https://fordatis.fraunhofer.de/handle/fordatis/38 2026-05-01T05:30:39Z 2026-05-01T05:30:39Z Elsenbast, Christian https://fordatis.fraunhofer.de/handle/fordatis/414 2024-09-03T01:30:43Z 2024-08-23T00:00:00Z

Titel: Attitude of Emergency Dispatchers Towards Artificial Intelligence – A Black Box of Expectations Datenautorinnen und Datenautoren: Elsenbast, Christian Zusammenfassung: Introduction: AI is transforming various industries, especially healthcare and emergency services. For example, AI helps with clinical decision support, detects cardiac arrest and stroke during calls, and manages text-to-speech translation. On the human-centered side, the societal and personal impacts of AI and other technologies are significant but under-researched. Therefore, this study examines the belief systems of emergency dispatchers regarding AI applications. Methods: From September 2021 to September 2023, eight extensive interviews were conducted with a total of 31 individuals, lasting over 619 minutes. Following grounded theory, the interview guide was iteratively adapted to support theory development. Results: The interviews revealed a high level of commitment to their profession and a strong appreciation and interest in research. While many issues within public safety and answering points (PSAPs) and the healthcare system were identified, few concrete ideas for AI-based solutions were mentioned. In addition to the common assumption of high mental workload in emergency call centers and the need for AI systems to be understandable, there are notable differences in the belief systems of dispatchers and other experts. These differences often lead to a more negative attitude towards AI, which is influenced by job status, AI knowledge and qualifications. However, the ability to reflect can mitigate these limitations. AI can support dispatchers who have to handle complex tasks under time pressure, information deficits and uncertainty. Conclusion: In addition to the assumption of high mental workload and the need for understandable AI systems, dispatchers and other experts have different belief systems. These can lead to a negative attitude towards AI, which is influenced by job status, AI knowledge and qualifications, although reflection can help to mitigate this. AI can support dispatchers to handle complex tasks under pressure, information deficits and uncertainty. To prevent rejection of AI and raise awareness of its opportunities and risks, a comprehensive package of measures such as the one we have introduced is needed.

2024-08-23T00:00:00Z Seifert, Daniel Jöckel, Lisa Honroth, Thorsten Trendowicz, Adam Jedlitschk, Andreas Ciolkowski, Marcus https://fordatis.fraunhofer.de/handle/fordatis/411 2024-08-07T01:30:46Z 2024-08-01T00:00:00Z

Titel: Can Large Language Models (LLMs) compete with Human Requirement Reviewers? - Replication of an Inspection Experiment on Requirements Documents Datenautorinnen und Datenautoren: Seifert, Daniel; Jöckel, Lisa; Honroth, Thorsten; Trendowicz, Adam; Jedlitschk, Andreas; Ciolkowski, Marcus Zusammenfassung: This is the data for the paper "Can Large Language Models (LLMs) Compete with Human Requirements Reviewers? – Replication of an Inspection Experiment on Requirements Documents". It contains the source code of the experiment to make our work transparent and reproducible. Furthermore, it contains the evaluation results.

2024-08-01T00:00:00Z Hippler, Barbara Elsenbast, Christian https://fordatis.fraunhofer.de/handle/fordatis/386 2024-02-22T03:56:39Z 2022-02-19T00:00:00Z

Titel: Anrufgründe bei medizinischen Notfällen: Entwicklung eines strukturierten semantischen Modells auf Basis einer randomisierten Stichprobe medizinischer Hilfeersuche einer integrierten Rettungsleitstelle Datenautorinnen und Datenautoren: Hippler, Barbara; Elsenbast, Christian Zusammenfassung: Rettungsleitstellen sehen sich mit steigenden Herausforderungen durch kontinuierlich steigende Notrufzahlen konfrontiert. Zur besseren Strukturierung und Priorisierung der Notrufgespräche werden vielerorts standardisierte Abfragesysteme implementiert. Aktuelle Entwicklungen im Bereich der künstlichen Intelligenz eröffnen neue Möglichkeiten der Entscheidungsunterstützung von Disponierenden. Voraussetzung hierfür ist ein prozesshaftes Modell des Notrufdialogs. Anbei finden sich das Zusatzmaterial zur Forschung.

2022-02-19T00:00:00Z Elsenbast, Christian https://fordatis.fraunhofer.de/handle/fordatis/333 2023-06-29T01:31:01Z 2023-06-06T00:00:00Z

Titel: TAKAI Team‐Arbeit‐Kontext‐Analyse Inventar Datenautorinnen und Datenautoren: Elsenbast, Christian Zusammenfassung: Das Team‐Arbeit‐Kontext‐Analyse Inventar nach Hagemann et al. dient zur Erhebung des Arbeitskontextes von High-Responsibility-Teams.; The Team Work Context Analysis Inventory (Hagemann et al.) is designed to assess the work context of high responsibility teams.

2023-06-06T00:00:00Z