AI in Education Trends 2024: Personalised Learning.

In 2024, one of the most promising trends in the use of AI is personalised learning. This leverages AI to tailor educational experiences to individual students' needs and learning styles. Here's a closer look at how AI drives personalised learning and what educators need to know to stay ahead of the curve:

Understanding Personalised Learning

Personalised learning is an educational approach that aims to customise learning for each student's strengths, needs and skills. Unlike the traditional one-size-fits-all model, personalised learning allows students to learn at their own pace, in their own way. As a former teacher, I know that this is not something that can be offered by a classroom teacher, given all the constraints present in the classroom. However, this approach recognises that every student is unique and that tailoring the educational experience can lead to better outcomes.

How AI Facilitates Personalised Learning

1. Adaptive Learning Platforms

AI-powered adaptive learning platforms are at the forefront of personalised learning. These platforms use algorithms to analyse students' performance in real-time and adjust the content accordingly. For example, if a student struggles with a particular concept, the system can provide additional resources and practice problems to reinforce understanding. Conversely, if students excel, the platform can present more challenging material to keep them engaged.

Examples:

Duolingo is a brilliant example. As a parent, I have seen the benefit of this method not only in the way it tailors the learning to learners’ needs but also makes it more engaging!

Adapt from Collins and CenturyTech are platforms that offer a truly adaptive learning experience used in schools.

2. Intelligent Tutoring Systems

Intelligent tutoring systems (ITS) are AI-driven applications that provide personalised instruction and feedback. These systems mimic the interaction between a student and a human tutor, offering hints, explanations, and tailored guidance. ITS can help students master complex subjects by providing immediate feedback and adapting to their learning progress.

Examples:

ROYBI provides a private tutoring experience that uses Artificial Intelligence.

Cognii uses chatbot-style conversational learning to engage students, guiding them towards conceptual mastery.

3. Predictive Analytics

Predictive analytics in education involves using AI to analyse historical and real-time data to predict future outcomes. Educators can use these insights to identify students at risk of falling behind and intervene early. By understanding patterns and trends, teachers can develop targeted strategies to support each student's success.

Examples:

Dreambox Learning, Lexia Learning and Khan Academy are examples of tools that analyse student performance data to predict areas where they might need additional support or recommend personalised learning pathways.

Benefits of Personalised Learning Through AI

1. Improved Student Engagement

When learning is tailored to individual needs and interests, students are more likely to stay engaged and motivated. Personalised learning can transform passive learning into an active and immersive experience.

2. Enhanced Learning Outcomes

By addressing each student's unique challenges and strengths, personalised learning can lead to better academic performance. Students can progress at their own pace, ensuring they fully grasp concepts before moving on.

3. Efficient Use of Educational Resources

AI can help educators make more efficient use of their time and resources. By automating routine tasks and providing insights into student performance, teachers can focus on delivering high-quality instruction and providing personalised support.

4. Equitable Education

Personalised learning can help bridge educational gaps by providing all students with the resources and support they need to succeed. This approach can be particularly beneficial for students with diverse learning needs and backgrounds.

Challenges and Considerations

While AI-powered personalised learning offers numerous benefits, there are also challenges to consider:

1. Data Privacy and Security

The use of AI in education involves collecting and analysing large amounts of student data. Ensuring the privacy and security of this data is paramount. Educators and institutions must adhere to strict data protection regulations and implement robust security measures.

2. Digital Divide

Access to AI-powered personalised learning tools requires reliable internet access and appropriate devices. Addressing the digital divide is essential to ensure that all students can benefit from these technologies.

3. Teacher Training

Educators need proper training to effectively integrate AI tools into their teaching practices. Professional development programs should focus on equipping teachers with the skills and knowledge to leverage AI for personalised learning.

4. Ethical Considerations

The use of AI in education raises ethical questions, such as algorithmic bias and the role of human judgment. Educators must critically assess AI tools and ensure they are used fairly and transparently.

Conclusion

By tailoring educational experiences to individual needs, AI can enhance student engagement, improve learning outcomes, and promote equitable education. By staying informed and proactive, educators can harness the power of AI to create a more personalised, effective, and inclusive learning environment for all students.

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