Artificial Intelligence (AI) in Education


AIML illustration

Teachers in K-12 classrooms have been using artificial intelligence and machine learning (AIML) for several decades.

Imagine a fifth-grade teacher focusing on the Egyptian pyramids as a part of a social studies curriculum unit. It is impossible to physically take the whole class to visit Egypt, so the teacher instead uses virtual reality goggles for each student to put on and feel as if they are there in Giza. If a teacher wants students to better understand American history, they might use the latest edition of the Oregon Trail software program to recreate the historical event through game simulation. If a third-grade student is learning to read, a teacher might have the child put on some headphones and use a reading software program that asks the child to read the words into the microphone and then the computer’s algorithm can ask the child to reread words that were decoded incorrectly.

AIML have been used in classroom settings for a long time as supporting tools for teaching and learning; however, the current wave of AI-supported academic technologies is bringing up ethical concerns regarding usage. There are fears that students may use AI tools to produce work that they did not originally create and the potential harms of AI in the K-12 classroom.

Large language models (LLMs) such as GPT-4 and ChatGPT made an impact recently on the education sector, notably the emerging problems of cheating. Teachers are proposing strategies to navigate these issues in their classrooms and school districts are drafting policies to prevent academic cheating and the consequences, if and when students are caught plagiarizing. The undetectable misuse of AI tools for academic cheating poses a significant challenge to educators, especially if they are not familiar with the student’s voice. The problem becomes overwhelming by the ease of access and rapid improvements in AI technology, outpacing the capabilities of detection technology, which is why many school districts have banned AI entirely.

For example, ChatGPT was first released in November 2022 and opens up a chatbox in which you can ask it anything you would like to explore. A student can ask the AI tool to write a paper on any historical event, solve a math problem of any size, summarize class notes or even generate an outline for a long-term project in a matter of seconds. Teachers have to use their sixth sense to determine whether the work submitted by the student is truly their own or AI generated, perhaps leading to further subjective bias and misconceptions of student ability.

AIML – Five Big Ideas

However, teachers also recognize the potential benefits of embracing such technologies in education, suggesting that they integrate AI into their curriculum and instruction practices. As AI technology advances and becomes more integrated into our daily lives, the need to enhance our K-12 students’ AI literacy has become more relevant. The Artificial Intelligence for K-12 Initiative is a website that provides national guidelines for AI education for K-12 teachers, an online, curated resource directory to facilitate AI instruction and a community of practitioners, researchers, resource and tool developers focused on AI for the K-12 audience. Illinois is a participating state in this national discourse in which there is a shared belief that “AI education is important for students of all ages, albeit for different reasons” (AI4K12, 2023). The website shares five “Big Ideas” for how to view the use of AI in the classroom.

One Big Idea to include in the AI curriculum is to teach students that computers perceive the world using sensors and that perception is the process of extracting meaning from sensory signals. This allows computers to “see” and “hear” well enough for practical uses, such as helping children to read.

Big Idea Two is that computers construct representations using data structures, and these representations support reasoning algorithms that derive new information from what is already known. While AI computer programs can reason about very complex problems, computers do not think the way a human does in infinite ways.

Big Idea Three is to teach K-12 students that people learn by observation, by being told, by asking questions, by experimentation, by practice and by making connections to past experience. People are natural learners, while computers have to be programmed to learn the way we learn. There are two ways that computers can be programmed to learn: they can learn by finding patterns in human-supplied examples, or they can learn by trial and error. Children as young as age five can understand that the type of learning that humans engage in can be replicated by a computer.

Big Idea Four focuses on how computers try to imitate human language such as when you ask ChatGPT to write an essay for you; however, the finished essay that gets generates in seconds may sound more like a robot than a human being because computers have difficulty understanding and producing text that makes use of metaphor, imagery, hyperbole, sarcasm, humor or word play.

Big Idea Five is that AI can impact our society in both positive and negative ways. AI technologies are changing the ways we work by helping us write emails, telling us if our flight is on time, translating documents quickly, converting our speech to text and then text to speech and self-driving cars to move us around across the country.

