Bridging the AI Divide: A Study on Perceptions and Usage of Artificial Intelligence Tools Among Graduate Students in Nepal

Abstract

Background: The integration of Artificial Intelligence (AI) into higher education presents both opportunities and challenges, potentially exacerbating existing digital divides. In Nepal, disparities in digital access and skills persist, but limited research exists on how these divides manifest in the perceived usage and attitudes toward AI tools among graduate students, a key group for national AI adoption.

Objectives: This study aimed to investigate the dimensions of the digital divide in the use of AI tools among graduate students in Nepal, focusing on their perceptions, confidence, and trust, and to examine potential variations based on demographic factors such as gender and field of study.

Methods: A quantitative, descriptive-explanatory study was conducted with 226 graduate students from various disciplines within Kathmandu Valley, selected via simple random sampling. Data were collected through a structured questionnaire. Reliability was confirmed with a Cronbach's Alpha of .750, and construct validity was established through factor analysis. Data were analyzed using descriptive statistics and an independent samples t-test.

Findings: Results indicated generally positive perceptions of AI’s utility, with students acknowledging awareness of beneficial tools. However, a significant confidence and trust gap was identified, with notable portions expressing neutrality or doubt regarding the correctness of AI information and their own confidence in using AI for academic work. No statistically significant gender difference in perceptions was found. Variation was observed across academic disciplines, suggesting field-specific relevance as a potential factor.

Conclusion: The study concludes that the digital divide in Nepal’s AI era is evolving beyond basic access into a second-level divide characterized by disparities in digital competence, critical evaluation skills, and trust in AI systems. Demographic factors like gender appear less influential than discipline-specific exposure and practical, critical literacy.

Implications: The findings underscore the need for educational policies and pedagogical strategies that move beyond providing access to focus on developing AI literacy, critical thinking, and discipline-specific competencies to ensure equitable and effective AI adoption in higher education.