The problem of AI-generated misinformation on social media platforms has transformed how users encounter, interpret, and circulate news. This creates additional challenges in evaluating the effectiveness of communication skills in the modern digital environment. This study describes the development and validation process of the Fake News Identification Scale (FNIS). It is an assessment tool designed to evaluate university students' ability to identify fake news on AI-mediated social media. Drawing on research on misinformation, social media use, and digital media literacy, the FNIS conceptualises fake news identification through three dimensions: Content Evaluation (critical appraisal of informational accuracy and source credibility), Appearance Assessment (attention to visual, interface, and stylistic cues shaped by platform design), and Motivation Awareness (recognition of ideological, economic, and algorithmic drivers behind misinformation, including generative AI). A 48-item instrument was administered to 217 tertiary students in Hong Kong. Exploratory factor analysis supported a three-factor structure consistent with the proposed framework, and reliability analyses indicated high internal consistency across all subscales.Results demonstrated a significant difference in respondents' abilities: students performed strongest on Appearance Assessment but were substantially weaker on Motivation Awareness, particularly regarding AI-generated content and platform-level dynamics. The study highlights the need for targeted, technology-focused media literacy interventions in higher education that address AI, algorithms, and social media architectures as key conditions shaping contemporary misinformation flows in the AI environment.

Authors:
William Ko-Wai Tang, Hong Kong Metropolitan University, Hong Kong

About the Presenter(s)
Dr William Ko-Wai Tang is a University Assistant Professor/Lecturer at Hong Kong Metropolitan University in Hong Kong

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