TEAMING AND INCLUSIVE MODELING

Stories are conveyed through dialogue, models, and collaborations. Collaborations are crucial for establishing trust and evaluating the compatibility of different viewpoints. Our research relies on collaborations with social scientists, scholars in the humanities, computer scientists, and other relevant experts. Moreover, we focus on creating inclusive approaches and tools that facilitate effective communication not only among academics but also among students and neurodiverse individuals.

Research Areas

Biometrics and AI

Studies how humans and AI systems jointly perform tasks, make decisions, and enhance team performance.

Designs AI systems that help in M&S.
Investigates trust, coordination, and shared situational awareness between humans and AI.

Inclusive Modeling Approaches

Builds models that integrate diverse knowledge, experiences, and viewpoints for more equitable outcomes.

Incorporates social, cultural, and contextual factors into modeling frameworks.
Ensures accessibility and representation of underrepresented groups in data and analysis.

Interdisciplinary Team Collaboration

Examines how experts from different fields work together, bridging disciplinary gaps to solve complex problems.

Studies communication dynamics across disciplines.
Develops frameworks to align differing methodologies, assumptions, and terminologies.

Selected Publications

Model Co-creation from a Modeler’s Perspective: Lessons Learned from the Collaboration Between Ethnographers and Modelers

Authors: Jose J Padilla, Erika Frydenlund, Hege Wallewik, Hanne Haaland

Tags: Inclusive Modeling Approaches, Interdisciplinary Team Collaboration

This paper describes a model co-creation process that integrates qualitative and quantitative methodologies, epistemologies, and ontologies to collaboratively develop an ethnographic simulation of the refugee situation in Lesbos, Greece.
The collaborative modeling process helped ethnographers refine research questions, adjust modeling scope, and identify challenges in translating rich field observations into simulation models, while facilitating new lines of inquiry through variable elicitation and prototype development.

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The use of artificial intelligence to detect students' sentimentsand emotions in gross anatomy reflections

Authors: Krzysztof J Rechowicz, & Carrie A Elzie

Tags: Biometrics and AI, Interdisciplinary Team Collaboration

This study applies natural language processing (NLP) and sentiment analysis to examine health professional students' reflective writings in gross anatomy, aiming to efficiently analyze the complex emotions embedded in student reflections about themselves and their anatomical donors.
Analysis of 1,365 reflections revealed predominantly positive sentiments, with trust, joy, and anticipation being the most frequent emotions across various body regions; NLP allowed the detection of shared emotional patterns between self-reflections and donor reflections, offering insights into students' person-centered perspectives.