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The guarded engagement loop: risk salience and interaction-driven underperformance in generative AI adoption.
Connie Mosher Syharat1, Arash Zaghi2, Sarira Motaref2
1College of Engineering, University of Connecticut, Storrs, CT, United States.
Frontiers in Research Metrics and Analytics
|July 6, 2026
Summary
Generative AI adoption depends on user interaction, not just technical skills. Risk perceptions influence engagement, affecting AI performance and trust through a "guarded engagement loop."
Area of Science:
- Artificial Intelligence
- Human-Computer Interaction
- Sociotechnical Systems
Background:
- Generative AI adoption is often viewed as a technical skill acquisition problem.
- This perspective neglects the crucial role of user interaction in shaping AI performance.
- Large language model (LLM) output quality is sensitive to user engagement depth and strategy.
Purpose of the Study:
- To propose a conceptual framework explaining how risk perceptions influence user engagement with generative AI.
- To introduce the 'guarded engagement loop' as a multilevel feedback mechanism.
- To explore implications for AI governance, design, and fostering calibrated reliance.
Main Methods:
- Conceptual framework development.
- Drawing on theories of trust in automation, privacy calculus, algorithm aversion, and risk amplification.
- Analysis of micro (individual interaction) and macro (societal discourse) levels.
Main Results:
- Risk salience (privacy, safety, ethics) can lead to guarded interaction strategies (reduced disclosure, limited iteration).
- Constrained interactions can decrease LLM output quality and increase errors, eroding trust.
- Macro-level withdrawal can amplify risk narratives and perceptions of AI harm.
Conclusions:
- Generative AI adoption is a feedback process where risk perceptions shape interactions, influencing performance and trust calibration.
- The 'guarded engagement loop' framework offers insights into user behavior and AI system dynamics.
- Understanding this loop is crucial for designing AI systems that enable bounded openness and calibrated reliance.
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