What is Artificial Intelligence?
artificial (ar·ti·fi·cial) [adj]:
made, produced, or done by humans especially to seem like something natural : man-made
intelligence (in·tel·li·gence) [noun]:
(1) the ability to learn or understand or to deal with new or trying situations : REASON
also : the skilled use of reason
(2) the ability to apply knowledge to manipulate one’s environment or to think abstractly as measured by objective criteria (such as tests)
Table of Contents
a focus on the human-defined nature of AI and a focus on the human-like nature of AI. If we explore commonly used definitions of AI, we can explore these different perspectives more.
Depending on which definition (or combination thereof) you adopt, you may focus more on AI’s resemblance to human cognition or on human-driven design and objectives. Recognizing that these dimensions coexist is crucial for understanding AI’s growing role in our schools and communities – and how we can monitor and regulate the impact it may have.
Florida K-12 AI Task Force Definition of AI
Taking both the “human-like” and “human-defined” approaches into account, we propose a working definition for this document:
“AI refers to human-created, machine-based systems that simulate tasks typically requiring human intelligence, including decision-making, predictions, content generation, and recommendations.”
This definition stresses that human creativity and oversight guide AI, even as these systems tackle increasingly sophisticated tasks once performed exclusively by people.
A Brief Timeline of AI in Education
How is AI used in education?
At the time of writing this, there are over 1000 apps that utilize AI with the goal of supporting education – and the number continues to grow. This list is not intended to demonstrate longitudinal success of each application and all approaches carry inherent risks that require careful monitoring and evaluation. For now, we are providing a few example approaches!
Preparing Florida for the Future of Work
The World Economic Forum’s Future of Jobs 2025 report underscores the significance of AI-related skills for thriving in the workforce by 2030 – adding additional motivation for systematically, effectively, and safely integrating AI literacy and tools into our schools. While it is crucial to prepare students—particularly high school graduates—with these competencies, it is equally important to establish robust professional development pathways for educators and staff. Equipping them with the necessary upskilling opportunities empowers current teachers and community leaders to drive meaningful change and foster a future-ready learning environment.
One approach to meet these needs is to establish a School Community Skills-Focused Checklist, created with teachers, staff, students, and parents/guardians, to identify ways in which community members are being offered opportunities to learn and implement these skills. For instance, for Curiosity and Lifelong Learning: In what ways do curricula at your school emphasize self-directed learning, reflection, and the ability to adapt skill sets using AI-enhanced technologies? Are there clear pathways for ongoing upskilling of your teachers and staff in emerging AI and digital tools?
Key AI in Education Terminology
A
- adaptive learning
using AI to personalize educational experiences by tailoring content and pacing to each student’s needs based on student’s performance on tasks, learning behaviors, response times, and interactions within the learning platform. - AI literacy
the knowledge and skills required to understand and interact with AI technologies effectively and responsibly - algorithm
A set of step-by-step instructions or rules used by computers to solve problems or perform tasks.
anonymized data
data that has been stripped of all personally identifiable information (PII), making it impossible to trace back to an individual. - artificial intelligence
human-created, machine-based systems that simulate tasks that typically require human intelligence, including decision-making, predictions, content generation, and recommendations. - artificial general intelligence (AGI)
a type of artificial intelligence that has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human intelligence. In theory, AGI can perform any intellectual task that a human being can do, demonstrating flexibility, creativity, and adaptability in problem-solving and decision-making. However, we aren’t there yet! - artificial narrow intelligence (ANI)
a type of artificial intelligence designed to perform a specific task or a narrow range of tasks very well. Unlike human intelligence, which is versatile and can handle a wide variety of activities, ANI is limited to its programmed functions, such as voice recognition, image classification, or playing a game like chess. - augmented reality (AR)
the integration of AI with real-world environments to overlay digital content, often used in educational applications. - automation
using AI to perform repetitive or mundane tasks, freeing up time for more complex or creative activities.
