Prof. Dr. Thomas Kosch

Die Forschung von Prof. Dr. Thomas Kosch konzentriert sich auf die Entwicklung intelligenter Benutzerschnittstellen und die Anwendung von Künstlicher Intelligenz, insbesondere Large Language Models, in Human-Computer Interaction. Ein zentrales Projekt ist easyTEM, das KI-Methoden wie Deep Reinforcement Learning nutzt, um den Betrieb von Transmissionselektronenmikroskopen zu automatisieren und ressourceneffizienter zu gestalten. Weitere Arbeiten untersuchen die Variabilität und Stabilität von LLMs bei kreativen Aufgaben sowie deren Einsatz in Requirements Engineering und Faktenchecks gegen Desinformation. Empirische Studien mit Eye-Tracking und EEG-Messungen analysieren kognitive Belastung, selektive Exposition und emotionale Reaktionen auf interaktive Systeme. Die Forschung verbindet technische Optimierung mit psychologischen und ethischen Aspekten der Mensch-Maschine-Interaktion.

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• ClaimGuard: Kollaborative Mensch-KI-Faktenchecks zur Abwehr von Desinformation

  Quelle ↗

  Förderer: Bundesministerium für Forschung, Technologie und Raumfahrt Zeitraum: 04/2026 - 03/2029 Projektleitung: Prof. Dr. Thomas Kosch

• easyTEM: Entwicklung von ressourceneffizienter Transmissionselektronenmikroskopie zur Demokratisierung ihres Einsatzes in der Materialforschung

  Quelle ↗

  Förderer: DFG Sachbeihilfe Zeitraum: 06/2026 - 05/2029 Projektleitung: Prof. Dr. Thomas Kosch

• Entwicklung von Ressourceneffizenter Transmissionselektronenmikroskopie zur Demokratisierung ihres Einsatzes in der Materialforschung

  Quelle ↗

  Förderer: DFG Sachbeihilfe Zeitraum: 04/2026 - 03/2029 Projektleitung: Prof. Christoph T. Koch, PhD, Prof. Dr. Thomas Kosch, Prof. Dr. Ulf Leser

• A Survey on Measuring Cognitive Workload in Human-Computer Interaction

  2023

  ACM Computing Surveys · DOI

  The ever-increasing number of computing devices around us results in more and more systems competing for our attention, making cognitive workload a crucial factor for the user experience of human-computer interfaces. Research in Human-Computer Interaction (HCI) has used various metrics to determine users’ mental demands. However, there needs to be a systematic way to choose an appropriate and effective measure for cognitive workload in experimental setups, posing a challenge to their reproducibility. We present a literature survey of past and current metrics for cognitive workload used throughout HCI literature to address this challenge. By initially exploring what cognitive workload resembles in the HCI context, we derive a categorization supporting researchers and practitioners in selecting cognitive workload metrics for system design and evaluation. We conclude with three following research gaps: (1) defining and interpreting cognitive workload in HCI, (2) the hidden cost of the NASA-TLX, and (3) HCI research as a catalyst for workload-aware systems, highlighting that HCI research has to deepen and conceptualize the understanding of cognitive workload in the context of interactive computing systems.

• Interactive worker assistance

  2016

  DOI

  With increasing complexity of assembly tasks and an increasing number of product variants, instruction systems providing cognitive support at the workplace are becoming more important. Different instruction systems for the workplace provide instructions on phones, tablets, and head-mounted displays (HMDs). Recently, many systems using in-situ projection for providing assembly instructions at the workplace have been proposed and became commercially available. Although comprehensive studies comparing HMD and tablet-based systems have been presented, in-situ projection has not been scientifically compared against state-of-the-art approaches yet. In this paper, we aim to close this gap by comparing HMD instructions, tablet instructions, and baseline paper instructions to in-situ projected instructions using an abstract Lego Duplo assembly task. Our results show that assembling parts is significantly faster using in-situ projection and locating positions is significantly slower using HMDs. Further, participants make less errors and have less perceived cognitive load using in-situ instructions compared to HMD instructions.