The exhibition brings topics relating to AI and work to life. On this page, you will find the academic studies, concepts and further resources that form the basis of the individual exhibits. <\/p>\n
Navigation:<\/b> Artificial intelligence (AI) refers to a <\/span>range of technical processes that process large amounts<\/span> of data in order to recognise patterns<\/span> The data processed includes, for example, numbers, <\/span>text, photos and voice recordings. Using <\/span>the recognised pattern, the AI presents a solution <\/span>that would originally have required human intelligence.<\/span> The AI highlights discrepancies, makes recommendations, <\/span>makes predictions, generates images,<\/span> and writes text and programmes.<\/span><\/p>\n<\/div><\/div><\/div><\/div><\/div> AI can detect anomalies in images that are barely visible to the human eye \u2013 and it does so quickly and without tiring. This is why AI is used, amongst other things, to analyse X-ray images. However, as it lacks an understanding of context, AI can make incorrect decisions in difficult cases. The best solution therefore often lies in collaboration between humans and AI. Research shows how this teamwork can be structured to deliver sound and responsible results. <\/span><\/p>\n<\/div> Authors: Abstract: Citation: Authors: Abstract: Citation: Authors: Abstract: Citation: Jobs change when AI supports workers in their tasks. Whilst this can make tasks easier, it does not automatically mean that working conditions improve for employees. A human-centred approach ensures that work remains varied, offers opportunities for development, and enables people to acquire new skills. Employees must be able to develop their roles further when working alongside AI. The outcomes of work carried out for other people \u2013 for example, in healthcare or the service sector \u2013 <\/span>must also become more reliable<\/span> and of a higher standard through the use of AI.<\/span><\/p>\n<\/div> Authors: Abstract: Citation: Authors: Citation: Authors: Abstract: Citation: Every job consists of a variety of tasks, which may be physical, social or technical in nature. These characteristics are relevant to our identity in the workplace, as they shape our sense of who we are, what matters to us and what makes us proud. When tasks are altered, shifted or replaced through the use of AI, this has an impact on our job identity. The transformation of work must therefore be shaped in such a way that identity-forming characteristics are preserved or enhanced. <\/span><\/p>\n<\/div>
\nStation 1: What is artificial intelligence?<\/a>
\nStation 2: Humans and AI working as a team<\/a>
\nStation 3: Focusing on people in the changing landscape of occupations within professional fields<\/a>
\nStation 4: The importance of job identity<\/a>
\nStation 5: Skills shortage \u2013 AI safeguards knowledge<\/a>
\nStation 6: Changes in the regional employment structure<\/a>
\nStation 7: Ethics and public participation in the use of AI<\/a>
\nStation 8: HUMAINE \u2013 People with responsibility for AI<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>Station 1: What is artificial intelligence?<\/span><\/h3>\n
Station 2: Humans and AI working as a team<\/h3>\n
<\/i><\/i><\/span>Defining human-AI teaming the human-centered way: a scoping review and network analysis <\/span><\/a><\/h4><\/div>
\n<\/strong>Sophie Berretta, <\/span>Alina Tausch, <\/span>Greta Ontrup, <\/span>Bj\u00f6rn Gilles, <\/span>Corinna Peifer, <\/span>Annette Kluge<\/span><\/p>\n
\n<\/strong>Introduction<\/b>: With the advancement of technology and the increasing utilization of AI, the nature of human work is evolving, requiring individuals to collaborate not only with other humans but also with AI technologies to accomplish complex goals. This requires a shift in perspective from technology-driven questions to a human-centered research and design agenda putting people and evolving teams in the center of attention. A socio-technical approach is needed to view AI as more than just a technological tool, but as a team member, leading to the emergence of human-AI teaming (HAIT). In this new form of work, humans and AI synergistically combine their respective capabilities to accomplish shared goals.
\n<\/span>Methods<\/b>: The aim of our work is to uncover current research streams on HAIT and derive a unified understanding of the construct through a bibliometric network analysis, a scoping review and synthetization of a definition from a socio-technical point of view. In addition, antecedents and outcomes examined in the literature are extracted to guide future research in this field.
\n<\/span>Results<\/b>: Through network analysis, five clusters with different research focuses on HAIT were identified. These clusters revolve around (1) human and (2) task-dependent variables, (3) AI explainability, (4) AI-driven robotic systems, and (5) the effects of AI performance on human perception. Despite these diverse research focuses, the current body of literature is predominantly driven by a technology-centric and engineering perspective, with no consistent definition or terminology of HAIT emerging to date.
