Transformative Influence of LLM and AI Tools in Student Social Media Engagement: Analyzing Personalization, Communication Efficiency, and Collaborative Learning
Masoud Bashiri, Kamran Kowsari
TL;DR
The paper investigates how LLMs and AI tools reshape student engagement on educational social networks, leveraging UniversityCube data. It combines HDLTex, a hierarchical deep learning approach for text classification, with analyses of AI-enabled educational networks and ARIMA-based time-series forecasting, supplemented by AI-driven visualization techniques. Key contributions include demonstrating HDLTex accuracy gains over baselines, showing personalized learning, enhanced collaboration, real-time feedback, increased engagement, and wellbeing benefits, as well as forecasting shifts in monthly and yearly network usage. The findings support data-driven design of AI-assisted educational social networks that personalize learning, optimize resource allocation, and foster inclusive, mentorship-rich online communities. These insights have practical implications for educators, platform developers, and policymakers aiming to enhance student outcomes in the digital age.
Abstract
The advent of Large Language Models (LLMs) and Artificial Intelligence (AI) tools has revolutionized various facets of our lives, particularly in the realm of social media. For students, these advancements have unlocked unprecedented opportunities for learning, collaboration, and personal growth. AI-driven applications are transforming how students interact with social media, offering personalized content and recommendations, and enabling smarter, more efficient communication. Recent studies utilizing data from UniversityCube underscore the profound impact of AI tools on students' academic and social experiences. These studies reveal that students engaging with AI-enhanced social media platforms report higher academic performance, enhanced critical thinking skills, and increased engagement in collaborative projects. Moreover, AI tools assist in filtering out distracting content, allowing students to concentrate more on educational materials and pertinent discussions. The integration of LLMs in social media has further facilitated improved peer-to-peer communication and mentorship opportunities. AI algorithms effectively match students based on shared academic interests and career goals, fostering a supportive and intellectually stimulating online community, thereby contributing to increased student satisfaction and retention rates. In this article, we delve into the data provided by UniversityCube to explore how LLMs and AI tools are specifically transforming social media for students. Through case studies and statistical analyses, we offer a comprehensive understanding of the educational and social benefits these technologies offer. Our exploration highlights the potential of AI-driven tools to create a more enriched, efficient, and supportive educational environment for students in the digital age.