Learning on Demand

Learning on Demand

Term explanation

Definition and meaning

Learning on demand is an approach in which learners access educational content whenever they choose, rather than following a fixed schedule. Content is typically available as pre-recorded videos, e-courses, or interactive modules accessible 24/7. This format suits self-directed learners and organizations that need training available across different time zones. It contrasts with synchronous learning, where all participants engage at the same time.

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Other glossary terms

Distributed Audience

A distributed audience is a group of presentation attendees who are physically located in different places — such as different offices, cities, or countries — and who attend the presentation remotely or from multiple simultaneous locations. Managing a distributed audience requires careful attention to technical setup, timing across time zones, and engagement tools that compensate for the lack of physical presence. Distributed audiences are common in global organizations, virtual events, and multi-site corporate communications.

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Agent Loop

The 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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.potm file extension

A .potm file is a macro-enabled PowerPoint template that combines the reusable layout and formatting of a standard template with embedded VBA macros. It allows organizations to distribute presentation templates that include automated functionality — such as auto-populating fields or enforcing formatting rules — while maintaining a consistent visual identity. Like .pptm files, .potm files should be handled carefully due to their macro capabilities.

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Generative Engine Optimization (GEO)

Generative engine optimization (GEO) is the practice of structuring content and digital presence to improve visibility in responses generated by AI systems — such as ChatGPT, Perplexity, Google Gemini, or Claude — rather than solely optimizing for traditional search engine rankings. Where SEO aims to rank on a results page, GEO aims to be cited inside an AI-generated answer. As AI-generated responses now account for over 60% of all search interactions, GEO has become critical alongside classical prompt engineering strategies for any organization that wants to remain visible in AI-driven search.

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