\n<\/span>Discussion<\/b>: We propose a unifying definition combining a human-centered and team-oriented perspective as well as summarize what is still needed in future research regarding HAIT. Thus, this work contributes to support the idea of the Frontiers Research Topic of a theoretical and conceptual basis for human work with AI systems. <\/span><\/p>\n
\n<\/b>Berretta, S., Tausch, A., Ontrup, G., Gilles, B., Peifer, C., & Kluge, A. (2023). Defining human-AI teaming the human-centered way: A scoping review and network analysis. Frontiers in Artificial Intelligence, 6, 1250725.<\/span><\/p>\n<\/i><\/i><\/span>Human or AI first? A holistic perspective on the sequential order of joint human-AI inspection workflows <\/span><\/a><\/h4><\/div>
\n<\/strong>Sophie Berretta, <\/span>Alina Tausch, <\/span>Florian B\u00fclow, <\/span>Bernd Kuhlenk\u00f6tter, <\/span>Maximilian Topp, <\/span>Christian Els, <\/span>Corinna Peifer, <\/span>Annette Kluge<\/span><\/p>\n
\n<\/strong>The complementary integration of artificial intelligence (AI) in the workplace requires balancing performance goals with psychological needs, as both are essential for sustained outcomes. This study examines different workflows (AI-first and human-first) as cognitive forcing strategies to test whether they enhance performance and psychological outcomes compared to human-only and AI-only processing. In a one-factorial between-subjects experiment (N = 101) within a visual inspection task, evaluated at up to three measurement points, performance variables (accuracy, speed, error rates) and psychological variables (vigilance, flow, teaming experience, wellbeing when working with the AI) were assessed. Human-AI collaboration outperformed AI-only in error rates (\u03b72 = 0.29) and human-only in speed (\u03b72 = 0.11 – 0.14), but only when AI preceded human processing. The AI-first workflow enhanced teaming perception compared to human-only processing (\u03b72 = 0.07). Moreover, human-AI collaborative processing reduced flow decrease compared to human-only processing (\u03b72 = 0.07). Overall, AI processing preceding human processing produces the best balance between performance and psychological outcomes in safety-critical inspection tasks, supporting a holistic view of AI integration in the workplace. <\/p>\n
\n<\/b>Berretta, S., Tausch, A., B\u00fclow, F., Kuhlenk\u00f6tter, B., Topp, M., Els, C., Peifer, C., & Kluge, A. (2025). Human or AI first? A holistic perspective on the sequential order of joint human-AI inspection workflows. Applied Ergonomics, 132, 104669.<\/span><\/p>\n<\/i><\/i><\/span>Maybe adaptive (not adaptable) automation in production: an experimental study comparing the locus of authority in work system dynamics<\/span><\/a><\/h4><\/div>
\n<\/strong>Sophie Berretta, <\/span>Alina Tausch, <\/span>Paul Glogowski<\/span><\/p>\n
\n<\/strong>Introduction<\/b>: In collaborative industrial work systems, the locus of authority\u2014whether control over system dynamics is initiated by the system (adaptive) or by the human operator (adaptable)\u2014can shape work experience and perceptions of the robotic partner. This study exploratively investigates how these control schemes and a static work system influence key psychological factors and which one should be favored in collaborative assembly tasks.
\n<\/span>Methods<\/b>: In an experimental laboratory study with n = 27 participants, a collaborative gearbox assembly task with a robot is used to compare adaptive and adaptable control schemes against a non-adjustable baseline. In the adaptive condition, the robot’s speed automatically adjusted to human proximity; in the adaptable condition, speed is manually adjustable via interface buttons; in the baseline condition, speed remains static. The primary endpoint was the longitudinal, comparative investigation of flow experience and perceived task demands as they represent central indicators of employees’ psychological experience in dynamic work systems. Dependent variables included additionally autonomy perception and workplace fit (task perception), and trust, safety, robot’s intelligence, and collaboration satisfaction (robot interaction perception), as well as cycle time (performance), measured across four collaborative trials and five time points, making group comparisons and the investigation of construct dynamics possible.
\n<\/span>Results<\/b>: Both control schemes demonstrated improved collaboration experiences compared to the baseline condition. Participants in the adaptive condition reported higher flow experience and workplace fit, and showed the fastest production times across all trials, while participants in the adaptable condition reported higher autonomy and task demands. Additionally, trust in the robot increases over time, harmonizing trust levels across conditions.
