What Is AI?

Artificial Intelligence (AI) is a broad field of computer science focused on creating systems capable of performing tasks that would normally require the use of a human’s intelligence to complete. These tasks include learning, reasoning, problem-solving, perception, understanding and interpreting a language, and even being creative. (Russell and Norvig 2010) Unlike in traditional programming, in which specific instructions are coded, AI systems learn from data and experiences to improve their performance over time. The original intent of AI was to understand and mimic human intelligence to solve complex problems and perform tasks that were too tedious or difficult for humans. Early AI research aimed to create machines that could reason, learn, and adapt. This vision has evolved, but the core goal remains: to build intelligent systems that can assist and augment human capabilities.

Brief Overview of Its Inception

AI’s formal inception began with the work of Alan Turing, a British mathematician and logician often credited as one of the founding figures of AI. In 1950, he proposed the idea of a “universal machine” in his groundbreaking paper Computing Machinery and Intelligence.  In this paper, he posed the question “Can machines think?” (Turing 1950) and introduced the “imitation test” or Turing Test to determine whether a machine could exhibit intelligent behavior indistinguishable from that of a human. The Turing Test went like this: The Interrogator (judge/human) would ask both a human and a machine the same question. Both the human and machine would respond to the question. Without knowing which answer came from which source, if the Interrogator could not distinguish between the human and machine responses, it is said that it passed the Turing Test.

AI Has Been Around Longer Than You Think

While the buzz around generative AI models like ChatGPT, Gemini, Co-Pilot, Claude, and DALL-E3 —along with so many more—is exciting, AI has been a part of our lives for decades. Early applications include systems that played chess, like IBM's Deep Blue, which famously defeated world chess champion Garry Kasparov in 1997. (IBM n.d.) Now, let’s fast forward to Apple's voice recognition virtual assistant, Siri, which was first released in October 2011 with the iPhone 4S. The technology behind Siri originated from a project called CALO, which was funded by the Defense Advanced Research Projects Agency (DARPA) to create a virtual assistant for military personnel. AI is even more prevalent in modern everyday life—from personalized ads, suggestions for the next best item while shopping online, suggestions for alternative routes in our GPS, suggestions for completing texts, automatic misspelling correction, and the completion of sentences when you compose your e-mail message —and it is used to open your phone via facial recognition. (Stone et al 2016) 

How Does AI Work?

AI systems work by processing large amounts of data, identifying patterns, and making decisions based on that information. Here's a simplified example to illustrate this.

Imagine you want to teach an AI system to recognize images of a particular animal. You would provide the system with thousands of images of the animal and non-animals. AI uses algorithms to analyze the features of these images, such as shapes, colors, and textures. Over time, it learns to distinguish among these images with increasing accuracy.

This learning process involves several key components.

Data Collection. Gathering and organizing data for the AI to learn from;

Algorithms. Mathematical models that process data and identify patterns;

Training. The process of feeding data into the algorithms and adjusting them to improve accuracy; and

Inference. Applying the trained model to new data to make predictions or decisions. (Russell and Norvig 2010)

Why Should I Care About AI as a Teacher?

As a science teacher, understanding AI can help you prepare your students for a future in which AI plays a significant role, not only in the education space, but also in industry. AI saves you time on several tasks. AI can also enhance your teaching methods and improve student outcomes in several ways, including these:

Personalized Learning. AI can analyze student performance data, allowing you to differentiate instruction based on students’ individual needs and learning styles.

Automated Grading. AI can assist in grading assignments, giving you more time to focus on planning and providing interactive and engaging teaching activities.

Educational Tool.: AI-powered tools, such as adaptive learning/intelligent tutoring systems, can provide just-in-time support to students, making learning more streamlined, personalized, accessible, and effective.

What’s Next in This Blog Series?

In subsequent posts, we will discuss the use of AI tools in designing science lessons. We’ll explore specific AI tools and applications that you can integrate into your classroom, share success stories from educators who have embraced AI, and provide practical tips for incorporating AI into your teaching practice. Stay tuned to this Teacher’s AIde to discover how you can leverage AI to become the AI Rock Star in your school and university.

References

IBM. (n.d.). IBM100—Deep Blue. Retrieved from IBM Deep Blue. www.ibm.com/history/deep-blue.

Russell, S., and P. Norvig. 2010. Artificial intelligence: A modern approach. Hoboken, NJ: Prentice Hall.

Stone, P., R. Brooks, E. Brynjolfsson, R. Calo, O. Etzioni, G. Hager, J. Hirschberg, S. Kalyanakrishnan, E.. Kamar, S.,Kraus, K. Leyton-Brown, M. Mekelburg, T. Mitchell, and G. Press. 2016. Artificial intelligence and life in 2030: One hundred year study on artificial intelligence: Report of the 2015–2016 study panel. Stanford, CA: Stanford University.https://ai100.stanford.edu/2016-report.

Turing, A. M. 1950. Computing machinery and intelligence. Mind 59 (236): 433–460.
 

Valerie Bennett, Ph.D., Ed.D., is an Assistant Professor in STEM Education at Clark Atlanta University, where she also serves as the Program Director for Graduate Teacher Education and the Director for Educational Technology and Innovation. With over 25 years of experience and degrees in engineering from Vanderbilt University and Georgia Tech, she focuses on STEM equity for underserved groups. Her research includes AI interventions in STEM education, and she currently co-leads the Noyce NSF grant, works with the AUC Data Science Initiative, and collaborates with Google to address CS workforce diversity and engagement in the Atlanta University Center K-12 community.
 

Christine Anne Royce, Ed.D., is a past president of the National Science Teaching Association and currently serves as a Professor in Teacher Education and the Co-Director for the MAT in STEM Education at Shippensburg University. Her areas of interest and research include utilizing digital technologies and tools within the classroom, global education, and the integration of children's literature into the science classroom. She is an author of more than 140 publications, including the Science and Children Teaching Through Trade Books column.

Note: This article is part of the new blog series, From Chalkboards to AI, which focuses on how artificial intelligence can be utilized within the classroom in support of science as explained and described in A Framework for K–12 Science Education and the Next Generation Science Standards.

The mission of NSTA is to transform science education to benefit all through professional learning, partnerships, and advocacy.