Task Delegability to AI: Evaluation of a Framework in a Knowledge Work Context

With the increased research focus on ways to use AI for augmentation rather than automation of knowledge-intensive work, a myriad of questions on how this should be accomplished arises. To break down the complexity of Human-AI collaboration, this paper pursues the identification of factors that contribute to the delegation of tasks to AI in such a setting, and consequently gain insights into requirements for meaningful task allocation. To address this research gap, we carried out an empirical study on an existing task delegability framework in a knowledge work context. We employed several statistical approaches such as confirmatory factor analysis, linear regression, and analysis of covariance. Results show that an adapted framework with fewer factors fits the data better. As for the framework factors, we show that the factor trust predicts delegability best. Furthermore, we find a significant impact of task on delegability decision. Finally, we derive theoretical and design implications

Designing Automated Facilitation for Design Thinking: A Chatbot for Supporting Teams in the Empathy Map Method

The Empathy Map Method (EMM) in the Design Thinking approach is a powerful tool for user centered design but relies on the methodological skills and experience of rare facilitation experts to guide the team. In a collaboration engineering effort, we aim to make this expertise available to teams without constant access to a professional facilitator by packaging facilitation knowledge into structured process support and state-of-the art technology. Based on requirements from scientific and practitioners’ literature, we introduce the concept of a conversational agent in the form of a chatbot to take over the role of the facilitator of the EMM. We present an initial wizard of oz evaluation to derive insights and implications for improvements and the software implementation towards the ambitious goal of automated, non-human facilitation of EMM

Complex Problem Solving through Human-AI Collaboration: Literature Review on Research Contexts

Solving complex problems has been proclaimed as one major challenge for hybrid teams of humans and artificial intelligence (AI) systems. Human-AI collaboration brings immense opportunities in these complex tasks, in which humans struggle, but full automation is also impossible. Understanding and designing human-AI collaboration for complex problem solving is a wicked and multifaceted research problem itself. We contribute to this emergent field by reviewing to what extent existing research on instantiated human-AI collaboration already addresses this challenge. After clarifying the two key concepts (complex problem solving and human-AI collaboration), we perform a systematic literature review. We extract research contexts and assess them considering different complexity features. We thereby provide an overview of existing and guidance for designing new, suitable research contexts for studying complex problem solving through human-AI collaboration and present an outlook for further work on this research challenge

Developing a GIS-integrated Tool to Obtain Citizens’ Input in On-site Participation—Learnings from Participatory Urban Planning of a Large City

Informal participation procedures are used by authorities to obtain citizen input and to ease formal plan approval procedures and decision-making at an early stage of urban planning projects. Participation in spatial planning is no longer conceivable without geo-referenced contributions. Hence, digital tools such as geographic information systems (GIS) and multi-touch tables (MTT) are increasingly being used to complement traditional tools. These technologies offer advantages such as visual presentations based on spatial and planning data that can help to simplify and illustrate complex issues. However, the integration of GIS and MTT in on-site participation is challenging, since media disruptions and missing tool capabilities impede the collection of citizens’ input and subsequent processing. We address these challenges by eliciting requirements and prototypically developing a GIS-integrated tool that enables citizens to comment via GIS and MTT in a context-related and intuitive way using mobile devices at participatory planning events

Learning by Doing: Educators’ Perspective on an Illustrative Tool for AI-Generated Scaffolding for Students in Conceptualizing Design Science Research Studies

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

Conversational Agent as a Black Hat: Can Criticising Improve Idea Generation?

The Ideate phase of Design Thinking is the source of many idea creations. In this context, criticism is considered a creativity killer, yet recent studies show that criticism can be beneficial. An example of this is the black hat of one creativity method: Six Thinking Hats. It points out the weaknesses of an idea so that they are eliminated by further refining. Previous research shows that conversational agents have an advantage over humans when criticizing because of their perceived neutrality. To investigate this, we developed and implemented a conversational agent and evaluated it using an A/B test. The results of the study show that the prototype is perceived as less neutral when it criticizes. Criticizing by the conversational agent can lead to higher quality ideas. This work contributes to a better understanding of conversational agents in the black hat role as well as of their neutrality

Digital Facilitation Assistance for Collaborative, Creative Design Processes

People focus more and more on creating innovations collaboratively. Digital assistants (DAs) can accelerate such collaborative, creative design processes by supporting people in their work. Especially in the context of design, such as design thinking, moderators that facilitate collaborative, creative workshops can benefit from the support for their teams and themselves in the form of a DA. Based on interviews with experienced workshop facilitators from research and practice, we discuss implications for the design and usage of DAs in collaborative, creative design processes. We identify 16 distinct capabilities of DAs for task, process and interaction facilitation to guide design research and practitioners’ endeavors toward helpful automated DT facilitation support. Moreover, we outline a research agenda to foster future research on this young research area

Cultural Influences on Collaborative Work in Software Engineering Teams

International business activities increasingly lead to the formation of multicultural teams that work together as project teams, for a certain time both at a site as well as in virtual teams. Despite the modern conception of many companies that multicultural composite teams are more productive due to various perspectives and work styles, the ignorance and disrespect of these differences in work styles and perspectives can lead to misunderstanding and loss of productivity. In this paper, we report our findings from a systematic literature review that analyzes previous research on cross-cultural software engineering, to identify potential impacts of national cultural factors on collaborative approaches and behavior in software engineering teams. We discuss the current emerging state of knowledge and point out directions for advancing the understanding of cultural influences in this domain to lay the foundation for better collaboration design for cross-cultural software engineering teams

Exploring AI supported Citizen Argumentation on Urban Participation Platforms

The paradigm shift in urban planning toward citizen participation originates from the Smart City concept, as politicians and scientists argue that citizens should be included in the design of their environment. This led to the development of urban participation platforms and was enhanced by the COVID-19 pandemic as on-site participation was unavailable. Past projects showed that urban participation platforms can reach thousands of citizens, but it became apparent that citizens' contributions vary widely and are sometimes not understandable and comprehensible which limits their value for urban projects. Therefore, we examined how an AI-based feedback system can increase citizens’ argumentation on urban platforms. For this, an explorative comparison of two prototypes was conducted by applying Argumentation Theory and Mayring's qualitative content analysis to empirically analyze collected data. The findings highlight that the developed AI-based feedback system supports citizens and leads to more argumentative and comprehensible argumentations on urban participation platforms

Prototyping a Conversational Agent for AI-Supported Ideation in Organizational Creativity Processes

In this study, we present design guidelines (DGs) for the development and improvement of a virtual collaborator (VC) for Design Thinking (DT). Based on interviews in an ex ante study, we designed a first prototype of a VC. From an ex post evaluation using focus group discussions, we derive strengths, weaknesses, opportunities and threats of the VC. Strengths of the VC are good structure, giving inspiration as well as pace and accuracy. Opportunities are to set level of detail, give a more humane representation, and linking search with other DT phases. Weaknesses are not always suitable content and the VC being rather suitable for research phases as well as one-sided communication and no empathy. Threats are questionable search parameters and too narrow focus of search. We then derived DGs for further improvement of the VC, addressing the weaknesses, threats and ideas from participants