Interdisciplinary teams developing complex technologies such as healthtech struggle to align disciplinary perspectives, stakeholder priorities, and evolving problem framings, particularly during rapid iteration, when existing collaboration tools offer limited support for in-session negotiation. We present KNIT, an AI-mediated framework that conceptualises AI-generated artefacts as computational boundary objects. KNIT supports convergence by externalising anonymised individual inputs into shared artefacts, including semantic clusters and stakeholder-centred problem reframings, that surface differences in interpretation and make them available for negotiation. We evaluated KNIT in workshops with seven early-stage healthtech teams (28 participants), analysing 190 interaction episodes using Carlile’s 3T framework. KNIT supported knowledge boundary crossing across syntactic (95.0%), semantic (86.3%), and pragmatic (84.8%) levels. We contribute empirical evidence and design principles showing how computational boundary objects mediate distinct boundary-crossing mechanisms, demonstrating that representational transformation rather than automation is the primary mechanism through which AI enables convergence across disciplinary boundaries.

Conversational agents (CAs) are rapidly advancing across industry and academia and it is crucial to consider the values embedded within these systems. Value-sensitive design practices have benefited AI-based systems, but have not yet been widely applied to CAs. This paper introduces the Value-Sensitive Conversational Agent (VSCA) Framework. The framework uses collaborative design (co-design) activities to guide CA creators and CA users to collaboratively create three key artefacts that elicit CA users’ values and are technically useful for CA creators to drive implementation forward, resulting in value embodied CA prototypes. The paper presents the practical framework and toolkit, followed by a mixed-method evaluation through design workshops, semi-structured interviews, and a comparative survey. Results show that the framework and toolkit increase CA creators’ value-sensitivity, empower CA users, enhance collaboration, and produce value-embodied prototypes. Based on this work, we offer 14 guidelines to practically support value sensitivity in CAs.

As AI systems become increasingly prevalent in critical domains, ensuring their alignment with stakeholders’ values is essential. However, recent studies have revealed deficiencies in socio-technical design processes and design activities for AI, particularly in eliciting diverse stakeholder values and integrating them into system design and development. To investigate these challenges empirically, we conducted 30 semi-structured interviews with AI practitioners. Our findings reveal several key challenges faced during AI design and development. Firstly, practitioners struggle with identifying and involving stakeholders due to uncertainties regarding user demo-graphics, and a lack of interdisciplinary expertise. Secondly, they encounter obstacles in integrating values into technologies, citing practical complexities, unclear responsibilities, and limited support. This paper presents a concept map detailing these four primary barriers and discusses potential strategies and recommendations for overcoming them. These strategies revolve around improving value elicitation practices, facilitating more meaningful stakeholder engagement, and understanding the impact of stakeholder values By qualitatively describing the barriers to integrating stakeholder values into AI systems, this study contributes to the emerging field of value-sensitive AI.

Abstract This study addresses the challenge of limited AI literacy among the general public hindering effective participation in AI codesign. We present a card-based AI literacy toolkit designed to inform nontechnical audiences about AI and stimulate idea generation. The toolkit incorporates 16 competencies from the AI Literacy conceptual framework and employs ‘What if?’ prompts to encourage questioning, mirroring designers’ approaches. Using a mixed methods approach, we assessed the impact of the toolkit. In a design task with nontechnical participants (N = 50), we observed a statistically significant improvement in critical feedback and breadth of AI-related questions after toolkit use. Further, a codesign workshop involving six participants, half without an AI background, revealed positive effects on collaboration between practitioners and end-users, fostering a shared vision and common ground. This research emphasizes the potential of AI literacy tools to enhance the involvement of nontechnical audiences in codesigning AI systems, contributing to more inclusive and informed participatory processes.

Value Sensitive Design (VSD) is a framework for integrating human values throughout the technology design process. In parallel, Responsible AI (RAI) advocates for the development of systems aligning with ethical values, such as fairness and transparency. In this study, we posit that a VSD approach is not only compatible, but also advantageous to the development of RAI toolkits. To empirically assess this hypothesis, we conducted four workshops involving 17 early-career AI researchers. Our aim was to establish links between VSD and RAI values while examining how existing toolkits incorporate VSD principles in their design. Our findings show that collaborative and educational design features within these toolkits, including illustrative examples and open-ended cues, facilitate an understanding of human and ethical values, and empower researchers to incorporate values into AI systems. Drawing on these insights, we formulated six design guidelines for integrating VSD values into the development of RAI toolkits.