Engagement modes and attitude polarization toward AI: the role of cognitive load and reliability among Chinese undergraduates

TL;DR

This study shows that structured AI education reduces attitude polarization among Chinese undergraduates, while self-directed learning increases it.

Contribution

The study introduces a novel framework linking cognitive load and perceived reliability to attitude polarization in AI education.

Findings

Structured courses reduced attitude polarization (β = −0.32, p < 0.01).

Self-directed research increased polarization (β = 0.45, p < 0.01).

Polarization correlated strongly with pre-to-post-test shifts (r = 0.68, p < 0.001).

Abstract

This experimental study investigates how engagement modes with AI-related information—structured courses, group discussions, and self-directed research—influence attitude polarization and policy preferences among 132 Chinese undergraduates at a northern Chinese university. Methods: Participants were randomly assigned to conditions over a six-week intervention, with cognitive load and perceived reliability assessed as key mechanisms.

Participants were randomly assigned to conditions over a six-week intervention, with cognitive load and perceived reliability assessed as key mechanisms.

Hierarchical regression revealed structured courses, marked by high cognitive load and reliability, significantly reduced polarization (β = −0.32, p < 0.01, η2 = 0.11), while self-directed research increased it (β = 0.45, p < 0.01, η2 = 0.15). Self-reported polarization strongly correlated with pre-to-post-test shifts (r = 0.68, p < 0.001), validating the General Attitudes Toward Artificial Intelligence Scale (GAAIS). Policy preferences mirrored these shifts, with structured courses fostering balanced stances (mean change = −0.15, SD = 0.40, p < 0.05).

This study suggests structured, reliable, cognitively demanding interventions mitigate polarization, offering theoretical insights into attitude formation and practical guidance for AI education and policy design.

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