SHAPR: A Solo Human-Centred and AI-Assisted Practice Framework for Research Software Development

TL;DR

The paper introduces SHAPR, a practical framework designed to guide solo researchers in AI-assisted research software development, emphasizing accountability, reflection, and alignment with Action Design Research principles.

Contribution

It presents SHAPR as a novel operational framework that translates ADR principles into actionable guidance for solo, AI-assisted research software development.

Findings

SHAPR supports ADR cycles with explicit roles and reflective practices.

The framework enhances human accountability and learning in AI-assisted development.

SHAPR is evaluated through reflective analysis of its coherence and applicability.

Abstract

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.