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Artificial Intelligence (AI)
Artificial intelligence, or AI, is expected to have a transformative impact on the future of education globally. Educators and students will require enhanced skills to identify and navigate the use of these AI systems. Educators need to prepare students for changing workplaces, including teaching them how to use emerging technologies.
This webpage provides an overview of foundational AI terms and concepts, high-level discussion questions regarding AI in education to consider at the Local Education Agency (LEA) leadership and classroom educator levels, resources to learn more about AI, resources for educators to teach their students about AI, as well as links to further research and reports regarding AI.
Foundational AI Terms and Concepts
| Term/Concept | Definition | Example |
|---|---|---|
| Artificial Intelligence | Artificial Intelligence, or AI, refers to the development of computer systems that can perform tasks that typically require human intelligence, such as recognizing patterns, making decisions, and learning from experience. | Examples of AI include facial recognition, autonomous vehicles, text prediction, digital voice assistants, and many other applications. |
| Machine Learning | Machine learning is a type of artificial intelligence that involves training algorithms to make predictions or decisions based on data. | Examples of AI systems that use machine learning are facial recognition, product and entertainment recommendations, email spam filtering, and predictive text. |
| Deep Learning | Deep learning is a subset of machine learning that involves training multiple layers of interconnected nodes in an artificial neural network on large datasets to identify complex patterns and relationships in the data. | Examples of AI systems that use deep learning are self-driving cars, medical diagnosis applications, chatbots, and customer service bots. |
| Data and Training Data | Within the context of AI, “data” are units of information about people or objects that are used by AI systems. “Training data” refers to large amounts of data that has been labeled/coded and is used to teach AI models or machine learning algorithms to make the most appropriate decisions to the best of its abilities. | In an AI system for a self-driving car, the training data will include images and videos labeled to identify cars versus street signs versus people. |
| Generative AI | A type of narrow or weak AI that can generate new content, such as text or images, in response to a prompt. | Examples of generative AI systems include AI chatbots such as OpenAI’s ChatGPT and Google’s Gemini, Github’s programming AI system Copilot, and art creator Midjourney. |
AI System Classifications
| Classification | Definition | Example |
|---|---|---|
| Weak or Narrow AI | AI systems that are designed to perform specific tasks or solve specific problems, rather than having a broad range of intelligence. | Examples of weak or narrow AI systems include digital voice assistants, predictive analytics, chatbots, and delivery bots. |
| Strong or General AI | Strong AI, also known as artificial general intelligence (AGI) or general AI, is a type of theoretical AI system that would have intelligence and decision-making skills equal to humans. It would have a self-aware consciousness that could solve problems, learn, plan, and adapt independently. | There are currently no AI systems that can match human intelligence, but there are some instances of Strong AI systems in the form of research projects or hypothetical scenarios. |
Resources
- New Jersey Department of Education's Pre-Recorded Technical Assistance Webinar, "Considerations for Artificial Intelligence in Education"
This technical assistance webinar provides an overview of foundational AI terms and concepts, high-level discussion questions regarding AI in education to consider at the Local Education Agency (LEA) leadership and classroom educator levels. - Code.org's AI 101 for Teachers
This is a free, foundational online learning series for any educator interested in the groundbreaking world of artificial intelligence (AI) and its transformative potential in education. - Common Sense Education's Free Resources to Explore and Use ChatGPT and AI
Provides an educator-focused approach to information, concerns, and use cases for AI in the classroom. - IBM's Artificial Intelligence Free Learning and Resources
Provides resources for educators to gain a foundational understanding of artificial intelligence, including technical underpinnings like natural language processing, practical applications, and ethical considerations. This site also includes resources for students.
- TeachAI Webpage
The TeachAI initiative, of which the New Jersey Department of Education is a participant, provides best practice guidelines for policymakers, education leaders, classroom educators, parents, and companies offering valuable insights on incorporating AI in primary and secondary education curriculum standards, courses, tools, assessments, and professional learning. - Code.org's "How AI Works" Resources
This page provides a series of short videos and accompanying in-classroom lessons that introduce students to how artificial intelligence works and why it matters. - International Society for Technology in Education (ISTE)'s AI Exploration for Educators
This page includes links to The Hands-On AI Projects for the Classroom guides from ISTE and General Motors that provide elementary, secondary, elective and computer science teachers with innovative curricular resources about AI across various grade levels and subject areas. - MIT Responsible AI for Social Empowerment (MIT RAISE) AI Literacy Units
Massachusetts Institute of Technology's RAISE initiative developed a wide range of learning units for K-12 AI Literacy. - Stanford Graduate School of Education's Classroom-Ready Resources About AI for Teaching (CRAFT)
CRAFT is a collection of co-designed free AI Literacy resources about AI for high school teachers, to help students explore, understand, question, and critique AI. CRAFT intentionally pursues a multidisciplinary approach so educators with a variety of discipline backgrounds can teach about AI.
- Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations, United States Department of Education Office of Educational Technology
This 2023 report describes AI as a rapidly-advancing set of technologies for recognizing patterns in data and automating actions, and guides educators in understanding what these emerging technologies can do to advance educational goals—while evaluating and limiting key risks. - Bringing AI to School: Tips for School Leaders, International Society for Technology in Education (ISTE)
This guide for local education agency leaders provides background knowledge for learning leaders in an AI-infused world. - Blueprint for an AI Bill of Rights, United States White House Office of Science and Technology Policy
The Blueprint for an AI Bill of Rights is a set of five principles and associated practices to help guide the design, use, and deployment of automated systems to protect the rights of the American public in the age of artificial intelligence.
- Google's Quick Draw Application and Quick Draw Dataset Explorer
An application that recognizes your drawings using AI. Quick Draw also provides the full datasets used by the AI algorithm, which is a powerful example of how training data is used for AI systems. These datasets can also be explored. - Google's Teachable Machine
Use the camera or microphone on your device to train a machine through AI to see or hear something and predict what it is. This is a good application to use as an introduction to machine learning. - Stable Diffusion
A text-to-image generator that creates photo-realistic images given any text prompt. Review Stable Diffusion’s prompts search engine to explore millions of AI-generated images created with individual and unique prompts. - OpenAI's ChatGPT 3.5
A chatbot that generates text in response to user-created prompts in a conversational format. The free version, 3.5, can be accessed with registration. This version is only trained on data from up to 2021.