Glossary

One financial term per page — direct definitions in plain language, optimized for search and answer engines.

AI financial advisor

Software that helps users explore financial questions using AI — ideally grounded in real data, not invented answers.

Using artificial intelligence to personalize explanations, practice, and feedback in money learning.

Planning how income is allocated across spending, saving, and goals over a period.

Interest earned on both the original principal and previously accumulated interest.

A cognitive framework where abstract ideas are understood through mappings from concrete, physical experiences.

Systems that combine reasoning, data engines, and structured outputs to support complex choices.

Spreading investments across assets or categories to reduce reliance on any single outcome.

Education delivered digitally — online courses, modules, and self-paced sessions accessible from any device.

Cash reserves set aside for unexpected expenses or income loss.

Structured teaching of money skills and concepts — in schools, workplaces, banks, or self-directed platforms.

The ability to understand and use financial skills — budgeting, saving, investing, and risk — in everyday life.

A mapping from a tangible experience to an abstract money concept — e.g. growth, loss, or waiting.

Learning that happens through playing designed games rather than passive content consumption.

Using game design elements — goals, feedback, stakes, and progression — to teach non-game subjects.

The rate at which the general level of prices for goods and services rises, reducing purchasing power over time.

A statistical method that models many possible outcomes by random sampling to show ranges, not single guesses.

The principle that money available today is worth more than the same amount in the future, due to earning potential and inflation.

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