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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

This summary is machine-generated.

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."

Keywords:

algorithm aversion generative AI adoption guarded engagement loop human-AI interaction large language models risk salience trust in automation

Related Experiment Videos

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.