AI education

With artificial intelligence (AI) technology proliferating through virtually every industry, the education space is set to see a new paradigm. However, although there are plenty of potential benefits to adopting this technology, educators must do so carefully, considering the ethical concerns of this technology.

Data Privacy and AI in Education

One key concern regarding AI use in educational settings is users’ data privacy because, as a data-driven technology, user data often serves as a fundamental part of training the AI models’ future output. With this in mind, educational institutions must carefully scrutinize privacy policies and terms of use to determine who owns student data collected by AI tools, how it is used, and how it is shared beyond educational contexts. This is essential to ensure that users’ data is not revealed, leaving them vulnerable to exploitation.

These data privacy concerns are incredibly complex in educational settings, however, because the data collected and used is from children. Organizations must maintain compliance with specific regulations if they are handling children’s data. One of the most obvious laws in the United States is COPPA (the Children’s Online Privacy Protection Rule), which requires businesses to obtain parental consent to sell the data of children under 13 and the consent of the individuals themselves if they are between ages 13 and 15. Some laws have been proposed that would increase these restrictions to apply to any children under the age of 18.

Businesses that fail to comply with accurate regulations like COPPA could be subject to hefty civil penalties of up to $51,744 per violation. Considering that many AI platforms collect and store the data of thousands, if not millions, of users, the potential liability of failing to comply with data protection laws regarding children’s data is frightening.

Algorithmic Bias in Education Use Cases for AI

Artificial intelligence also carries the potential for algorithmic bias, which can be dangerous in educational settings. Since AI models are still entirely dependent on pre-existing data, their output will reflect any biases in the data they are trained. If biased data is fed into a model, it can lead to unfair assessments, recommendations, and learning experiences for students — especially those from marginalized groups.

To mitigate this bias, educators who hope to implement artificial intelligence into the classroom must carefully consider the ethicality of the model and data they use. Training data must be used from a wide variety of sources, as this will help them avoid algorithmic bias and its potential consequences as much as possible.

Learning Gaps Created by AI

One of the primary use cases that proponents of artificial intelligence in educational settings have touted is the ability to personalize learning. With class sizes steadily increasing across the United States and teachers being increasingly overwhelmed, teachers are faced with a situation where they can’t always provide the individualized attention each student needs. For students who need additional assistance beyond even their peers, AI-powered personalized learning can be a tool to help them bridge their learning gaps.

However, for all the benefits of personalization in education, there can also be some unfortunate consequences. For one, personalization could create a situation where some students are far ahead of their peers. Although using artificial intelligence to identify learning gaps can be effective, the overuse of AI in educational settings could create even worse gaps. 

The potential formation of “filter bubbles” is also troubling, as AI may put students in a situation where they only encounter information reinforcing their existing beliefs. An essential part of the educational process is being exposed to diverse perspectives and learning critical thinking skills. 

Effectively Implementing AI in Educational Settings

Artificial intelligence tools are designed not as an outright replacement for teachers but rather as a tool to supplement their productivity. After all, teacher oversight is still required in education to ensure equitable and efficient learning outcomes. 

For example, a teacher could use artificial intelligence technology in the classroom as a way of helping students reinforce their learning with tools that analyze students’ understanding and focus more on the topics they need the most help with. However, the teacher should conduct the initial instruction of the material, allowing them to ensure all students are on a level starting ground.

Artificial intelligence can be a powerful tool in the classroom, helping students learn more effectively and teachers teach more efficiently, though it does not come without certain risks that educators should be aware of. Understanding the implications of AI technology — including data privacy, bias, and the creation of learning gaps — can help educators better integrate artificial intelligence for educational use cases.

 

Ed Watal is the founder and principal of Intellibus, an INC 5000 Top 100 Software firm based in Reston, Virginia. He regularly serves as a board advisor to the world’s largest financial institutions. C-level executives rely on him for IT strategy & architecture due to his business acumen & deep IT knowledge. One of Ed’s key projects includes BigParser (an Ethical AI Platform and an A Data Commons for the World).  He has also built and sold several Tech & AI startups. Prior to becoming an entrepreneur, he worked in some of the largest global financial institutions, including RBS, Deutsche Bank, and Citigroup. He is the author of numerous articles and one of the defining books on cloud fundamentals called ‘Cloud Basics.’ Ed has substantial teaching experience and has served as a lecturer for universities globally, including NYU and Stanford. Ed has been featured on Fox News, QR Calgary Radio, and Medical Device News.