Innovation in Education: Leveraging AI for personalised learning

In the last decade accelerated advancement of technology has changed the way we live, work and learn. Educational institutions have found emerging technologies like AI and Data Science beneficial in improving the teaching-learning outcomes by fostering adaptability and engagement. 

The traditional approach towards education follows the same set of curricula for all the students and at the same pace. This method often fails to address the unique needs and learning styles of individual students. AI in education offers teachers or educators the potential to customise the learning experience for each student, making it easier and a better learning experience for them.  

AI – the GPS for education and learning 

Imagine a classroom where every lesson is intricately customised to each student’s learning curve. This is becoming possible with the new approach of learning with AI. It’s not merely about offering different content but tailoring the learning experience itself. It is like a GPS for education - instead of a singular route, multiple pathways are devised, all leading to the same destination but adjusted to the learner’s pace, preference, and proficiency.  

AI based models can be used to assess the data of the learners and develop intelligent tutoring systems that can actively interact with each student, giving valuable feedback, making their learning experience better and easier. Additionally, AI-driven tutoring systems can assess students’ understanding, identify areas of difficulty, and offer targeted explanations, and practice exercises, adapting the learning material to each student's progress. 

Personalised learning models benefitting students 

Flexible learning: Personalized learning allows students to learn at their own pace aligning with their personal schedule. This flexibility is perfect for students with busy lives or other commitments. This also helps them learn about multiple courses at once.  

Improved Engagement: Personalized learning is like getting a custom-made learning experience for the students. It keeps students interested because it gives them exactly what they need and want to learn about. When students are learning about things that interests them, they feel more excited and motivated to learn. This makes the whole learning process more fun and satisfying for the students.  

Better Outcomes: When learning is personalized, students perform better. They retain more of what they are taught, resulting in better performance in tests and assignments. This happens because the learning materials are tailored to fit each student's unique talent or abilities and cater to their specific needs. When students feel that the learning is catering to them, they understand things better and can use what they learn more effectively in the real world. 

Emerging technologies helping universities evolve  

Traditionally, universities have employed a one-size-fits-all approach, where students follow a predetermined curriculum and are assessed using the standard of method of learning. However, this approach does not always accommodate the diverse learning needs and preferences of individual students. Technological innovation, pedagogical evolution and changing educational priorities have contributed to the growing adoption of personalised learning models among the universities. These teaching-learning models are closely aligned to the changing needs of students.  

Additionally, universities need to tailor-make their curricula to cater to the industry needs. Courses in AI & Data Science would enable students to get acquainted with the advanced tools and will help them stand out. 

As we steer through this era of technological transformation, embracing AI and Data Science to improve learning outcomes is no longer an option but a necessity. Through personalised learning approach, universities can ensure that the students get what they are seeking as they pursue their degrees.  

The author is President, NIIT University.

Disclaimer: The views expressed above are the author's own and do not reflect those of DNA.