Term explanation
A student response system (SRS) is a technology that allows students to respond to questions or polls during a class or presentation using personal devices or dedicated clickers. Responses are collected and displayed in real time, giving instructors immediate insight into comprehension levels and enabling on-the-spot adjustments to pacing or content. Student response systems improve engagement, reduce passive listening, and make large group instruction more interactive.
SlideLizard LIVE is a full student response system built directly into PowerPoint. Students respond to polls and quizzes from any device, you get honest anonymous feedback in real time, and every session becomes more engaging.
Learn more →A .pot file is a legacy PowerPoint template format used to define reusable styles, layouts, and formatting for presentations. Like its successor .potx, it allows teams to create multiple presentations that share the same visual identity without starting from scratch each time. The .pot format was replaced by .potx in Office 2007, which introduced an open XML-based structure for improved compatibility.
Learn moreComputer-based training (CBT) refers to self-paced educational programs delivered through software on a computer, without requiring an internet connection or live instructor. CBT packages typically include multimedia content, simulations, and assessments. This format was widely adopted in the 1990s and 2000s as an alternative to classroom training, offering consistency and repeatability. Today, CBT is largely succeeded by web-based training (WBT), though it remains in use where internet access is limited or security is a concern.
Learn moreA learning chunk is a small, self-contained unit of educational content covering a single concept or skill. Chunking is a core principle of instructional design: breaking complex topics into manageable segments reduces cognitive load and improves retention. Learning chunks are the building blocks of microlearning programs and modular course structures, and work well in both digital and instructor-led training contexts.
Learn moreA large language model (LLM) is an AI system trained on vast amounts of text data that can understand, generate, and transform language at a human-like level. LLMs power a wide range of applications — from chatbots and writing assistants to automated document creation and data summarization. In enterprise software, LLMs are increasingly embedded into workflows to interpret unstructured data, draft content, and translate information between systems automatically. In contrast, the Large Presentation Model (LPM) is a specialised AI system that orchestrates the entire presentation cycle in an enterprise context.
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