AI Glossary

Artificial Intelligence (AI) is rapidly transforming various fields, and counseling is no exception. To effectively navigate this evolving landscape, counselors need a basic understanding of key AI terminology.

Term	Meaning
AI-powered assessments	Tools that use AI to analyze beneficiary data and provide personalized career recommendations, identify potential career paths, and pinpoint skills gaps.
Algorithm	A set of rules or instructions that a computer follows to perform a specific task.
Artificial Intelligence (AI)	The ability of machines to exhibit human-like intelligence, such as learning, problem-solving, and decision-making.
Bias	Unintended and often unobservable systematic errors in AI systems that can lead to unfair or discriminatory outcomes.
Chatbots	AI-powered conversational agents that can interact with users through text or voice.
Chatbots for initial screening	AI-powered chatbots can provide initial support and information to beneficiaries, such as answering frequently asked questions or scheduling appointments.
Deep Learning	A type of ML that utilizes artificial neural networks with multiple layers to analyze complex patterns in data.
Explainability	The ability to understand and interpret how an AI system arrived at a particular decision.
Job market analysis	AI systems that analyze vast amounts of data to identify emerging job trends, predict future skill demands, and provide insights into industry growth and decline.
Machine Learning (ML)	A subset of AI that allows systems to learn and improve from experience without being explicitly programmed.
Natural Language Processing (NLP)	Enables computers to understand, interpret, and generate human language. This includes tasks like sentiment analysis, text summarization, and chatbots.
Personalized learning	AI-powered platforms that recommend tailored learning paths and resources to help beneficiaries acquire new skills and upgrade existing ones.
Reinforcement Learning	ML algorithms that learn to make decisions by interacting with an environment and receiving rewards or penalties.
Skill matching	AI algorithms that match job seekers with suitable positions based on their skills, experience, and preferences.
Supervised Learning	ML algorithms trained on labeled data to predict outcomes. Unsupervised Learning: ML algorithms that identify patterns and structures within unlabeled data.