arXiv subjects

Matthias Huemmer

Publications and source records attributed to Matthias Huemmer.

4 recordsLinked to original sources

arXiv2026

AI, Metacognition, and the Verification Bottleneck: A Three-Wave Longitudinal Study of Human Problem-Solving

This longitudinal pilot study tracked how generative AI reshapes problem-solving over six months across three waves in an academic setting. AI integration reached saturation by Wave 3, with daily use rising from 52.4% to 95.7% and ChatGPT adoption from 85.7% to 100%. A dominant hybrid workflow increased 2.7-fold, adopted by 39.1% of participants. The verification paradox emerged: participants relied most heavily on AI for difficult tasks (73.9%) yet showed declining verification confidence (68.1%) where performance was worst (47.8% accuracy on complex tasks). Objective performance declined systematically: 95.2% to 81.0% to 66.7% to 47.8% across problem difficulty, with belief-performance gaps widening to 34.6 percentage points. This indicates a fundamental shift where verification, not solution generation, became the bottleneck in human-AI problem-solving. The ACTIVE Framework synthesizes findings grounded in cognitive load theory: Awareness and task-AI alignment, Critical verification protocols, Transparent human-in-the-loop integration, Iterative skill development countering cognitive offloading, Verification confidence calibration, and Ethical evaluation. The authors provide implementation pathways for institutions and practitioners. Key limitations include sample homogeneity (academic cohort only, convenience sampling) limiting generalizability to corporate, clinical, or regulated professional contexts; self-report bias in confidence measures (32.2 percentage point divergence from objective performance); lack of control conditions; restriction to mathematical/analytical problems; and insufficient timeframe to assess long-term skill trajectories. Results generalize primarily to early-adopter, academically affiliated populations. Causal validation requires randomized controlled trials.

cs.CY↗

arXiv2025

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

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.

cs.CY↗

arXiv2025

Cultural Dimensions of Artificial Intelligence Adoption: Empirical Insights for Wave 1 from a Multinational Longitudinal Pilot Study

The swift diffusion of artificial intelligence (AI) raises critical questions about how cultural contexts shape adoption patterns and their consequences for human daily life. This study investigates the cultural dimensions of AI adoption and their influence on cognitive strategies across nine national contexts in Europe, Africa, Asia, and South America. Drawing on survey data from a diverse pilot sample (n = 21) and guided by cross-cultural psychology, digital ethics, and sociotechnical systems theory, we examine how demographic variables (age, gender, professional role) and cultural orientations (language, values, and institutional exposure) mediate perceptions of trust, ethical acceptability, and reliance on AI. Results reveal two key findings: First, cultural factors, particularly language and age, significantly affect AI adoption and perceptions of reliability with older participants reporting higher engagement with AI for educational purposes. Second, ethical judgment about AI use varied across domains, with professional contexts normalizing its role as a pragmatic collaborator while academic settings emphasized risks of plagiarism. These findings extend prior research on culture and technology adoption by demonstrating that AI use is neither universal nor neutral but culturally contingent, domain-specific, and ethically situated. The study highlights implications for AI use in education, professional practice, and global technology policy, pointing at actions that enable usage of AI in a way that is both culturally adaptive and ethically robust.

cs.CY↗

arXiv2025

Exploring Artificial Intelligence and Culture: Methodology for a comparative study of AI's impact on norms, trust, and problem-solving across academic and business environments

This paper proposes a rigorous framework to examine the two-way relationship between artificial intelligence (AI), human cognition, problem-solving, and cultural adaptation across academic and business settings. It addresses a key gap by asking how AI reshapes cognitive processes and organizational norms, and how cultural values and institutional contexts shape AI adoption, trust, and use over time. We employ a three-wave longitudinal design that tracks AI knowledge, perceived competence, trust trajectories, and cultural responses. Participants span academic institutions and diverse firms, enabling contextual comparison. A dynamic sample continuous, intermittent, and wave-specific respondents mirrors real organizational variability and strengthens ecological validity. Methodologically, the study integrates quantitative longitudinal modeling with qualitative thematic analysis to capture temporal, structural, and cultural patterns in AI uptake. We trace AI acculturation through phases of initial resistance, exploratory adoption, and cultural embedding, revealing distinctive trust curves and problem-solving strategies by context: academic environments tend to collaborative, deliberative integration; business environments prioritize performance, speed, and measurable outcomes. Framing adoption as bidirectional challenges deterministic views: AI both reflects and reconfigures norms, decision-making, and cognitive engagement. As the first comparative longitudinal study of its kind, this work advances methodological rigor and offers actionable foundations for human-centred, culturally responsive AI strategies-supporting evidence-based policies, training, and governance that align cognitive performance, organizational goals, and ethical commitments.

cs.HC↗