AUTHOR=Bo Duan , Ma’rof Aini Azeqa , Zaremohzzabieh Zeinab , Rongfeng Li , Danhe Zheng 

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

JOURNAL=Frontiers in Psychology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1596330

DOI=10.3389/fpsyg.2025.1596330

ISSN=1664-1078

ABSTRACT=IntroductionThis 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.MethodsParticipants were randomly assigned to conditions over a six-week intervention, with cognitive load and perceived reliability assessed as key mechanisms.ResultsHierarchical 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).DiscussionThis study suggests structured, reliable, cognitively demanding interventions mitigate polarization, offering theoretical insights into attitude formation and practical guidance for AI education and policy design.