Rethinking AI Literacy Education in Higher Education: Bridging Risk Perception and Responsible Adoption

TL;DR

This study investigates how AI students perceive risks and their willingness to adopt AI, revealing gaps between risk awareness and responsible use, and suggesting tailored AI literacy strategies.

Contribution

It uncovers discrepancies between explicit risk perception and scenario-based risk assessment among AI students, informing targeted AI literacy education.

Findings

Students worry more about explicit risks than scenario-based risks.

Risk perception inversely correlates with willingness to adopt AI.

Gender differences in risk awareness are reduced by technical education.

Abstract

As AI becomes increasingly embedded across societal domains, understanding how future AI practitioners, particularly technology students, perceive its risks is essential for responsible development and adoption. This study analyzed responses from 139 students in Computer Science, Data Science/Data Analytics, and other disciplines using both explicit AI risk ratings and scenario-based assessments of risk and adoption willingness. Four key findings emerged: (1) Students expressed substantially higher concern for concrete, explicitly stated risks than for abstract or scenario-embedded risks; (2) Perceived risk and willingness to adopt AI demonstrated a clear inverse relationship; (3) Although technical education narrowed gender differences in risk awareness, male students reported higher adoption willingness; and (4) A form of "risk underappreciation" was observed, wherein students in AI-related specializations showed both elevated explicit risk awareness and higher willingness to adopt AI, despite lower recognition of risks in applied scenarios. These findings underscore the need for differentiated AI literacy strategies that bridge the gap between awareness and responsible adoption and offer valuable insights for educators, policymakers, industry leaders, and academic institutions aiming to cultivate ethically informed and socially responsible AI practitioners.