Term explanation
Process questions ask participants to explain how something works, how a decision was made, or how a result was reached — rather than simply what the answer is. They focus on reasoning, methodology, and the steps taken to arrive at an outcome. In training, coaching, and facilitated workshops, process questions help participants reflect on their thinking and deepen their understanding. They are more cognitively demanding than recall questions and are effective for developing critical thinking and problem-solving skills.
LIZ AI answers process questions through automation. It handles the recurring tasks of updating, formatting, and distributing presentations — so teams can focus on the substantive questions, not the operational ones.
Learn more →A declamation speech describes the re-giving of an important speech that was originally delivered by someone else. It is usually performed with emotion and passion, aiming to re-create the impact of the original address. Declamation speeches are common in academic debate competitions, speech training courses, and public speaking programs where students study and recreate historical or literary speeches.
Learn moreA virtual event is an event that takes place entirely online, with all participants joining remotely via digital platforms. Virtual events range from webinars and online conferences to virtual trade shows, networking events, and team celebrations. They eliminate geographic and logistical barriers, allowing global participation at low cost. Successful virtual events invest in interactivity — live polls, Q&A sessions, breakout rooms, and networking features — to compensate for the lack of physical presence.
Learn moreFlipped Classroom means that students work out the subject matter themselves at home through tasks such as reading, videos, etc. Interactive learning activities and exercises then take place in class.
Learn moreThe agent loop is the core operating cycle of an autonomous AI agent. It runs continuously through four phases: Perception (gathering information), Reasoning (planning the next step), Action (executing — such as calling a tool or generating content), and Observation (evaluating the result). The loop repeats until the task is complete or the agent requires human input. This is the mechanism behind Agentic AI systems — it is what allows agents to handle complex, multi-step tasks that a single prompt-and-response model could not.
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