Unveiling Student Sentiment Dynamics Toward AI-Based Education Through Statistical Analysis and Monte Carlo Simulation

Abstract

This study explores the multifaceted dynamics of student sentiment towards artificial intelligence (AI)-based education by integrating sentiment analysis techniques with statistical methods, including Monte Carlo simulations and decision tree modelling, alongside qualitative grounded theory analysis. Data were collected from 540 university students, whose responses to open-ended and scale-based questions were systematically analysed to capture the nuances of their perceptions regarding the transformative potential and inherent challenges of AI in educational settings. Quantitatively, sentiment scores were derived using GPT-4, categorised into positive, neutral and negative bins, and further examined through descriptive statistics, one-way ANOVA and Scheff & eacute; post hoc tests. Monte Carlo simulations provided a resilient estimation of sentiment distributions, while decision tree analysis elucidated key demographic and attitudinal predictors of AI adoption, particularly highlighting the roles of age and ethical perceptions. Qualitatively, grounded theory was employed to extract emergent themes that reflect both the enthusiasm for personalised, efficient learning and the concerns over ethical dilemmas, social isolation and diminished teacher-student interactions. The findings reveal a dual-edged view of AI-based education, while a majority of students acknowledge its advantages for enhancing learning efficiency and access to information.