How to Use AI for Personalized Learning

Education is quickly becoming the new battleground for how Artificial Intelligence (AI) is used by students and teachers alike with many fearing that applications like ChatGPT helping students avoid classwork. However, AI poses many opportunities for greater customization to learning, helping teachers craft more personalized learning materials for students.

Personalized learning, when combined with AI, creates a multitude of benefits for students, especially in classrooms where comprehension ranges drastically between competent and unsure. So, let’s dive into how AI can be used for personalized learning, improving student engagement and understanding. 

Background of AI in Education

Technology has always played a significant role in the classroom from the early installation of computers in school during the second half of the 20th century to the notion of intelligent tutoring systems that started to arise in the late 1970s with content that provided personalized feedback to student performance. During the 1980s and 1990s, more complex learning systems emerged with the development of machine learning which could be used to highlight the needs of specific students. However, these programs were limited at the time because AI and computer development were still in their infancy and unable to process complex problem-solving. 

By the turn of the 21st century, the internet became more commonplace in classrooms, giving students immediate access to massive amounts of information. While some of this information was difficult to verify (like content from Wikipedia) it still served as a useful reference for many. At the same time, deep learning was also becoming stronger, implementing natural language processing features into word processors that helped students identify and correct spelling/grammar mistakes more readily. 

The use of AI added another layer of complexity during the COVID-19 pandemic when students were required to stay indoors during quarantine lockdowns, relying on video call programs to attend class. This major shift in learning worldwide showed how heavily education systems relied on internet access and computer technology, resulting in major amounts of data collection. 

Importance of Personalized Learning

The challenges of traditional teaching methods have been understood for a long time without any clear solutions. Standard classroom teaching approaches learning with a one-size-fits-all approach that tends to cater to the general student population on a bell curve. This can cause problems for students who struggle to keep up with others and students who excel in the class and need more stimulating materials. 

With AI education systems, personalized learning can solve this dilemma by generating content that is tailored to every student’s abilities. Not only will this help with student engagement, but it can also help teachers with classroom management. Disruptive students tend to have trouble focusing on work that is too difficult and using personalized learning materials can help keep them motivated because the material will be within their range of abilities. 

Integrating AI in Personalized Learning Systems

With computers becoming so prevalent in many classrooms around the world, there are a variety of ways that AI can be implemented to help with student learning. Examples include:

• Data Mining: Data collection on student performance can help teachers evaluate how well the class is learning as a whole and identify key areas for improvement. This can also help teachers identify their own weaknesses. 

• Predictive Analytics: Predictive algorithms can help teachers prepare students when introducing new concepts and materials that cause students to fall behind. This can be helpful in subjects like math where the leap from arithmetic to algebra can be especially difficult. 

• Chatbots: Virtual assistants can provide students with direct access to tutoring chatbots that can assist them when the teacher is busy with another student or unavailable. 

• Pattern Recognition: Machine learning algorithms can spot patterns in a student’s performance, showcasing improvements or flagging potential cheating.

• Natural Language Processing: NLP can be used to assist with writing, giving students dedicated help with sentence structure and expression. 

Challenges of Implementing AI

While AI has many applications in personalized learning, chatbots also pose a major threat to student evaluation and cheating/plagiarism. They can auto-complete essays and other critical thinking tasks in mere seconds, tempting unmotivated students who would rather do something besides study. 

This is posing a major concern for teachers around the world who are struggling to find ways to stop and mitigate the use of AI with methods like turning in essays written on paper or relying on plagiarism detection bots themselves. However, avoiding AI and telling students to ignore it instead of teaching them how to utilize new technology is the true fault here. 

Future Prospects of AI in Personalized Learning

AI is unquestionably becoming the next major technological apparatus for student learning. It stands in a long line of revolutionary innovations like the computer and smartphone, the internet, and even simpler devices like the disposable pen and even the printing press. 

When we look at the future of education, there is no doubt that it will have a stronger emphasis on AI and how to use it than we might be ready for, but that doesn’t mean we need to avoid it. There are many ways that AI can be applied to personalized learning and used to improve student performance instead of falling back on rudimentary styles of teaching that forgo the use of technology altogether.