Autonomous driving paper index
Uncanny Valley of Artificial Intelligence: Primary School Students' Fears of AI Attributing Human Characteristics
One-line summary
This qualitative study systematically investigates the artificial intelligence (AI) fears of primary school students through the conceptual lens of the “Uncanny Valley” theory.
Engineering notes
Key topics: autonomous driving, perception, control. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充:本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
This qualitative study systematically investigates the artificial intelligence (AI) fears of primary school students through the conceptual lens of the “Uncanny Valley” theory. As AI technologies become increasingly integrated into modern educational environments, understanding their profound psychological impact on young learners is fundamentally crucial. Employing a rigorous grounded theory design, in-depth interviews were conducted with eight students aged 10 to 11 to thoroughly explore their underlying perceptions. The data analysis revealed that students' anxieties regarding AI are primarily concentrated within three distinct themes: omniscience, permanent memory, and autonomous evolution. Findings indicate that students perceive AI not merely as a functional technological tool, but rather as an infallible, human-like competitor. This specific perception triggers profound anxieties concerning severe existential threats, the permanent loss of privacy, and an inevitable loss of human control over technology. Consequently, the study concludes that children experience a unique “Uncanny Valley of Mind”; their intense apprehension stems from the hyperrealistic cognitive capabilities of AI rather than its physical appearance. To effectively mitigate these complex anxieties, the study strongly recommends implementing comprehensive, multidimensional AI literacy programmes within schools. These targeted educational interventions must focus on demystifying the statistical nature of AI, highlighting its inherent limitations, and promoting human-centric pedagogical designs that prioritise uniquely human competencies over algorithmic outputs.
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