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Design Science Research (DSR) provides a rich body of frameworks, approaches, and methods to support researchers in conducting DSR projects. However, there is little tool support and guidance for effectively documenting DSR processes. In this article, we present a prototypical implementation of a conversational agent called "DSR Buddy" that is inte...
Citations
... Tool-based scaffolding was proposed to address several learning challenges (Law et al., 2020). Existing tools for DSR support (e.g., Contell et al., 2017;Gau et al., 2022;Morana et al., 2018a;vom Brocke et al., 2017) focus on problem-independent meta-level support, e.g., a canvas for organizing study contents or links to reading material. Offering content-level support (CLS), however, might be particularly helpful to novices, as they might lack the required prior knowledge to grasp the problem and develop appropriate solutions. ...
Design science research (DSR) is taught in university courses and used by students for their final theses. For successfully learning DSR, it is important to learn to apply it to real-world problems. However, students not only need to learn the new DSR paradigm (meta-level) but also need to develop an understanding of the problem domain (content-level). In this paper, we focus on content-level support (CLS), proposing an illustrative tool to aid students when learning to develop a conceptual design with DSR (e.g., for a prototype). Following the DSR paradigm, we deductively identify students’ issues and use the scaffolding approach to develop design requirements (DRs) and design principles (DPs). To offer AI-generated scaffolding, we use the generative language model (GLM) “GPT-3.” We evaluate our illustrative design through 13 expert interviews. Our results show that providing students with CLS is perceived to be helpful, but the interaction with the student needs to be designed carefully to circumvent unintended usage patterns. We contribute DPs and an illustrative instantiation thereof toward a DSR tool support ecosystem. More broadly, we contribute to the understanding of how humans can be supported by AI to solve problems, an important challenge in human-AI collaboration research.
https://jise.org/volume34/n3/JISE2023v34n3pp279-292.html
Research transparency promotes openness and trust in the process, evidence, contributions, and implications of scientific inquiry. Information Systems (IS), as a pluralistic research community, must address transparency in relation to its use of multiple research methods appropriate to complex socio-technical contexts and challenging research questions. This commentary presents a set of important transparency challenges and actionable guidance for the Design Science Research (DSR) community. We propose a DSR Transparency Framework containing six forms of transparency: process, problem space, solution space, build, evaluation, and contribution. For each, we discuss challenges with guidance to achieve effective DSR transparency throughout the publication process.
