Term explanation
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.
SlideLizard LIVE brings game-based learning to any PowerPoint presentation: create competitive quizzes with scoreboards, give participants instant feedback after every question, and make knowledge transfer genuinely fun.
Learn more →An extemporaneous speech is a speech that involves little preparation, as the speaker may use notes or cards to give his talk. It is important that speakers will still use their own words and talk naturally. .
Learn moreChain of thought is an AI reasoning technique in which a model explicitly works through intermediate steps before arriving at a final answer. By laying out its reasoning step by step, the model produces more accurate and reliable outputs — especially for complex, multi-part problems. In agentic AI systems, chain-of-thought reasoning is used to plan workflows and make decisions at each stage of an agent loop. For enterprise applications, it increases transparency and makes AI behavior easier to audit.
Learn moreLeading questions are phrased in a way that suggests or implies a preferred answer, subtly guiding the respondent toward a specific response. For example, 'Don't you think this approach is more efficient?' nudges toward agreement. In presentations and sales contexts, leading questions can be used deliberately to build consensus or steer a conversation. However, they can also introduce bias in research and surveys, making it important to recognize and manage their influence on responses.
Learn moreAI grounding is the process of anchoring an AI system's outputs to verified, real-world data rather than relying solely on knowledge encoded during model training. A grounded AI retrieves relevant, up-to-date information from external sources before generating a response. This significantly reduces the risk of AI hallucinations and ensures that outputs are accurate, current, and contextually relevant — a critical requirement for enterprise AI applications where factual reliability is non-negotiable. Grounding is a core technique used in LLM-powered systems.
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