B
- bias
Unfair or inaccurate outcomes in AI systems caused by skewed or incomplete training data, leading to discriminatory or unbalanced results. - big data
Extremely large datasets that require specialized tools and methods for analysis. AI can use big data to uncover patterns and insights.
C
- chatbot
an AI application that simulates conversation with users, often used for customer service or educational purposes. - classification
an AI task where data is categorized into predefined groups, such as identifying spam emails or sorting student submissions. - clustering
an AI technique used to group similar data points together without predefined categories, often part of unsupervised learning. - computer vision
an area of AI that enables machines to interpret and make decisions based on visual inputs, such as images or videos.
D
- data
information collected from different sources, like numbers, facts, images, or sounds, that can be used to understand something, solve problems, or make decisions. - data annotation
the process of labeling data (e.g., tagging images, highlighting text) to create training datasets for AI models. - data privacy
protecting personal or sensitive information when using AI systems. Teachers must try to ensure student data is handled responsibly and ethically. - deep learning
a subset of machine learning where computers use multiple layers of artificial neurons, known as neural networks, to analyze and learn from large amounts of data. Each layer processes the data in increasingly complex ways, allowing the system to recognize patterns, make decisions, and improve over time without explicit programming. This approach is inspired by the way the human brain processes information, enabling tasks such as image and speech recognition, natural language processing, and even playing complex games. - de-identified data
data from which personally identifiable information (PII) has been removed or masked, but which could still be linked back to an individual with additional information.
E
- ethics in AI
the principles guiding the responsible use of AI, ensuring fairness, transparency, accountability, and the avoidance of harm. - explainability
the ability to understand and interpret how an AI model makes its decisions, which is important for trust and transparency.
F
- feedback loop
the process of using the outcomes of an AI model to refine and improve its performance over time.
G
- generative AI
a type of artificial intelligence that can create new content, such as text, images, music, or even video, by learning from existing data. It uses models like neural networks to generate original material that mimics the patterns and styles found in the data it was trained on.
H
- human-in-the-loop
a method where humans work alongside AI systems, providing oversight, input, or corrections to improve results.
I
- IoT (Internet of Things)
the network of physical devices connected to the internet, enabling them to collect and share data. AI can analyze this data to support learning environments.
M
- machine learning
a type of artificial intelligence where computers learn from data to make decisions or predictions without being explicitly programmed. It’s like how teachers help students learn by giving them examples and exercises, allowing them to improve and understand concepts better over time.
O
- overfitting
a situation where an AI model performs well on training data but fails to generalize to new, unseen data. This is particularly important in education because it can lead to tools that are not effective for diverse learners or varying classroom environments.
P
- Personally Identifiable Information (PII)
information that can directly or indirectly identify a specific individual, such as names, addresses, social security numbers, or student ID numbers. - predictive modeling
the use of AI to analyze data and predict potential future outcomes, such as student performance or trends - prompt engineering
the process of crafting effective questions or instructions to get desired outputs from generative AI tools.
R
- reinforcement learning
a type of machine learning where an AI system learns by trial and error, receiving rewards for correct actions and penalties for incorrect ones.
S
- supervised learning
a type of machine learning where the model is trained using labeled data (e.g., photos labeled as “cat” or “dog”) to predict outcomes. For our Teachable Machine tasks, we are labeling photos as “ripe” or “unripe” bananas.
T
- tokenization
the process of breaking down text into smaller pieces, such as words, sentences, or even individual characters, so a computer can analyze it. In AI, tokenization is used to help systems understand and process human language, like in chatbots or language translation tools. - training data
The dataset used to teach an AI system to perform a specific task. High-quality, diverse training data is essential for creating accurate models. - transfer learning
reusing a pre-trained AI model on a new but related task, reducing the amount of training data needed. - Turing Test
a test proposed by Alan Turing to evaluate whether a machine can exhibit intelligent behavior indistinguishable from that of a human.
U
- unsupervised learning
a type of machine learning where the model identifies patterns or groupings in data without being given explicit labels.