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Cited by 436Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Healthcare and Education

AI in Education: A Review of Personalized Learning and Educational Technology

Open access
Oyebola Olusola Ayeni, Nancy Mohd Al Hamad, Onyebuchi Nneamaka Chisom, Blessing Osawaru, Ololade Elizabeth Adewusi·2024·GSC Advanced Research and Reviews
DOI 10.30574/gscarr.2024.18.2.0062
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Abstract

The integration of Artificial Intelligence (AI) in education has ushered in a transformative era, redefining traditional teaching and learning methods. This review explores the multifaceted role of AI in education, with a particular focus on personalized learning and educational technology. The synergy between AI and education promises to address individualized needs, enhance student engagement, and optimize learning outcomes. Personalized learning, enabled by AI algorithms, tailors educational experiences to the unique needs, preferences, and pace of each student. This approach goes beyond a one-size-fits-all model, fostering a more inclusive and effective learning environment. The review delves into the diverse applications of AI-driven personalized learning, ranging from adaptive content delivery and real-time feedback to intelligent tutoring systems. It analyzes the impact of these technologies on student performance, highlighting the potential to narrow educational gaps and cater to diverse learning styles. Educational technology, powered by AI, extends beyond the classroom, encompassing online platforms, virtual reality, and interactive tools. The review explores the integration of AI in curriculum development, content creation, and assessment methods, offering insights into how these technologies augment the teaching and learning experience. Furthermore, the review examines the role of AI in automating administrative tasks, allowing educators to redirect their focus towards personalized instruction. Challenges and ethical considerations associated with the adoption of AI in education are also scrutinized. Privacy concerns, algorithmic biases, and the digital divide are discussed, emphasizing the importance of responsible AI implementation. The review underscores the need for collaborative efforts among educators, policymakers, and technologists to establish ethical guidelines and ensure the equitable distribution of AI-enhanced educational resources. This review provides a comprehensive examination of the evolving landscape of AI in education, with a spotlight on personalized learning and educational technology. As the symbiosis between AI and education continues to evolve, this synthesis of current research and trends aims to guide future developments, fostering an informed and progressive approach to the integration of AI in the educational sphere.

AI snapshot

Written from the paper’s full text.

TL;DR

This narrative review examines AI in education with emphasis on personalized learning and AI-enhanced educational technology, arguing that these tools can tailor instruction, improve engagement, and support equity while raising ethical and access concerns. It calls for responsible implementation, ethical guidelines, and collaboration among educators, policymakers, and technologists.

Key findings

  • AI-driven personalized learning uses algorithms to adapt content, pacing, and assessment to individual students, including adaptive content delivery, real-time feedback, and intelligent tutoring systems.
  • AI-enhanced educational technology is described as transforming curriculum development, content creation, assessment, virtual reality/interactive tools, and administrative automation.
  • The review identifies major challenges: privacy concerns, algorithmic biases, the digital divide, and the need for ethical guidelines and responsible AI usage.
  • AI is presented as potentially advancing educational equity by addressing diverse learning styles, improving accessibility, and fostering inclusive learning environments, though no quantitative outcomes are reported.

Methods

  • Design: narrative review of AI in education, focused on personalized learning and educational technology.
  • Data or sample: Not stated in the paper; no systematic search strategy, databases, inclusion criteria, or study sample are described.
  • Main analysis: thematic synthesis of existing research and trends, with sections on personalized learning, EdTech, challenges, equity, future directions, and recommendations.

Limitations

  • The paper does not state limitations of the review itself.
  • Evident from the text: it lacks a systematic review protocol, search dates, inclusion/exclusion criteria, and empirical data or quantitative outcomes; its claims are broad and largely supported by cited literature rather than original analysis.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.30574/gscarr.2024.18.2.0062

Cites

50 of 50 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 64 cited sources checked · 0 supported · 0 flagged

  • Premisereasoning

    AI's integration in curriculum development enables educators and curriculum developers to tailor instructional content, ensuring relevance and alignment with evolving educational standards.

