On the Influence of Artificial Intelligence on Human Problem-Solving: Empirical Insights for the Third Wave in a Multinational Longitudinal Pilot Study

• URL: http://arxiv.org/abs/2511.11738v1
• Date: Thu, 13 Nov 2025 10:20:07 GMT
• Title: On the Influence of Artificial Intelligence on Human Problem-Solving: Empirical Insights for the Third Wave in a Multinational Longitudinal Pilot Study
• Authors: Matthias Huemmer, Theophile Shyiramunda, Franziska Durner, Michelle J. Cummings-Koether,
• Abstract summary: This article investigates the evolving paradigm of human-AI collaboration in problem-solving contexts.<n>Building upon previous waves, our findings reveal the consolidation of a hybrid problem-solving culture.<n>The study concludes that educational and technological interventions must prioritize verification scaffolds.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article presents the results and their discussion for the third wave (with n=23 participants) within a multinational longitudinal study that investigates the evolving paradigm of human-AI collaboration in problem-solving contexts. Building upon previous waves, our findings reveal the consolidation of a hybrid problem-solving culture characterized by strategic integration of AI tools within structured cognitive workflows. The data demonstrate near-universal AI adoption (95.7% with prior knowledge, 100% ChatGPT usage) primarily deployed through human-led sequences such as "Think, Internet, ChatGPT, Further Processing" (39.1%). However, this collaboration reveals a critical verification deficit that escalates with problem complexity. We empirically identify and quantify two systematic epistemic gaps: a belief-performance gap (up to +80.8 percentage points discrepancy between perceived and actual correctness) and a proof-belief gap (up to -16.8 percentage points between confidence and verification capability). These findings, derived from behavioral data and problem vignettes across complexity levels, indicate that the fundamental constraint on reliable AI-assisted work is solution validation rather than generation. The study concludes that educational and technological interventions must prioritize verification scaffolds (including assumption documentation protocols, adequacy criteria checklists, and triangulation procedures) to fortify the human role as critical validator in this new cognitive ecosystem.

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