How do users perceive AI? A dual-process perspective on enhancement and replacement

How do users perceive AI? A dual-process perspective on enhancement and replacement

This study investigates the perceived value of artificial intelligence (AI) in human-machine interactions, focusing on the distinction between augmentation and replacement. Grounded in dual-process theory, we examine how different cognitive processing pathways influence AI perception, while social c...

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Bibliographic Details
Main Authors: Fan Li, Xiaotian Wei, Chenyang Wang, Chenyue Zhang, Guoming Yu, Ya Yang, Yuhan Liu
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Telematics and Informatics Reports
Subjects:
AI perception
Augmentation and replacement
Dual-process theory
Social cognitive theory
AI learning motivation
Online Access:http://www.sciencedirect.com/science/article/pii/S2772503025000416
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Summary:This study investigates the perceived value of artificial intelligence (AI) in human-machine interactions, focusing on the distinction between augmentation and replacement. Grounded in dual-process theory, we examine how different cognitive processing pathways influence AI perception, while social cognitive theory provides insight into users’ learning motivation. Using a two-wave survey study (N = 267), we explore (1) whether users perceive AI primarily as an augmentative or substitutive tool, (2) the impact of heuristic (Type 1) vs. analytical (Type 2) processing on AI value perception, and (3) the role of self-directed vs. socially influenced learning motivation in AI engagement. The results reveal that users predominantly view AI as an augmentative tool, with Type 2 processing leading to stronger AI value perceptions. Additionally, AI’s perceived value varies by content context, with profit-driven scenarios (e.g., advertising) amplifying both augmentation and replacement perceptions compared to non-profit contexts (e.g., news). Finally, self-directed AI usage behavior significantly predicts AI learning motivation, while external social influences play a lesser role. These findings underscore the importance of cognitive and social factors in shaping AI perception and emphasize the need for AI literacy to maximize its benefits in human-machine collaboration.
ISSN:2772-5030

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