    “This data-driven approach enables educators and curriculum developers to tailor instructional content, ensuring relevance and alignment with evolving educational standards.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Ahmad, et. al., 2023

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    • cites Górriz, et. al., 2020

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    • cites Luan, et. al., 2020

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  • Premisecitation

    AI algorithms analyze vast amounts of data, including students' progress, performance, and preferences, to tailor educational experiences.

    “AI algorithms analyze vast amounts of data, including students' progress, performance, and preferences, to tailor educational experiences.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Kabudi, Pappas & Olsen, 2021

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    • cites Zhai, et. al., 2021

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  • Premisecitation

    Intelligent tutoring systems, powered by AI, offer personalized guidance, adapting to the pace at which students grasp concepts.

    “Intelligent tutoring systems, powered by AI, offer personalized guidance, adapting to the pace at which students grasp concepts.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Kabudi, Pappas & Olsen, 2021

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    • cites Zhai, et. al., 2021

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  • Premisecitation

    Intelligent tutoring systems leverage AI algorithms to emulate human tutors, providing tailored instruction and support.

    “Intelligent tutoring systems (ITS) represent a sophisticated application of AI in personalized learning.These systems leverage AI algorithms to emulate human tutors, providing tailored instruction and support.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Akyuz, 2020

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    • cites Conati, et. al., 2021

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    • cites Kim & Kim, 2020

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  • Premisecitation

    AI facilitates real-time data analysis, enabling educators to gain insights into student engagement, comprehension, and areas of struggle.

    “Moreover, AI facilitates real-time data analysis, enabling educators to gain insights into student engagement, comprehension, and areas of struggle.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Kabudi, Pappas & Olsen, 2021

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    • cites Zhai, et. al., 2021

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  • May combine two claims. 15
  • Classified as probably not a substantial claim. 9

  • May combine two claims.

    Premisecitation

    This versatility ensures that each student can access and engage with the content in a manner that aligns with their preferred learning style, fostering a more inclusive and effective educational experience.

    “This versatility ensures that each student can access and engage with the content in a manner that aligns with their preferred learning style, fostering a more inclusive and effective educational experience (Kabudi, Pappas & Olsen, 2021, Luan & Tsai, 2021, Regan & Jesse, 2019).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Kabudi, Pappas & Olsen, 2021

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    • cites Luan & Tsai, 2021

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    • cites Regan & Jesse, 2019

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  • May combine two claims.

    Premisecitation

    By continuously assessing individual progress, AI ensures that learners receive content that aligns with their current proficiency levels, preventing boredom or frustration and promoting optimal learning conditions.

    “By continuously assessing individual progress, AI ensures that learners receive content that aligns with their current proficiency levels, preventing boredom or frustration and promoting optimal learning conditions (Anis, 2023, Khonturaev, 2023, Tapalova & Zhiyenbayeva, 2022).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Anis, 2023

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    • cites Khonturaev, 2023

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    • cites Tapalova & Zhiyenbayeva, 2022

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  • Classified as probably not a substantial claim.

    Premisecitation

    Personalized learning, underpinned by AI algorithms, tailors educational experiences to the unique aptitudes, learning styles, and progress trajectories of individual students.

    “It transcends the limitations of traditional onesize-fits-all approaches, tailoring educational experiences to the unique aptitudes, learning styles, and progress trajectories of individual students (Miao, et. al., 2021, Pedro, et. al.,2019, Tan, 2023).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Miao, et. al., 2021

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    • cites Pedro, et. al.,2019

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    • cites Tan, 2023

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  • May combine two claims.

    Premisecitation

    The effectiveness of intelligent tutoring systems lies in their ability to address specific learning gaps, reinforce concepts, and adaptively respond to the evolving needs of each student.

    “The effectiveness of intelligent tutoring systems lies in their ability to address specific learning gaps, reinforce concepts, and adaptively respond to the evolving needs of each student (Akyuz, 2020, Conati, et. al., 2021, Kim & Kim, 2020).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Akyuz, 2020

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    • cites Conati, et. al., 2021

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    • cites Kim & Kim, 2020

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  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    AI's impact on education is far-reaching, shaping the future of education.

