AI receptivity

AI receptivity is the willingness, preference, or readiness to use AI products, services, or task execution. It is not the same construct as ai-literacy. Receptivity can come from informed confidence, convenience, social pressure, perceived usefulness, novelty, awe, or misunderstanding; the source of willingness matters for adoption quality.

2025-tully-ai-literacy-receptivity shows why this construct needs its own page. Across cross-country data and six preregistered studies, lower AI literacy often predicted greater AI receptivity. The proposed mechanism was magical perception and awe when AI appeared to perform tasks associated with distinctly human attributes, rather than stronger belief in AI capability or lower fear of AI.

2026-almog-barriers-ai-adoption adds a workplace visibility case where receptivity becomes contingent on evaluation. Workers did not simply reject AI recommendations; they followed them less when reliance was visible to an HR evaluator, lowering accuracy. Receptivity therefore has a social-expression layer: willingness to use AI privately may diverge from willingness to be seen using AI.

That distinction matters for AI adoption. High receptivity is not automatically good, and low visible use is not automatically technical resistance. Receptivity may indicate openness to useful experimentation, anthropomorphic over-trust, poor calibration, consumer-facing appeal built on mystification, or fear that AI use will be read as weak judgment. Adoption work therefore needs to ask not only whether people are receptive, but why and under whose gaze.

2026-zhu-choose-your-agent adds a control-preference case. Participants preferred the Advisor modality even though Delegate access produced stronger welfare results in the bargaining game. Receptivity can therefore favor interfaces that preserve agency, veto power, or a felt sense of authorship, even when a more autonomous mode produces better objective outcomes in the studied environment.

2025-welch-underrepresented-employee-ai-receptivity adds an inclusion boundary to this account. Its narrative review argues that receptivity measures and rollout models can hide intersectional differences in how workers encounter transparency, psychological safety, ethical accountability, and culturally competent communication. The claim is not yet causal evidence that stakeholder engagement raises receptivity; it is a warning that treating receptivity as one homogeneous adoption disposition can erase the groups most exposed to unequal or exclusionary AI transitions.

Connections

• Relates to ai-adoption because receptivity can precede trial, usage, and organizational adoption, but does not by itself show durable work-system change.
• Relates to human-ai-collaboration because willingness to involve AI does not guarantee calibrated delegation, verification, or accountability.
• Relates to automation-complacency because receptivity driven by awe or magical thinking may increase acceptance without increasing oversight.
• Relates to construct-validity because receptivity measures can conflate intention, preference for AI over humans, past use, perceived usefulness, trust, and readiness.

Contradictions & open questions

• 2025-tully-ai-literacy-receptivity complicates the assumption that more AI knowledge produces more willingness to use AI; in some domains, mystification may increase receptivity.
• Receptivity may reverse by task framing. For tasks associated with shared rather than distinctly human attributes, higher AI literacy can predict greater AI receptivity.
• Receptivity is not adoption quality. Greater willingness to use AI could mean useful exploration, overreliance, susceptibility to anthropomorphic marketing, or premature delegation depending on stakes and governance.
• Receptivity is not always stable across visibility conditions. 2026-almog-barriers-ai-adoption suggests workers can reduce AI use when it is attached to evaluator judgments, even when use would improve task performance.
• The workplace version remains underdeveloped: consumer or student receptivity may not translate into sustained AI use inside accountable jobs, teams, or organizations.