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Home Technology Artificial Intelligence

The evolution of personalised learning platforms

by Hassan Baickdeli
August 19, 2024
in Artificial Intelligence, Expert Contributors, Technology, Thought Leaders
Reading Time: 6 mins read
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Personalised learning platforms represent a paradigm shift in education. Image: Lenovo

Personalised learning platforms represent a paradigm shift in education. Image: Lenovo

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While challenges such as data privacy and equity must be navigated carefully, the promise of personalised learning platforms powered by AI offers a transformative vision for the future of education.

Mr Hassan Baickdeli, Head of Emerging Technology and Solutions at Lenovo, details how the tech company is helping schools harness AI for education, powered by Windows 11 Pro built for business.

Hassan Baickdeli. Image: Lenovo

In the rapidly evolving landscape of education, traditional classroom settings are being complemented and sometimes completely transformed by innovative technologies that promise to personalise learning experiences like never before. At the very core of this capability enabling this transformation are Personalised Learning Platforms (PLPs), powered by Artificial Intelligence (AI) bolstered by sophisticated data analytics. These platforms are revolutionising education by tailoring independent curated learning journeys, focusing on individual student needs, preferences, and learning styles.

Combined with Microsoft learning, security, and IT management solutions, our innovative technology is shaping the future of education. The Lenovo 13w Yoga Gen 2, powered by Windows 11 Pro, supports instructors and administrators across every hybrid learning environment. Upgradable memory and storage options easily adapt to increased workloads, providing faster connectivity for virtual lessons and peer collaboration. Windows 11 makes it easier for everyone.

Understanding personalised learning platforms

PLP’s utilise AI algorithms to analyse vast amounts of school, curriculum, and student data. This data includes learning preferences, strengths, weaknesses, and pace of learning. By leveraging machine learning techniques, these platforms can provide customised content, adaptive assessments, and real-time feedback, all aimed at optimising the learning journey and catering it to each specific student.

The core principle behind PLPs is the recognition that every student learns differently. Traditional one-size-fits-all approaches often leave some students behind while failing to challenge others. PLPs address this issue by dynamically adjusting the educational content and pace based on the student’s performance and comprehension.

How AI enhances personalised learning

AI serves as the backbone of PLPs, enabling them to perform several key functions:

  1. Data-Driven Insights: By analysing student data, AI can identify patterns in learning behaviours and predict future learning needs. This allows educators to intervene early when a student is struggling or provide advanced materials to those who excel.
  2. Adaptive Learning Paths: PLPs can dynamically adjust the sequence and difficulty of learning materials based on the student’s responses and progress. This ensures that each student is challenged appropriately and remains engaged.
  3. Personalised Content Recommendations: AI algorithms can recommend supplementary resources, such as articles, videos, or interactive exercises, that align with the student’s interests and learning objectives.
  4. Real-Time Feedback: Immediate feedback is crucial for learning. AI-powered PLPs can provide instant feedback on quizzes, assignments, and practice exercises, helping students understand their mistakes and correct misconceptions promptly.

Let’s look at cookie-based advertising as one example, we’ve all had the experience where we are searching for a new jumper/jacket or cap and then suddenly everywhere you are looking, you now have ads showing you deals on what you were searching for. Now imagine this from a student’s perspective where (throughout any part of their learning journey) from year 7 onwards things such as interests, content searches, progress is being ethically analysed to determine if they are struggling in music and perhaps showing more interest in engineering as an example. With AI analysing all the data presented to it, the student is shown suggestion (again based on interests) to course content flavoured around this. There are of course elements of security, framework and rigour that need to be in place here for these systems to deliver these types of suggestions and output with all elements of ethical and appropriate outcomes in mind.

Practical applications in education

The implementation of PLPs in educational institutions has yielded promising results across various educational levels:

  1. K-12 Education: In primary and secondary schools, PLPs have been used to support differentiated instruction in subjects like mathematics and language arts. For example, adaptive math programs can adjust the difficulty of problems based on a student’s performance in real-time.
  2. Higher Education: Universities and colleges are integrating PLPs into their online courses and blended learning environments. Students benefit from Personalised study plans and interactive learning modules that cater to their unique learning needs.
  3. Professional Development: Beyond traditional academia, PLPs are also being used in corporate training and professional development programs. AI-driven platforms can assess employees’ skills gaps and deliver targeted training modules to enhance performance.

Challenges and considerations

While the potential of PLPs is vast, several challenges and considerations must be addressed:

  1. Data Privacy: Handling sensitive student data requires robust security measures to protect privacy and comply with regulations from the Australian Government’s Federal Department of Education
  2. Equity and Accessibility: Not all students have equal access to technology at home or in school. Ensuring equitable access to PLPs is crucial to avoid widening the digital divide.
  3. Teacher Training: Educators need training and support to effectively integrate PLPs into their teaching practices and interpret AI-generated insights.

Future directions and innovations

Looking ahead, the evolution of PLPs continues with ongoing advancements in AI and educational technology:

  1. Natural Language Processing (NLP): AI-powered chatbots and virtual tutors can engage students in interactive conversations, providing on-demand assistance and explanations.
  2. Predictive Analytics: AI algorithms can predict future learning outcomes and recommend interventions to improve student performance proactively.
  3. Collaborative Learning Environments: PLPs may facilitate collaborative projects among students, leveraging AI to enhance group dynamics and productivity.

Personalised Learning Platforms represent a paradigm shift in education, harnessing the power of AI to cater to the individual needs of students. By offering customised learning experiences, adaptive assessments, and real-time feedback, PLPs empower educators and learners alike to achieve better educational outcomes. As technology continues to evolve, so too will the potential of PLPs to revolutionise education and make learning more engaging, effective, and accessible for all.

Lenovo and Windows 11 help you manage the modern classroom with technology built for education. Lenovo Devices with Windows 11 offer personalised learning experiences, making it easier to meet, teach and share content. New tools that reduce distraction, like background blur and settings to minimise visual clutter, help educators to streamline class management and focus on instruction.

In summary, while challenges such as data privacy and equity must be navigated carefully, the promise of Personalised Learning Platforms powered by AI offers a transformative vision for the future of education. A future where every student can learn at their own pace and reach their full potential.

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