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arXiv 2407.15012cs.CYcs.CLcs.LGcs.SI
大语言模型与人工智能工具对学生社交媒体参与的变革性影响：个性化、沟通效率与协作学习分析
Transformative Influence of LLM and AI Tools in Student Social Media Engagement: Analyzing Personalization, Communication Efficiency, and Collaborative Learning
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.
PRIME: Large Language Model Personalization with Cognitive Dual-Memory and Personalized Thought Process
Computer Science and Engineering, University of Michigan, Ann Arbor, MI（计算机科学与工程系，密歇根大学，安娜堡分校）；Department of Political Science, Northeastern University, Boston, MA（政治学系，东北大学，波士顿分校）
该研究提出融合认知双记忆与个性化慢思考的 PRIME 框架，并构建基于 Reddit CMV 的长上下文个性化数据集，实验证明其可捕捉动态用户偏好。
Personalized Graph-Based Retrieval for Large Language Models
University of California Santa Cruz（加州大学圣克ruz分校）；Meta AI；Adobe Research（Adobe研究）；University of Oregon（俄勒冈大学）；University of Southern California（南加州大学）；Cisco AI Research（思科人工智能研究）
Beyond classical and contemporary models: a transformative AI framework for student dropout prediction in distance learning using RAG, Prompt engineering, and Cross-modal fusion
Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations
Iran University of Science & Technology（伊朗科学技术大学）；Iran University of Medical Sciences（伊朗医科大学）；Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学）；Islamic Azad University（伊斯兰阿扎德大学）；Alborz Vocational Technical University（厄尔布尔士职业技术大学）
This paper provides a simple theoretical framework to evaluate the effect of key parameters of ranking algorithms, namely popularity and personalization parameters, on measures of platform engagement, misinformation and polarization. The results show that an increase in the weight assigned to online social interactions (e.g., likes and shares) and to personalized content may increase engagement on the social media platform, while at the same time increasing misinformation and/or polarization. By exploiting Facebook's 2018 "Meaningful Social Interactions" algorithmic ranking update, we also pro…
Centre for Artificial Intelligence, University College London（伦敦大学学院人工智能中心）；Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）
Educational recommenders have received much less attention in comparison to e-commerce and entertainment-related recommenders, even though efficient intelligent tutors have great potential to improve learning gains. One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets. In this work, we release a large, novel dataset of learners engaging with educational videos in-the-wild. The dataset, named Personalised Educational Engagement with Knowledge Topics PEEK, is the first publicly available dataset of this nature. The video lectures h…
Keeping students engaged with the course content outside the classroom is a challenging task. Since learning during undergraduate years occurs not only as student engagement in class, but also during out of class activities, we need to redesign and reinvent such activities for this and future generation of students. Although active learning has been used widely to improve in class student learning and engagement, its usage outside the classroom is not widespread and researched. Active learning is often not utilized for out of class activities and traditional unsupervised activities are used mo…
The Impact of Artificial Intelligence on the Evolution of Digital Education: A Comparative Study of OpenAI Text Generation Tools including ChatGPT, Bing Chat, Bard, and Ernie
Department of English literature and language（英语文学与语言系）；St.Mary’s University of San Antonio（圣玛丽大学）；Foster School of Businesses（商学院）；University of Washington（华盛顿大学）；Department of Civil and Environmental Engineering（土木与环境工程系）；Florida International University（佛罗里达国际大学）；Department of Electrical Engineering and Computer Science（电气工程与计算机科学系）；Florida Atlantic University（佛罗里达Atlantic大学）
LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems
Department of Communication, University of California, Los Angeles, CA 90095（通信系，加州大学洛杉矶分校，CA 90095）；Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104（计算机与信息科学系，宾夕法尼亚大学，Philadelphia, PA 19104）；Amenenberg School of Communication, University of Pennsylvania, Philadelphia, PA 19104（安纳伯格通信学院，宾夕法尼亚大学，Philadelphia, PA 19104）；Operations, Information, and Decisions Department, University of Pennsylvania, Philadelphia, PA 19104（运营、信息与决策系，宾夕法尼亚大学，Philadelphia, PA 19104）
The Role of Mobile and Social Media Services in Enhancing Freedom of Expression: Opportunities, Challenges, and Prospects for Local Platform Development in Uganda's Digital Ecosystem
