# Enhancing Online Learning Through Multi-Agent Debates for CS University Students
## 1 Introduction  
With the increasing availability of online courses, a growing number of undergraduate and graduate students are turning to these resources for learning. Millions now access free educational videos on platforms like Khan Academy, Coursera, edX, Udacity, MIT Open-CourseWare, YouTube, and Chinese MOOC platforms. This trend underscores how Internet technology is enhancing educational access and broadening the range of available courses, particularly serving as a valuable alternative for students unable to attend in-person classes during the COVID-19 pandemic [1]. However, most current online courses still rely primarily on one-way lectures from an instructor, supplemented by a few post-lecture quizzes or self-regulated learning prompts [2, 3]. There is a lack of necessary interaction and discussion, making them not significantly different from traditional, teacher-centered instruction,except that they are delivered through the Internet. Previous research on human-computer interaction has focused on enhancing educational videos through various techniques, such as interactive transcripts, word clouds, keyword search, and storyboard highlights [4]. Other studies have segmented videos to display key moments alongside snapshots and transcripts [5]. Additionally, some researchers have developed video digests structured in a textbook-like format, organized into chapters, titles, and sections with accompanying text summaries [6]. Moreover, [PERSON] et al. [7] found that students who used summarized slide previews achieved higher pre-quiz scores and required less time compared to those who previewed the original learning materials. Therefore, using large language models (LLMs) to summarize video content and subsequently perform retrieval-augmented generation (RAG) may also provide an effective human-computer interaction (HCI) approach to enhance students' learning efficiency. The advent of LLMs, such as Generative Pre-trained Transformer 4 (GPT-4) [8], has provided significant technological advancements in various educational activities, including engineering education [9], language teaching and learning [10], and personalized learning [11]. Despite their potential, LLMs still face challenges [12; 13] such as hallucination, reliance on static training data, lack of real-time knowledge, limited problem-solving capabilities, memory continuity issues, ethical and safety concerns, and insufficient personalization. Careful use of LLMs poses challenges for both teachers and learners [14]. However, these limitations can potentially be mitigated by advanced prompt engineering techniques, such as prompt engineering [15], chain of thought (CoT) [16], self-consistency (SC) [17], and retrieval-augmented generation (RAG) [18]. Ongoing advancements in generative AI, including RAG, are progressively enhancing the factual reliability and responsiveness of large language models (LLMs). Autonomous agents have long been a central research area in both academia and industry. Large language models have been applied as autonomous agents in many fields, especially in educational assistance [19; 20]. This advancement suggests a promising pathway for implementing multi-agent systems based on large language models, enabling novel online teaching and learning experiences. Accordingly, we present an online learning-assisted scheme that offers an intuitive and user-friendly solution. This scheme supports both question-and-answer sessions and topic discussions through video-based multi-agent debates. The purpose of this paper is to explore the role of open-source LLM-based agents in facilitating self-learning among undergraduate students, particularly in the field of computer science. The research question for this study is the following:  
* RQ. How can video-based multi-agent debates enhance online learning outcomes for CS students? Regarding the above-mentioned research question, we further elaborate and propose the following hypotheses to explore the effects of multi-agents on students. * H1.