\n<\/span>Discussion<\/b>: Despite limitations related to an exploratory design and a small sample size potentially masking existing effects, the findings indicate that dynamic working conditions can improve worker experience. However, the findings do not present a consistent picture favoring either adaptive or adaptable control schemes. Further research is needed to determine which control scheme is preferable in different task contexts. To inspire future work, we derive a set of hypotheses from the study’s initial findings. <\/span><\/p>\n
\n<\/b>Berretta, S., Tausch, A., & Glogowski, P. (2025). Maybe adaptive (not adaptable) automation in production: an experimental study comparing the locus of authority in work system dynamics. Frontiers in Organizational Psychology, 3, Article 1685961.<\/span><\/p>\nStation 3: Focusing on people in the changing landscape of occupations within professional fields<\/span><\/h3>\n
<\/i><\/i><\/span>Types of Human-AI Role Development - Benefits, Harms and Risks of AI-Based Assistance from the Perspective of Professionals in Radiology<\/span><\/a><\/h4><\/div>
\n<\/strong>Uta Wilkens, <\/span>Valentin Langholf, <\/span>Marc Dewey<\/span><\/p>\n
\n<\/strong>In this paper we analyse the role development of professionals in healthcare in face of AI applications to their workplaces. The conceptual background is role development theory aligned to human-AI work settings. The empirical fundament is a case study analysis conducted at Charit\u00e9 including a profile analysis of survey data from radiology (N=128) and a structured content analysis of ten semi-structured interviews with professionals. The outcome is the distinction of two most typical human-AI role concepts, (1) the AI-embracing human-AI role concept, and (2) the AI-ambivalent human AI-role concept. These types are based on the same set of antecedents in terms of AI literacy, former digital experience, individual perspective on the technology and the impact of AI on the overall change of individual tasks. This allows to understand why the first type experiences benefits from the human-AI role development while the second type cannot exclude personal harms. The AI-embracing role concept enhances role making with AI and incorporates AI implementation, the latent risk of AI in the AI-ambivalent concept leads to role taking against the technology. <\/p>\n
\n<\/b>Wilkens, U., Langholf, V., & Dewey, M. (2024). Types of Human-AI Role Development – Benefits, Harms and Risks of AI-Based Assistance from the Perspective of Professionals in Radiology. Journal of Competences, Strategy & Management, 12. <\/span><\/p>\n<\/i><\/i><\/span>CollaborAId SMART: A concept for designing identity-creating basic work in the context of AI<\/span><\/a><\/h4><\/div>
\n<\/strong>Sophie Berretta, Alina Tausch, Annette Kluge<\/span><\/p>\n
\n<\/b>Berretta, S., Tausch, A., & Kluge, A. (2023). CollaborAId SMART: A concept for designing identity-creating basic work in the context of AI. In Gesellschaft f\u00fcr Arbeitswissenschaft e.V., Sankt Augustin (Ed.), Menschengerechte Arbeitsgestaltung \u2013 Basisarbeit und neue Arbeitsformen. GfA-Press. <\/span><\/p>\n<\/div><\/div><\/div><\/i><\/i><\/span>Pathway to work with AI: Testing the clAIr role development method in an industrial work environment<\/span><\/a><\/h4><\/div>
\n<\/strong>Valentin Langholf, Uta Wilkens<\/p>\n
\n<\/strong>The use of artificial intelligence (AI) in work processes requires proactive changes to job roles, as areas of responsibility within job profiles are shifting, resulting in new patterns of interaction between humans and AI, and between employees. To avoid role conflicts, resistance to AI and other undesirable side effects of AI integration, organisations must support the development of human\u2013AI roles through appropriate measures. This article presents a methodologically sound approach to role development (\u2018clarifying AI-augmented individual roles\u2019 \u2013 clAIr), which was developed and tested using the example of service technicians in a mechanical engineering company, both before and during the introduction of AI-based services. It illustrates how role clarity in interaction with AI can be achieved when tasks are restructured, and how role development also encompasses internal collaboration with other departments and goal-oriented external communication within the customer environment. The method yields six basic roles, which are based on insights from role theory and encompass not only role clarity but also role identity and role innovation. clAIr enables a proactive, process-oriented examination of human\u2013AI work roles.
\n<\/span>Practical relevance: AI applications are affecting an ever-increasing number of work processes. Scientifically sound findings and examples of good practice are needed to ensure their successful integration into work processes. A socio-technical systems analysis focused on changes in work roles is particularly promising in this regard, as it translates the changes to tasks and occupations brought about by the use of AI into a comprehensive approach that also takes the identity of the worker into account. Existing job evaluation approaches focus on the characteristics of job designs, but are unable to capture the preceding process of role definition as a key determinant of implementation. This process support is enabled by the clAIr method for defining roles for working with AI. Its use requires an understanding of role theory and expertise in organisational development. <\/span><\/p>\n
\n<\/b>Langholf, V., & Wilkens, U. (2024). Pathway to work with AI: Testing the clAIr role development method in an industrial work environment. Zeitschrift f\u00fcr Arbeitswissenschaft, 78, 377\u2013386. <\/span><\/p>\nStation 4: The importance of job identity<\/h3>\n