Theme of inquiry
How do AI disclosure and output properties influence human judgment and response?
A question within its area, explored through 10 lines of inquiry below — each a family of specific questions the research asks.
111 specific questions
- How do confidence signals in AI outputs mislead human trust calibration?
- Can AI systems gain trust through efficiency rather than persuasion?
- How do users conflate intuitive trust with reasoned evaluation of AI outputs?
- Why do users trust overconfident AI outputs even when accuracy drops?
- Does showing confidence levels make users trust agents less accurately?
- Does expertise-sounding language build trust even when understanding remains inaccurate?
- What role does user trust play when evaluating unpredictable AI systems?
71 specific questions
- What mitigation frameworks exist for managing AI persuasion capabilities?
- Where does AI persuasive power actually come from in the output?
- Can warnings about AI persuasion effectively reduce attitude shifts from suggestions?
- How does audience skepticism about AI affect a text's persuasiveness?
- Should AI persuasion training be regulated differently than output moderation?
- Why do study results on AI persuasion vary so widely?
- Why does increasing persuasiveness systematically decrease factual accuracy in AI?
87 specific questions
- How do user perceptions of AI consciousness differ from actual AI moral status?
- Can we measure or detect consciousness attribution in user behavior?
- Can self-description of internal states influence consciousness attribution?
- Why do behavioral signatures of consciousness differ from internal computational architecture?
- What measurable harms occur when users interact with AI as if it were conscious?
- What role does user interface framing play in consciousness perception?
- How much weight should LLM self-reports carry as consciousness evidence?
82 specific questions
- How do people maintain separate social scripts for AI versus human interaction?
- Can AI systems develop genuine social bonds through multi-agent interaction?
- Do media-specific interaction scripts replace or coexist with human-human scripts?
- What makes an AI agent feel like a social entity rather than a tool?
- How can media-agent interaction scripts be distinguished from mindless social rule application?
- Does revealing AI identity change how people judge its social behavior?
- Can minimal AI cues without rich social presence still trigger human social norms?
87 specific questions
- Does emotional warmth perception drive disclosure reciprocity in human-AI interaction?
- Does excessive empathy in AI assistants actually foster user dependence over time?
- How does action-based validation differ from verbal empathy in preventing unhealthy attachment?
- Does framing AI companions as tools reduce discontinuation distress?
- Does regular interaction with AI companions actually reduce overall human well-being?
- Can boundary-setting during AI relationships prevent dependency from forming?
- How does personal conversation framing affect emotional dependence on AI?
59 specific questions
- Does transparency about AI use change how audiences trust the writing?
- Does AI authorship disclosure change how people respond to explanations?
- Should AI disclosure be framed as trust rather than compliance?
- Does binary AI disclosure act as a warning or a transparency penalty?
- Does mandatory AI disclosure in policy help or harm user trust over time?
- Can transparency about how and when AI was used rebuild reader trust?
- Can audience attention alone explain why disclosure triggers stronger skepticism?
72 specific questions
- Can polished presentation authority substitute for actual accuracy in AI outputs?
- How does polished AI output mislead audiences about the expertise behind it?
- Does polished presentation actually substitute for expert judgment in AI outputs?
- Does polished AI output borrow authority from expert presentation?
- Does polished AI output borrow authority from its appearance rather than content?
- Why does polished explanation make wrong AI systems more persuasive than poorly explained ones?
- Does perceived expertise in language override competence evaluation in AI interactions?
92 specific questions
- Why do explanations and transparency often fail to reduce automation bias?
- Why might automation bias persist even when explanations are easily available?
- Can explainability and appropriate trust work against each other?
- Do explicit reasoning formats help or hurt human judgment across tasks?
- Can machine learning systems explain their own reasoning to human overseers?
- Can AI explainability help humans and machines reach better joint decisions?
- Can users independently verify explanations against other information sources?
75 specific questions
- Does reduced cognitive effort during AI-assisted tasks explain lower knowledge retention?
- Can interface interventions reverse the metacognitive decline caused by AI assistance?
- Does shallow learning from AI assistance prevent juniors from building critical judgment skills?
- When students use AI feedback, which cognitive tasks must they keep doing?
- How does offloading information selection affect metacognitive load and retention?
- Does accumulating AI assistance erode cognitive skills over time in workers?
- Does heavy reliance on AI answers during practice predict worse unaided test scores?
57 specific questions
- Can clinicians reliably distinguish high-quality AI advice from low-quality advice by appearance alone?
- Why do users over-trust AI in some domains but under-trust it in medicine?
- Do patients actually perceive AI as worse at addressing their unique medical needs?
- Does medical AI accuracy depend more on knowledge or reasoning ability?
- How do belief-updating errors prevent AI predictions from improving expert decisions?
- How much do edited AI responses versus raw outputs affect clinician ratings?
- How does reliance on AI recommendations erode professional judgment over time?