Mateusz Dolata

Mateusz Dolata, Dzmitry Katsiuba, Natalie Wellnhammer et al.

Digital agents are considered a general-purpose technology. They spread quickly in private and organizational contexts, including education. Yet, research lacks a conceptual framing to describe interaction with such agents in a holistic manner. While focusing on the interaction with a pedagogical agent, i.e., a digital agent capable of natural-language interaction with a learner, we propose a model of learning activity based on activity theory. We use this model and a review of prior research on digital agents in education to analyze how various characteristics of the activity, including features of a pedagogical agent or learner, influence learning outcomes. The analysis leads to identification of IS research directions and guidance for developers of pedagogical agents and digital agents in general. We conclude by extending the activity theory-based model beyond the context of education and show how it helps designers and researchers ask the right questions when creating a digital agent.

Christoph Burtscher, Mateusz Dolata

Because artificial intelligence (AI) increasingly mediates organizational work, fairness has become a critical governance challenge. Existing frameworks often prioritize abstract ethical principles rather than fairness-specific ones and lack actionable guidance across the entire AI lifecycle. This study addresses the principles-to-practice gap in AI fairness governance. We develop actionable AI fairness practices and draw on a socio-technical and praxiological lens, conducting discourse and thematic analyses of 60 academic, policy, and practitioner sources. From these analyses, we derive a structured set of AI fairness practices in a comprehensive, AI lifecycle-spanning matrix organized by obligation degree and organizational role. The matrix provides dynamic, role-specific guidance to support implementation and sustainment of AI fairness. By extending the AI fairness beyond abstract principles to operationalized, actionable practices, we contribute to IS scholarship and offer a modular governance scaffold.

Sven Eckhardt, Niklas Kühl, Mateusz Dolata et al.

Although artificial intelligence (AI) systems are becoming increasingly indispensable, research into how humans rely on these systems (AI reliance) is lagging behind. To advance this research, this survey presents a novel, comprehensive sociotechnical perspective on AI reliance, essential to fully understand the phenomenon. To address these challenges, the survey introduces a categorization framework resulting in a morphological box, which guides rigorous AI reliance research. Further, the survey identifies the core influences on AI reliance within the components of a sociotechnical system and discusses current limitations alongside emerging future research avenues to form a research agenda.

Andreas Bucher, Birgit Schenk, Mateusz Dolata et al.

Workplace learning is used to train employees systematically, e.g., via e-learning or in 1:1 training. However, this is often deemed ineffective and costly. Whereas pure e-learning lacks the possibility of conversational exercise and personal contact, 1:1 training with human instructors involves a high level of personnel and organizational costs. Hence, pedagogical conversational agents (PCAs), based on generative AI, seem to compensate for the disadvantages of both forms. Following Action Design Research, this paper describes an organizational communication training with a Generative PCA (GenPCA). The evaluation shows promising results: the agent was perceived positively among employees and contributed to an improvement in self-determined learning. However, the integration of such agent comes not without limitations. We conclude with suggestions concerning the didactical methods, which are supported by a GenPCA, and possible improvements of such an agent for workplace learning.

Wenyuan Wu, Jasmin Heierli, Max Meisterhans et al.

The advent of increasingly powerful language models has raised expectations for language-based interactions. However, controlling these models is a challenge, emphasizing the need to be able to investigate the feasibility and value of their application. We present PROMISE, a framework that facilitates the development of complex language-based interactions with information systems. Its use of state machine modeling concepts enables model-driven, dynamic prompt orchestration across hierarchically nested states and transitions. This improves the control of the behavior of language models and thus enables their effective and efficient use. In this technical report we show the benefits of PROMISE in the context of application scenarios within health information systems and demonstrate its ability to handle complex interactions. We also include code examples and present default user interfaces available as part of PROMISE.

Mateusz Dolata, Stefan Feuerriegel, Gerhard Schwabe

Algorithmic fairness has been framed as a newly emerging technology that mitigates systemic discrimination in automated decision-making, providing opportunities to improve fairness in information systems (IS). However, based on a state-of-the-art literature review, we argue that fairness is an inherently social concept and that technologies for algorithmic fairness should therefore be approached through a sociotechnical lens. We advance the discourse on algorithmic fairness as a sociotechnical phenomenon. Our research objective is to embed AF in the sociotechnical view of IS. Specifically, we elaborate on why outcomes of a system that uses algorithmic means to assure fairness depends on mutual influences between technical and social structures. This perspective can generate new insights that integrate knowledge from both technical fields and social studies. Further, it spurs new directions for IS debates. We contribute as follows: First, we problematize fundamental assumptions in the current discourse on algorithmic fairness based on a systematic analysis of 310 articles. Second, we respond to these assumptions by theorizing algorithmic fairness as a sociotechnical construct. Third, we propose directions for IS researchers to enhance their impacts by pursuing a unique understanding of sociotechnical algorithmic fairness. We call for and undertake a holistic approach to AF. A sociotechnical perspective on algorithmic fairness can yield holistic solutions to systemic biases and discrimination.