADR Building-Intervention-Evaluation cycles by making explicit the roles, artefacts, reflective practices, and lightweight governance mechanisms required to sustain human accountability and learning in AI-assisted development. The contribution of the paper is conceptual: SHAPR itself is treated as the primary design artefact and unit of analysis and is evaluated formatively through reflective analysis of its internal coherence, alignment with ADR principles, and applicability to solo research practice. By explicitly linking research software development, Human-AI collaboration, and reflective learning, this study contributes to broader discussions on how SHAPR can support both knowledge production and HDR researcher training.

翻译：研究软件已成为许多高等学位研究（HDR）场景中开展探究与学习的核心载体，越来越多的独立研究者将开发基于软件的研究制品作为其研究方法论的一部分。与此同时，生成式人工智能正在重塑开发实践，它既提供了强大的辅助形式，也为研究的可问责性、反思性与方法论严谨性带来了新的挑战。尽管行动设计研究（ADR）为研究与构建社会技术制品提供了成熟的理论基础，但其原则如何在单人、人工智能辅助的研究软件开发的日常实践中具体实施，ADR 提供的指导较为有限。本文提出 SHAPR 框架（单人中心化、人工智能辅助实践框架）作为一个实践层面的操作框架，通过将 ADR 的高层原则转化为适用于当代研究情境的可操作指南，从而对 ADR 形成补充。SHAPR 通过明确在人工智能辅助开发中维持人的可问责性与学习所需承担的角色、产生的制品、反思性实践及轻量级治理机制，来支持 ADR “构建-干预-评估”循环的具体实施。本文的贡献是概念性的：SHAPR 本身被视为主要的设计制品与分析单元，并通过对其内部一致性、与 ADR 原则的契合度以及对单人研究实践的适用性进行反思性分析，以形成性评估的方式加以检验。通过明确地将研究软件开发、人机协作与反思性学习联系起来，本研究为更广泛地探讨 SHAPR 如何支持知识生产与 HDR 研究者培养作出了贡献。