Introduction

The integration of Artificial Intelligence (AI) in education has generated a wave of excitement and concern. Where I’m most excited is how it can help influence and structure more personalised learning, help individuals to achieve their career aspirations and achieve their true potential.

As we explore the future of personalised learning, it is essential to critically evaluate the benefits and downsides that AI brings to the table. While AI holds immense potential to revolutionise education, enhancing individualised learning experiences, there are also valid concerns regarding its impact on human interaction, data privacy, algorithmic bias, and equity. Let's try to examine both sides of the AI in education debate.

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Benefits of AI in Personalised Learning
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• Individualised Learning Paths: AI-powered systems analyse student data to create customised learning paths, allowing learners to progress at their own pace, focus on areas of need, and explore topics of interest. Examples include adaptive learning platforms like DreamBox and Knewton that adjust content based on individual performance.
• Enhanced Engagement: AI introduces interactive and immersive experiences through virtualreality (VR), augmented reality (AR), and gamification. Platforms like Nearpodand Classcraft employ AI to transform learning materials into interactivesimulations, virtual laboratories, and game-like scenarios.
• Intelligent Tutoring: AI-powered chatbots and virtual assistants act as intelligent tutors, providing real-time feedback, guidance, and support. Duolingo and Khan Academy employ AI to deliver personalised learning experiences and instant feedback.
• Data-Driven Insights: AI analyses educational data, offering valuable insights for educators to refine instructional strategies and make data-informed decisions. EdTech tools like Clever and Bright Bytes use AI to provide educators with actionable data and analytics.

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Some of the Downsides and Challenges of AI in Personalised Learning

• Loss of Human Interaction: There is concern that excessive reliance on AI may reduce human interaction and the essential role of educators in facilitating meaningful learning experiences.
• Data Privacy and Security: The collection and use of learner data raise concerns about privacy and security. Stricter policies and safeguards are needed to protect sensitive information from unauthorised access or misuse.
• Algorithmic Bias and Fairness: AI systems can perpetuate biases if trained on biased data, potentially leading to unequal opportunities and discrimination. Efforts must be made to address algorithmic bias and ensure fairness in personalised learning.
• Overreliance on Technology: Overdependence on AI-driven systems may lead to passive learning experiences, necessitating a balance between AI and activities that promote active learning, critical thinking, and independent exploration.
• Ethical Considerations: Ethical questions arise regarding the commercial use of student data, AI's potential to manipulate or influence learners, and the responsibility of AI systems in making important decisions. Ethical frameworks must guide AI use in education.
• Access and Equity: AI's potential to enhance personalised learning must be balanced with addressing disparities in access to technology, internet connectivity, and AI-powered tools to avoid deepening educational in equalities.

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Conclusion

The integration of AI in personalised learning brings both benefits and challenges. AI empowers individualised learning paths, enhances engagement, offers intelligent tutoring, and provides valuable data-driven insights. However, concerns exist regarding reduced human interaction, data privacy, algorithm. From a myAko perspective, our priority is examining the potential of personalised development pathways, which is an exciting opportunity to deliver more relevant, engaging learning experience and more meaningful outcomes. Both for the learner and the organisation they support.