SHAPR：一种面向研究软件开发的独立人类中心化和AI辅助实践框架
SHAPR: A Solo Human-Centred and AI-Assisted Practice Framework for Research Software Development
AI总结：
SHAPR框架为独立人类中心化和AI辅助的研究软件开发提供实践指导，通过整合行动设计研究原则，促进反思学习和方法论严谨性。
摘要：
研究软件在许多高级学位研究（HDR）情境中已成为探究和学习的核心工具，其中越来越多的独立研究者将开发基于软件的成果作为其研究方法的一部分。同时，生成式人工智能正在重塑开发实践，提供了强大的帮助形式，同时也引入了新的挑战，如责任、反思和方法论严谨性。尽管行动设计研究（ADR）为研究和构建社会技术成果提供了坚实的理论基础，但其在独立、AI辅助研究软件开发日常实践中如何操作化指导方面有限。本文提出了SHAPR框架（独立、人类中心化、AI辅助实践）作为实践层面的操作框架，通过将ADR的高层原则转化为当代研究环境中的可操作指导来补充ADR。SHAPR通过明确角色、成果、反思实践和轻量级治理机制，支持执行ADR的构建-干预-评估循环，以维持AI辅助开发中的人类责任和学习。本文的贡献是概念性的：SHAPR本身被视为主要设计成果和分析单位，并通过反思分析其内部一致性、与ADR原则的契合度以及在独立研究实践中的适用性进行形式性评估。通过明确将研究软件开发、人机协作和反思学习联系起来，本研究为更广泛的讨论提供了支持，探讨SHAPR如何支持知识生产和HDR研究者培训。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
