Adaptive AI Systems in Education: Real-Time Personalised Learning Pathways for Skill Development

The integration of Adaptive Artificial Intelligence (AI) systems in educational settings has emerged as a transformative approach, offering tailored learning experiences that dynamically adapt to individual learners' needs, preferences and skill trajectories.This study explores the design, implementation and impact of AI-driven personalised learning pathways, focusing on their role in fostering skill development and improving educational outcomes.Drawing from advancements in adaptive learning platforms and intelligent tutoring systems, we analysed their mechanisms for real-time feedback, dynamic content delivery and personalised assessment.These systems utilise machine learning algorithms and natural language processing to identify gaps in understanding, recommend optimised learning pathways and adjust instructional strategies, thereby creating a highly responsive and student-centred learning environment.This research highlights key implementations, including the use of AI-enabled learning analytics to predict and address dropout risks, enhance engagement and support diverse learning styles.Notable examples, such as Knewton and DreamBox, illustrate the potential of adaptive learning to bridge educational disparities and promote equity by providing learners with customised resources and feedback.Furthermore, this paper addresses critical challenges, including ethical considerations, data privacy and algorithmic biases and offers a framework for the ethical deployment of AI in educational settings.By aligning adaptive AI systems with pedagogical principles, this study underscores their capacity to augment human instruction, enabling educators to focus on fostering higherorder cognitive skills and critical thinking.This exploration contributes to the growing discourse on AI in education and provides actionable insights for educators, technologists and policymakers.It emphasises the transformative potential of adaptive AI systems in preparing learners for the demands of a rapidly evolving digital economy, while fostering inclusivity and lifelong learning.The findings underscore the urgency of advancing these systems to effectively meet global educational challenges.

Authors

Publication Details

Journal
Journal of Artificial Intelligence Machine Learning and Data Science
Published
2025-03-15
DOI
https://doi.org/10.51219/jaimld/akinyemi-sadeeq-akintola/534
Citations
3
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
7.44
Controls
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article

Adaptive AI Systems in Education: Real-Time Personalised Learning Pathways for Skill Development

M.G. Michael, Chukwuka Michael Oforgu, Akinyemi Sadeeq Akintola, Michael Akintayo et al.
3 citations
Journal of Artificial Intelligence Machine Learning and Data Science
Intelligent Tutoring Systems and Adaptive Learning
7.44
article

Adaptive AI Systems in Education: Real-Time Personalised Learning Pathways for Skill Development

M.G. Michael, Chukwuka Michael Oforgu, Akinyemi Sadeeq Akintola, Michael Akintayo, Maxwell Nwanna, Temitope Kadri
article en
3 citations

Abstract

The integration of Adaptive Artificial Intelligence (AI) systems in educational settings has emerged as a transformative approach, offering tailored learning experiences that dynamically adapt to individual learners' needs, preferences and skill trajectories.This study explores the design, implementation and impact of AI-driven personalised learning pathways, focusing on their role in fostering skill development and improving educational outcomes.Drawing from advancements in adaptive learning platforms and intelligent tutoring systems, we analysed their mechanisms for real-time feedback, dynamic content delivery and personalised assessment.These systems utilise machine learning algorithms and natural language processing to identify gaps in understanding, recommend optimised learning pathways and adjust instructional strategies, thereby creating a highly responsive and student-centred learning environment.This research highlights key implementations, including the use of AI-enabled learning analytics to predict and address dropout risks, enhance engagement and support diverse learning styles.Notable examples, such as Knewton and DreamBox, illustrate the potential of adaptive learning to bridge educational disparities and promote equity by providing learners with customised resources and feedback.Furthermore, this paper addresses critical challenges, including ethical considerations, data privacy and algorithmic biases and offers a framework for the ethical deployment of AI in educational settings.By aligning adaptive AI systems with pedagogical principles, this study underscores their capacity to augment human instruction, enabling educators to focus on fostering higherorder cognitive skills and critical thinking.This exploration contributes to the growing discourse on AI in education and provides actionable insights for educators, technologists and policymakers.It emphasises the transformative potential of adaptive AI systems in preparing learners for the demands of a rapidly evolving digital economy, while fostering inclusivity and lifelong learning.The findings underscore the urgency of advancing these systems to effectively meet global educational challenges.

Journal of Artificial Intelligence Machine Learning and Data ScienceVol. 3(1)
Quality Education
Openalex Percentile: Top 3%
Intelligent Tutoring Systems and Adaptive Learning
7.44
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