From Passive Watching to Active Learning: Empowering Proactive Participation in Digital Classrooms with AI Video Assistant
In online education, innovative tools are crucial for enhancing learning outcomes. SAM (Study with AI Mentor) is an advanced platform that integrates educational videos with a context-aware chat interface powered by large language models. SAM encourages students to ask questions and explore unclear concepts in real-time, offering personalized, context-specific assistance, including explanations of formulas, slides, and images. In a crowdsourced user study involving 140 participants, SAM was evaluated through pre- and post-knowledge tests, comparing a group using SAM with a control group. The results demonstrated that SAM users achieved greater knowledge gains, with a 96.8% answer accuracy. Participants also provided positive feedback on SAM's usability and effectiveness. SAM's proactive approach to learning not only enhances learning outcomes but also empowers students to take full ownership of their educational experience, representing a promising future direction for online learning tools.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Big Brother is Watching: Proactive Deepfake Detection via Learnable Hidden Face
As deepfake technologies continue to advance, passive detection methods struggle to generalize with various forgery manipulations and datasets. Proactive defense techniques have been actively studied with the primary aim…
DeepFake DetectionFace SwappingCoward: Collision-based OOD Watermarking for Practical Proactive Federated Backdoor Detection
Backdoor detection is currently the mainstream defense against backdoor attacks in federated learning (FL), where a small number of malicious clients can upload poisoned updates to compromise the federated global model. …
Federated LearningProACT: Towards Breakdown-Aware Proactive Agent in Multi-User Collaboration
Conversational agents are increasingly embedded in human collaborative work, yet they remain fundamentally passive and reactive: they respond to explicit user requests rather than proactively recognizing moments when a t…
Decision MakingExploring Human-Robot Collaboration: Analysis of Interaction Modalities in Challenging Tasks
This work compares three interaction modalities for human-robot collaboration: passive, reactive, and proactive. We studied 18 participants assembling a seven-layer colored tower from memory while using nearby and distan…
Combating Client Dropout in Federated Learning via Friend Model Substitution
Federated learning (FL) is a new distributed machine learning framework known for its benefits on data privacy and communication efficiency. Since full client participation in many cases is infeasible due to constrained …
Federated Learning