A Quick Reference Guide to Artificial Intelligence for Teachers
The PreK-12 education community is buzzing with excitement, concern, and conjecture about the implications of artificial intelligence (AI) on the future of teaching and learning. AI is nothing if not complex, so we’ve put together a short guide to understanding AI in the context of education to help you navigate conversations in your district and changes in your own classroom.
Key Terms & Definitions
If you’ve read or heard about AI, you’ve likely encountered a number of technical terms that can be confusing and are sometimes used interchangeably. Here are definitions from industry leaders for a few of the most important AI terms to know:
Machine Learning: “A subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.” (Definition Credit: MIT) Machine Learning is sometimes used interchangeably with Artificial Intelligence. Machine learning uses data to train, and the more data it has, the better the program performs.
Deep Learning: “A subset of machine learning (ML), where artificial neural networks — algorithms modeled to work like the human brain — learn from large amounts of data. Deep learning is powered by layers of neural networks, which are algorithms loosely modeled on the way human brains work. Training with large amounts of data is what configures the neurons in the neural network. The result is a deep learning model which, once trained, processes new data.” (Definition Credit: Oracle) Speech recognition on phones is one example of deep learning.
Artificial Neural Networks: “A subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.” (Definition Credit: IBM) Google’s search algorithm is an example.
Generative AI: “Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos.” (Definition Credit: McKinsey)
Large Language Model: “LLMs are machine learning models that utilize deep learning algorithms to process and understand language. They’re trained with immense amounts of data to learn language patterns so they can perform tasks. Those tasks can range from translating texts to responding in chatbot conversations — basically anything that requires language analysis of some sort.” (Definition Credit: Fast Company) The technology that powers ChatGPT is a large language model.
What AI Looks Like in the Classroom
Both machine learning and generative AI can play a role in teaching and learning. Generative AI has perhaps a more visible, obvious role in the classroom, especially as tools like ChatGPT and Bard dominate the news. These tools generate answers to users’ questions and prompts. Teachers can use them to brainstorm lesson plans and students can use them to inspire their writing or to get help with schoolwork.
Machine learning is already present in many classrooms and has been for some time. Education technology tools that identify gaps in a student’s understanding and deliver instruction at the student’s zone of proximal development, often called adaptive learning technologies (not to be confused with assistive technology), use machine learning. These tools are present in many classrooms, enabling teachers to differentiate or personalize instruction while driving student agency. An example of this technology is ALEKS, an adaptive math and science program for grades 3–12.
Here are a few articles on how to use generative AI and machine learning in the classroom:
AI in Education Research and Policy
While there is so much that we don’t yet know about how AI will impact PreK-12 education, researchers, government agencies, and educational leadership groups are working to anticipate and prepare for inevitable, transformative changes to teaching and learning. Here are a few recent reports and policies from industry leaders to familiarize yourself with the evolving landscape:
U.S. Department of Education Office of Educational Technology policy report, Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations:
Digital Promise Resources on AI in Education:
CoSN Spring 2023 Report on AI in Education:
McGraw Hill AI-Powered Learning Programs
We use artificial intelligence to ease teacher workflows, enable differentiation and personalization, boost student engagement, and foster student-teacher partnerships. All our AI-powered technology is built on what we know about learning science, is designed with students and teachers in mind, and adheres to our overarching, global McGraw Hill privacy practices. Here are a few examples:
- Our adaptive online supplemental math and science program for grades 3–12, ALEKS®, uses “big data” (billions of data points gleaned from over 25 million students) and sophisticated applied math theory to drive improved student learning outcomes by identifying precisely what content the individual student has mastered and what the individual is now ready to learn.
- Actively Learn™, our digital supplemental curriculum platform for grades 3–12 ELA, science, and social studies uses automatic short-answer response grading, allowing educators to devote additional time to other highly impactful activities, such as lesson planning and direct student interaction.
- McGraw Hill Plus for PreK–12 uses student data insights to present recommended individualized learning content to teachers, who can then assign or adjust it to meet their differentiation and personalization goals.
- Achieve3000 Literacy uses embedded assessments and a proprietary acceleration engine to automatically adjust the difficulty of texts as students’ reading skills improve.
Ethical Implications of AI in PreK-12 Education
This past school year, conversations about AI were largely dominated by concerns about ChatGPT or other generative AI tools and academic integrity. However, the broad spectrum of implications for AI in classrooms is far more nuanced, including impacts on student data privacy, equity and bias concerns, and the need for human oversight. Our learning scientists outlined our commitment to ethical AI in the McGraw Hill PreK-12 Commitment to Ethical Artificial Intelligence. Our AI exists to automate otherwise manual workflows, assist educators, engage learners, make personalized instruction scalable, and elevate human relationships.
Read the full document here:
