The term “artificial intelligence” (AI) is increasingly more familiar as we encounter it in our daily lives. (Have you asked Siri or Alexa a question lately?) As technology becomes a bigger part of teaching and learning, accelerated by the COVID-19 pandemic, we are likely to encounter AI more frequently in our schools. How do we see AI in the classroom? And do we really know what AI—or rather, an AI system—is?
What Is an AI System?
AI systems can be defined as software or machines that are able to perform tasks we typically associate with humans. These systems are helpful because of the algorithms they use to make decisions. In other words, algorithms are the “brains” of an AI system, and there can be risks and rewards associated with using algorithms to make decisions. Using algorithms, AI systems can examine data, draw conclusions, and then perform some sort of action. In addition to many other tasks, AI systems can also:
- Perform perception via sensors, like those for face recognition
- Allow natural interaction (e.g., interacting with people through speech or gesture)
- Use models for reasoning about complex patterns or behaviors
- Discover patterns in large amounts of data
With different combinations of capabilities and algorithms, AI systems can do some things very well, very quickly, and at a massive scale. At times their performance can resemble humans. Some people are excited by the possibilities, some are scared, and others aren’t even sure what’s possible and what’s science fiction—there is a lot of hype out there!
While many people fear that AI systems will replace humans in the workplace, we take a human-centered perspective that sees AI systems working with humans and helping to augment human skills. People should always play a leading role in education, and AI systems should not replace teachers, ever. There are certain interactions that require human touch and understanding, such as giving certain types of feedback or building trusting relationships with students. At the same time, AI systems could automate some routine, repetitive tasks to provide time for teachers to focus on more complex activities.
Educators, AI, and the Future of Learning Webinars
There are additional ways to define AI and think about its possibilities in education, including its use to support collaboration and help teachers understand more about what their students know and don’t yet know. In the upcoming webinar series, “Educators, Artificial Intelligence, and the Future of Learning,” we will focus on three areas of learning that AI products currently support or are poised to impact in the next five to 10 years: assessment, learning environments, and teacher support.
Researchers and technologists are currently using AI to transform learning by:
- Creating new kinds of assessments that help teachers better understand where their students are
- Giving teachers insight into what is happening with a student, or a group of students, such as when the teacher is working with other students
- Supporting collaboration between students and students with teachers
- Helping teachers and students when they work in different ways to tackle complex problems (e.g., as teams, in whole class lessons, individual work)
In addition to sharing the latest research on AI and education, the webinars will dive into questions that educators have about using AI in their classrooms, such as: How can teachers understand the decisions that AI systems make? Can they override them? How can educators be sure their students’ data is safe, and that AI systems don’t act with bias?
Our goal is to start a conversation between educators, researchers, and entrepreneurs to illuminate the future of education with AI.
How to Get Involved
Watch the webinar recordings:
To learn more about AI and the future of STEM learning, check out this list of resources. We encourage you to also read the CIRCLS “AI and the Future of Learning” report to learn about new visions for AI systems.
This work was supported by the National Science Foundation under grant numbers #2040753 and #2021159. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
