Perceived Usefulness, Trust, and Behavioral Intention: A Study on College Student User Adoption Behaviors of Artificial Intelligence Generated News Based on Technology Acceptance Model

This study intends to identify the critical factors that shape college students' adoption of AI-generated news, with a specific focus on integrating Big Data methodologies into the Technology Acceptance Model (TAM) framework. Building on TAM, the research incorporates "trust" as a core variable to develop a dual-path theoretical model that combines technological cognition (e.g., perceived usefulness, perceived ease of use) and psychological emotions. Unlike traditional TAM-based studies relying solely on questionnaire data, this research enriches its data sources by leveraging Big Data techniques-including the collection and analysis of college students' real-time behavioral data (e.g., AI news reading duration, sharing frequency, source verification clicks) and unstructured text data (e.g., sentiment orientation in comment sections)-to complement the survey data from 300 college students. Through a questionnaire survey of 300 college students and data analysis using the structural equation model, the study found that trust has the strongest direct positive impact on the willingness to use (β = 0.49, <i>p</i> < 0.001), and its influence is significantly greater than perceived usefulness (β = 0.35, <i>p</i> < 0.001). Meanwhile, although perceived ease of use does not directly affect the willingness to use, it has significant indirect effects by enhancing trust and perceived usefulness. The results show that in the AI news context with high-risk perception, trust is a more crucial psychological mechanism than traditional technological cognitive factors. These findings have expanded the explanatory boundaries of the TAM model in new technology fields and provided empirical evidence and practical inspiration for AI developers to optimize system credibility and for educators to conduct algorithmic literacy training.