The threshold-like usage pattern at the lower end of the scale is strongest for training interest and accuracy trust rather than uniform across outcomes¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? The early threshold-like pattern in the usage gradients is most visible for training interest and accuracy trust, while critical-thinking risk shifts mainly among daily users. → Yongkyung Oh 2026

Evidence¶

Yongkyung Oh 2026¶

Yongkyung Oh, Lynn Talton, Alex Bui. (2026). Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction. KDD 2026 AI for Education Day, at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '26). https://arxiv.org/abs/2606.18548 doi:10.48550/arxiv.2606.18548

q2 · i? · associational · r1

Authors' interpretation of the ordered-median gradient panels (Figure 2) in the appendix, which visualize the same 93-trainee survey medians across the intake features; the pattern distinguishes non-users from sustained users but does not support a uniform threshold across outcomes.

"The early threshold-like pattern is strongest for training interest and accuracy trust, but it is not uniform across all five outcomes."

Discussion¶

Learner Variables¶

• Interest — outcome: instruction changes it

Related Claims¶

• The cohort's baseline perception profile shows high endorsement of critical-thinking risk and training interest and substantially more cautious trust for complex-task use than for accuracy — related
• Self-reported LLM usage frequency shows Holm-corrected associations with all five baseline AI perception outcomes, strongest for complex-task trust, accuracy trust, and critical-thinking risk — related
• Self-rated LLM familiarity is a weaker secondary intake signal, surviving Holm correction for three of five outcomes with a bottom-anchored pattern — related