The internet is a never-ending stream of new video content, and keeping AI models up-to-date with the latest trends and topics is a huge challenge. Imagine training an AI to answer questions about videos, only to find it completely stumped by new uploads! This "catastrophic forgetting" problem is what researchers at Nanyang Technological University tackled in their work on continual Video Question Answering (VideoQA). Their approach, called Collaborative Prompting (ColPro), empowers large language models (LLMs) to learn continuously from video streams without losing their grip on previously learned knowledge. Think of it as giving the AI a helpful set of reminders and pointers, allowing it to connect new video information with what it already knows. ColPro cleverly combines three prompting techniques: one that helps the AI understand the specific question being asked, another that captures the visual and temporal aspects of the video (like the order of events), and a third that integrates this new knowledge with existing information. Tested on datasets like NExT-QA and DramaQA, ColPro showed significant improvements in accuracy and a remarkable reduction in forgetting compared to traditional methods. This research is a big step forward in building AI systems that can continuously adapt to the ever-changing world of online video, potentially revolutionizing how we search, understand, and interact with video content. While challenges remain, especially with the increasing complexity of video data, ColPro offers a promising path towards truly dynamic and adaptable AI.