Perceived Usefulness of GenAI among University Students for Academic Support, Accessibility and Skill Development

Abstract

This study aimed to determine the perceptions of the usefulness and impact of GenAI tools for academic activities and to explore significant differences in the perceived usefulness of GenAI across demographic factors. This study comprises students enrolled in undergraduate and postgraduate courses at Martin Luther Christian University, Shillong. Data were collected from 301 students using a self-administered questionnaire. The statistical test showed that the majority of students fall within 18–24 years (80.7%), with a significant gender disparity, as females constituted 72.3% of the respondents. The majority of students (62.8%) were enrolled in postgraduate (PG) courses. Regarding the level of agreement on the perceived usefulness of AI, 60.1% agreed that AI tools helped students in academic activities, 61.8% believed that AI improved academic writing, 70.4% agreed that AI tools helped reduce errors in their academic work, and 73.4% agreed that AI assisted them in solving academic challenges. An independent sample t-test was conducted to examine the perceived usefulness of AI by gender and level of study. ANOVA was used to determine the significant differences across age groups. The findings indicated no statistically significant differences in the perceived usefulness of AI across these demographic variables.
The findings show that institutions should integrate AI into their curricula, redesign examinations to assess critical thinking, and develop students’ AI literacy. Adequate infrastructure and equitable access are also crucial. Clear AI policies should be established to guide the ethical use of AI, while developing practical AI-related skills can support students employability and prepare them for careers in an increasingly AI-driven workplace.
The study is limited to a single-institution sample, cross-sectional samples, and demographic imbalances, all of which constrain generalizability. Future research should adopt multi-institutional, longitudinal, and experimental designs and mixed-methods approaches to further identify GenAI usage patterns, examine learning outcomes, and ethical practices across diverse Indian higher-education contexts.