Term explanation
In a learning context, virtual reality (VR) creates immersive, simulated environments in which learners can practice skills, explore scenarios, or experience situations that would be difficult, expensive, or dangerous to replicate in real life. VR training is used in industries such as healthcare, aviation, manufacturing, and emergency services. By placing learners inside a realistic environment, VR significantly increases engagement, retention, and the transfer of skills to real-world performance.
Before or after a VR learning experience, SlideLizard LIVE helps you gauge prior knowledge and gather feedback with live polls and quizzes directly in PowerPoint — making debriefs and knowledge checks seamlessly interactive.
Learn more →Game-based learning (GBL) uses game mechanics — such as points, levels, challenges, and rewards — to deliver educational content in an engaging format. Games motivate learners through competition, narrative, and immediate feedback, making them particularly effective for skill practice and knowledge reinforcement. Game-based learning ranges from simple quiz games to complex simulations and serious games developed for specific professional training scenarios.
Learn moreClosed questions are questions that can be answered with a limited set of responses — most commonly a simple 'yes' or 'no', or a selection from predefined options. They are used to gather specific, factual information quickly and efficiently. In presentations and training settings, closed questions are useful for gauging audience understanding, confirming agreement, or running quick polls. While efficient, they offer little depth and should be balanced with open-ended questions when richer feedback or discussion is needed.
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.
Learn moreA Large Presentation Model (LPM) is a specialised AI system designed for the demands of enterprise presentation communication. Unlike generic language models, an LPM does not simply process text — it connects company knowledge, audience context, live data, and brand guidelines to produce consistent, presentation-ready communication. The system encompasses the entire presentation cycle: from content structure and storyline to visual execution. Rather than acting as a simple generation tool, it takes on the role of a coordinating, agent-based system.
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