At the same time, students need to learn about the harms that can potentially occur such as racial and gender biases in the data used to train an AI system as well as human interference that can cause the AI system to malfunction. When you ask AI to generate images of a doctor, for example, most likely it will spit out images of older white men in lab coats. AI is created by human beings who are biased themselves and so AI generated images and texts will also be biased. Thus, it is important for teachers to develop criteria for the ethical design and deployment of AI-based systems in their classrooms.

How Can AI and AIML Be Used in Curriculum Planning?

Universities are providing professional development for K-12 teachers to integrate AI into their curriculum and instruction such as appreciating AI, understanding AI and utilizing AI strategically. Many teachers are using AI to generate lesson plans that would have taken hours for them to develop individually on their own, thereby reducing labor costs. Instead, teachers can now spend their time focusing on the execution of the lesson plans and the skills needed for successful implementation of lessons and activities.

Teachers are using AI tools in diverse ways, including as an information provider, an ideation machine and a customizer of lesson plans. Teachers are brainstorming more lessons and activities with ChatGPT than in individual brainstorming and to support reflective practices toward developing design expertise (Campos, Nguyen, Ahn, & Jackson, 2023).

In terms of AI curriculum resources, a team of researchers at the University of Florida designed and developed a conversational AI (ConvAI) using both rule- and generation-based techniques to facilitate math learning for high school and college students (Li, Zhu, Xiang & Guo, 2024). Eye-tracking data revealed that participants in the ConvAI group generally exhibited higher attention levels than the control group. StoryQ—a free web-based machine learning and text mining technology for K-12 students—explores the historical topic of redlining.

A team of researchers from North Carolina State University developed and implemented a week-long curricular intervention for high school sophomores using StoryQ to examine hundreds of neighborhood descriptions produced for the Home Owners Loan Corporation’s “residential security maps” in the late 1930s (The Concord Consortium, 2024). The researchers argue that it is possible for teachers to construct historical inquiries that aim to identify patterns in a large set of primary sources with the aid of AI (Nocera, Newton, & Shiang, 2024). Integrating AIML into history classrooms could potentially engage students in critical inquiry and develop their analytical skills, in particular, skills in recognizing biases in data sources and understanding the importance of context and language nuances in historical analysis.

A Healthy Blend of AI and Human Thought

AI has also been used to develop individualized assessments such as having an algorithm evaluate a piece of student writing using a prototype and then providing immediate feedback to the student based mostly on syntax. Grading student papers can be time consuming for teachers; however, if AI can help provide feedback for the student’s initial draft based more on grammar and sentence structure, then the teacher can spend more time on meaning making and the semantics of what the student is trying to convey in writing.

Utilizing AI to sort student data to identify trends and patterns may be helpful to teachers who may then need to differentiate their lessons based on the summative overview in front of them. The human perspective that a teacher provides is essential and can never be replaced by an algorithm.

Teachers also have that practical wisdom that can never be replaced by a computer and know how much AI should be integrated into their classroom, when and why. If students are chronically dependent upon AI as a substitute for learning, they will lose crucial opportunities to develop knowledge on their own and hone their critical thinking skills. Thus, teachers must intentionally create classroom lessons and assessments that force independent thinking and reflection in their students for their long-term success and ethical growth and development. The integrity of student outcomes needs to be at the forefront of our national discourse on how and when to integrate AI into the K-12 classroom.

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About the Author

science of reading

Samina Hadi-Tabassum is the Dean of the School of Education at Elmhurst University. Her research focuses on the intersection of race, language and culture. She teaches courses in language development, literacy and linguistics.

Posted April 23, 2024


Campos, F., Nguyen, H., Ahn, J., & Jackson, K. (2023). Leveraging cultural forms in human- centered learning analytics design. British Journal of Educational Technology.

Nocera, A., Newton, V. & Jiang, S. (2024). “They created segregation with the economy”: Using AI for a student-driven inquiry into redlining in the social studies classroom. Theory & Research in Social Education. 1-40.

Li, C., Zhu, W., Xing, W., & Guo, R. (2024). Analyzing Student Attention and Acceptance of Conversational AI for Math Learning: Insights from a Randomized Controlled Trial. Proceedings of LAK ’24: The 14th Learning Analytics and Knowledge Conference, Kyoto Japan.

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