posted on 2025-08-26, 03:15authored byStephen Bernard Elbourn
<p dir="ltr">In the contemporary educational landscape, Generative Artificial Intelligence (AI) has emerged as a transformative force, offering unprecedented opportunities for personalised learning while fundamentally disrupting traditional education paradigms. This research investigates the role of Generative AI in fostering entrepreneurial ambitions among high school students, who increasingly forgo conventional university degrees in favor of tailored, AI-driven learning experiences that equip them for entrepreneurial ventures at a younger age.</p><p dir="ltr">Using a robust methodology that integrates social media data analysis, this study employs sentiment analysis to capture public opinion, topic modeling to uncover emerging trends, and demographic analysis to understand the participation of various age groups and regions. Additional analyses include influencer engagement metrics, content typologies, hashtag connectivity, and temporal shifts in discussions to track the evolving discourse on AI in education. These analyses provide insights into how Generative AI content influences educational aspirations and career trajectories.</p><p dir="ltr">Key findings reveal that Generative AI significantly enhances accessibility to personalised learning and skill acquisition, fostering a new wave of young entrepreneurs. However, this paradigm shift challenges traditional university models and raises ethical concerns, such as equity and data privacy. This study emphasises the need for comprehensive policy frameworks to address these challenges and guide the integration of AI into educational systems. Additionally, predictive modeling highlights future trends, underscoring the pivotal role of AI in reshaping educational and career landscapes. These findings offer actionable insights for educators, policymakers, and technology developers seeking to navigate and leverage the transformative impact of Generative AI.</p>
History
Table of Contents
1 Introduction -- 2 Background and state-of-the-art -- 3 Methodology -- 4 Experiments and evaluation -- 5 Conclusion and future work -- Bibliography
Awarding Institution
Macquarie University
Degree Type
Thesis MRes
Degree
Master of Research
Department, Centre or School
School of Computing
Year of Award
2025
Principal Supervisor
Amin Beheshti
Additional Supervisor 1
Quanzheng Sheng
Rights
Copyright: The Author
Copyright disclaimer: https://www.mq.edu.au/copyright-disclaimer