Abstract
As generative artificial intelligence becomes increasingly embedded in higher education, international students are using AI not only to obtain information, but also to support language, expression and academic communication. However, limited attention has been given to how students assess the credibility of AI-generated content and how trust in AI influences their confidence in cross-cultural academic settings.
This session explores the relationship between AI trust, perceived credibility and communication confidence among international students through case studies from universities in China and Singapore. It examines whether students who feel less secure about their language ability or academic expression are more likely to rely on AI-generated content, and whether this reliance strengthens participation and confidence or creates new forms of dependence.
The session will also consider how cultural background, prior educational experience and academic expectations shape students’ judgement of AI-generated information. By comparing the two institutional contexts, the presenters will identify common patterns, differences and practical implications for student support.
Participants will gain insights into how universities can promote critical AI literacy while supporting international students’ language confidence, intercultural communication and independent academic judgement. The session argues for a human-centred approach that helps students use AI as a supportive tool without replacing personal voice, critical thinking or academic responsibility.
Learning objectives
- Examine how international students assess the credibility and reliability of AI-generated content.
- Understand how trust in AI may influence students’ confidence in cross-cultural academic communication.
- Identify how language insecurity, cultural background and prior educational experience may shape students’ reliance on AI.
- Distinguish between productive AI support and overdependence that may weaken independent judgement or personal voice.
- Compare emerging patterns from university cases in China and Singapore.
- Apply practical approaches to support critical AI literacy, communication confidence, academic integrity and human-centered learning.
Target audience
Information will be provided at a later stage