2 citations · 3 across the 3 of their papers we have counts for
5 papers
Within-Model vs Between-Prompt Variability in Large Language Models for Creative Tasks
Jennifer Haase, Jana Gonnermann-Müller, Paul H. P. Hanel +4
How much of LLM output variance is explained by prompts versus model choice versus stochasticity through sampling? We answer this by evaluating 12 LLMs on 10 creativity prompts wit…
S-DAT: A Multilingual, GenAI-Driven Framework for Automated Divergent Thinking Assessment
Jennifer Haase, Paul H. P. Hanel, Sebastian Pokutta
This paper introduces S-DAT (Synthetic-Divergent Association Task), a scalable, multilingual framework for automated assessment of divergent thinking (DT) -a core component of huma…
Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability
Jennifer Haase, Paul H. P. Hanel, Sebastian Pokutta
Following the widespread adoption of ChatGPT in early 2023, numerous studies reported that large language models (LLMs) can match or even surpass human performance in creative task…
Augmenting Coaching with GenAI: Insights into Use, Effectiveness, and Future Potential
Jennifer Haase
The integration of generative AI (GenAI) tools, particularly large language models (LLMs), is transforming professional coaching workflows. This study explores how coaches use GenA…
Human-AI Co-Creativity: Exploring Synergies Across Levels of Creative Collaboration
Jennifer Haase, Sebastian Pokutta
Human-AI co-creativity represents a transformative shift in how humans and generative AI tools collaborate in creative processes. This chapter explores the synergies between human…