From the standards
Glossary
These definitions are quoted from the June 2026 Idaho 9–12 Artificial Intelligence Content Standards. The Department notes the list is not comprehensive, nor are all terms used in the standards.
- Agentic AI
- AI systems designed to independently plan, execute, and adjust actions to achieve a specific goal with minimal human intervention, often capable of working across multiple tools or applications.
- AI Companion
- A highly personalized, conversational AI designed to simulate human-like relationships, offering emotional support, companionship, and customized personalities rather than just executing practical tasks.
- AI Literacy
- Technical knowledge, durable skills, and future-ready attitudes required to thrive in a world influenced by AI. It enables learners to engage, create, manage, and design AI, while critically evaluating its benefits, risks, and ethical implications.
- Anthropomorphism
- The cognitive tendency to attribute human traits, emotions, intentions, or consciousness to AI systems, which can lead to unrealistic expectations, misplaced trust, or the mistaken belief that an AI platform is capable of genuine social connection or personal experience.
- Artificial Intelligence (AI)
- A branch of computer science focused on building systems capable of performing tasks that typically require human intelligence. AI leverages massive data sets and computational power to make predictions, increasingly impacting various aspects of our daily lives.
- Automation
- The use of technology to perform tasks, processes, or workflows with minimal human assistance to increase efficiency, speed, or precision.
- Generative AI (Gen AI)
- A subset of machine learning and AI specifically designed to create new content, including text, images, audio, code, video, or synthetic data based off a prompt.
- Hallucination
- Instances where an artificial intelligence system generates information that is incorrect, misleading, or entirely fabricated, but presents it as if it were true.
- Human-Centered
- An approach where AI is used specifically to support human inquiry. It ensures that humans remain responsible for the initial objective, final decision-making, oversight, and ethical reflection at the beginning and end of every task (Human AI Human).
- Human-in-the-loop
- A model of interaction where a human is required to participate in a process, such as verifying, approving, or correcting, to ensure the accuracy, safety, and ethical alignment of AI-generated outcomes.
- Large Language Model (LLM)
- A specific type of Generative AI trained on vast amounts of text data to recognize, predict, and generate human-like text responses.
- Machine Learning
- A subfield of AI where computer systems are trained to identify patterns in large datasets, allowing them to improve their performance on tasks over time without being explicitly programmed for every step.
- Personally Identifiable Information (PII)
- Information that can be used to identify a specific person, such as a name, home address, phone number, email address, or student ID number.
- Predictive AI
- AI models that analyze historical and existing data to make forecasts or estimations about future events, trends, or outcomes.
- Simulated Environment
- An artificial, controlled setting created to replicate real-world conditions. Examples range from computer games and AI-generated videos, to two humans role-playing a situation like a job interview.
- Synthetic Media
- Content, such as images, audio, video, or text, that has been created, modified, or generated by AI, often used to simulate reality.