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Related Experiment Videos

How AI-Generated Content Shapes User Trust: The Roles of Cognitive Processing, Perceived Risk, and Transparency

Junting Liu1, Liangdong Lu1

  • 1Business School, Hohai University, Nanjing 211100, China.

Behavioral Sciences (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

Generative artificial intelligence (AI) transparency labels and presentation quality impact user trust in AI-generated videos. High quality reduces heuristic processing, while authenticity influences systematic processing and trust.

Keywords:
AI-generated contentadoption intentionartificial intelligencecognitive processingperceived risksharing intentiontransparency labelstrust

Related Experiment Videos

Area of Science:

  • Human-Computer Interaction
  • Artificial Intelligence Ethics
  • Cognitive Psychology

Background:

  • Generative AI is widely used for multimedia content creation, necessitating research into user evaluation and response.
  • Existing studies on AI transparency and adoption lack understanding of cognitive mechanisms linking external cues to user trust and behavior.
  • The heuristic-systematic model (HSM) provides a framework for understanding how users process information and form judgments.

Purpose of the Study:

  • To investigate the influence of AI transparency labels and presentation quality on cognitive processing, perceived authenticity, trust, and perceived risk in AI-generated video content.
  • To examine the relationships between perceived authenticity, cognitive processing (heuristic vs. systematic), trust, and behavioral intentions (adoption and sharing).
  • To understand the role of perceived risk in user responses to AI-generated content.

Main Methods:

  • A 2x2 between-subjects experimental design with 617 participants.
  • Manipulation of AI transparency labels (present vs. absent) and presentation quality (high vs. low).
  • Measurement of cognitive processing, perceived authenticity, trust, perceived risk, adoption intention, and sharing intention.

Main Results:

  • AI transparency labels decreased perceived authenticity but did not directly impact trust or adoption intention.
  • Higher presentation quality led to reduced reliance on heuristic processing.
  • Perceived authenticity correlated with lower cognitive load and increased systematic processing; systematic processing positively predicted trust, while heuristic processing negatively predicted trust.
  • Trust positively influenced adoption and sharing intentions, whereas perceived risk negatively affected these intentions.

Conclusions:

  • Cognitive processing and trust-risk dynamics are crucial in shaping user responses to AI-generated content.
  • Transparency labels affect perceived authenticity, influencing cognitive pathways to trust.
  • Presentation quality moderates heuristic processing, impacting trust formation.
  • Findings offer insights for designing transparent AI systems and fostering user trust in AI-mediated environments.