Knowing More and Not Being Anxious Are Different Things.
The Core Threat from Generative AI Is the Relativization of Human Uniqueness Rather Than Technical Errors.

Paper: "What Does University Students Anxiety Look Like in the Generative AI Era?" (Choi Jeong-won et al., Konyang University / EBP Korea, 2025). Research performed cluster analysis on six subfactors (reliability, excessive dependency, job reduction, unlimited competition, loss of individuality, data security) using the GAIAx-CS scale. Finding: generative AI anxiety is not simple fear but a complex phenomenon where career anxiety, identity anxiety, ethical anxiety, and concern about cognitive ability loss overlap. The AI literacy paradox: higher AI literacy does NOT produce lower anxiety -- high-literacy users show higher anxiety in specific dimensions (particularly "loss of individuality"). Mechanism: users who understand AI deeply enough to use it effectively also understand deeply what it might displace. Three anxiety cluster types: (1) Functional anxious -- worried about getting specific tasks wrong; (2) Identity anxious -- worried about what AI capabilities mean for their unique value; (3) Socially anxious -- worried about competitive disadvantage if others use AI better. Implication: teaching AI skills without addressing the identity and meaning questions AI raises may increase rather than decrease anxiety in capable students.