People with lower AI literacy are more receptive to AI technology, contrary to common expectations, due to their perception of AI as "magical."

Objective: To investigate which types of consumers show greater receptivity to AI and understand the underlying psychological mechanisms driving this receptivity.

Methods:

• Seven studies including cross-country data analysis
• Multiple measurements of AI literacy using third-party measures, 25-item measure, and 17-item measure
• Assessment of AI receptivity through various metrics including general adoption readiness, usage frequency, and task preferences
• Surveys and experiments with diverse populations including students, online participants, and citizens across countries

Key Findings:

• Lower AI literacy consistently predicts higher AI receptivity across different populations and contexts
• This relationship is explained by perceptions of AI as magical and feelings of awe among those with lower AI literacy
• The effect is stronger for tasks perceived to require distinctly human attributes
• The relationship reverses for tasks associated with shared human-AI attributes
• People with lower AI literacy show greater receptivity despite viewing AI as less capable and having more fears about its impact

Implications:

• Companies should consider targeting consumers with lower AI literacy for AI-based products
• Marketing strategies should maintain an element of mystery around AI rather than demystifying it
• Educational efforts to increase AI literacy might unintentionally reduce adoption rates
• Product development should focus on meeting the needs of consumers with lower AI literacy
• Different marketing approaches are needed for different consumer segments based on AI literacy levels

Limitations:

• Difficulty in causally manipulating AI literacy in short timeframes
• Potential correlation between AI literacy measures and other individual differences
• Limited exploration of sub-factors within AI literacy
• Focus on current perceptions without longitudinal data

Future Directions:

• Examine how AI literacy initiatives causally affect AI receptivity
• Study the relationship between literacy and receptivity longitudinally
• Investigate the impact of shifting norms and expectations on AI adoption
• Develop more distinct measures of AI literacy subcomponents
• Explore the long-term implications of education efforts on AI adoption

Title and Authors: "Lower Artificial Intelligence Literacy Predicts Greater AI Receptivity" by Stephanie Tully, Chiara Longoni, and Gil Appel

Published On: December 13, 2024