    “From revolutionizing curriculum development to enhancing assessment methods, virtual reality experiences, and administrative efficiency, AI's impact is far-reaching, shaping the future of education and preparing learners for the challenges of the 21st century.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI-enhanced VR platforms offer immersive learning environments that enhance retention and cater to various learning styles.
    • Rests on an assumption: AI in administrative functions reduces workload, minimizes errors, and enhances overall operational efficiency.

  • May combine two claims.

    Premisecitation

    AI-powered assessment tools provide timely and actionable feedback, allowing educators to tailor their instructional strategies and address specific learning gaps.

    “AI-powered assessment tools provide timely and actionable feedback, allowing educators to tailor their instructional strategies and address specific learning gaps (Babitha, Sushma & Gudivada, 2022, Gardner, O'Leary & Yuan, 2021, Hassoun, et. al.,2023).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Babitha, Sushma & Gudivada, 2022

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    • cites Gardner, O'Leary & Yuan, 2021

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    • cites Hassoun, et. al.,2023

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  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationreasoning

    AI-powered personalized learning adaptability is particularly impactful in reducing achievement gaps and fostering a more equitable educational landscape.

    “This adaptability is particularly impactful in reducing achievement gaps and fostering a more equitable educational landscape.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    AI-driven personalized learning enhances academic outcomes and addresses the diverse needs of learners.

    “By embracing the principles of flexibility, adaptability, and student agency, AIdriven personalized learning not only enhances academic outcomes but also addresses the diverse needs of learners.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Personalized learning experiences lead to increased student engagement, motivation, and a deeper understanding of the material.
    • Rests on an assumption: AI algorithms that adaptively deliver content contribute to improved retention and comprehension, ultimately translating into enhanced academic outcomes.

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Educational technology enhanced by AI, from adaptive content delivery to virtual reality applications, has contributed to creating more engaging and accessible learning environments.

    “Educational technology enhanced by AI, from adaptive content delivery to virtual reality applications, has contributed to creating more engaging and accessible learning environments.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: VR and AR immersive technologies enhance learning by providing interactive and three-dimensional experiences.

  • May combine two claims.

    Interpretationreasoning

    The integration of AI in education brings both transformative opportunities and challenges, including privacy concerns, algorithmic biases, and the digital divide.

    “In conclusion, the integration of AI in education brings forth a transformative wave of opportunities and challenges.Privacy concerns, algorithmic biases, the digital divide, and the need for ethical guidelines underscore the importance of approaching AI implementation with careful consideration.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    The ability of AI to recognize and accommodate individual needs fosters a sense of belonging, promoting an inclusive educational experience that empowers all learners.

    “The ability of AI to recognize and accommodate individual needs fosters a sense of belonging, promoting an inclusive educational experience that empowers all learners (Knox, Wang & Gallagher, 2019, Salas-Pilco, Xiao & Oshima, 2022).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Knox, Wang & Gallagher, 2019

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    • cites Salas-Pilco, Xiao & Oshima, 2022

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  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    Personalized learning driven by AI algorithms has shown remarkable effectiveness in addressing diverse learning styles, reducing educational disparities, and fostering improved academic outcomes.

    “Personalized learning, driven by AI algorithms, has shown remarkable effectiveness in addressing diverse learning styles, reducing educational disparities, and fostering improved academic outcomes.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    The integration of AI into educational technology has the potential to create a more adaptive, personalized, and engaging learning environment.

    “As technology continues to advance, the synergy between AI and educational technology has the potential to create a more adaptive, personalized, and engaging learning environment.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI-enhanced VR platforms offer immersive learning environments that enhance retention and cater to various learning styles.

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning

    Responsible AI integration has the potential to bridge educational gaps, cater to individual needs, and enhance the overall quality of education.

    “The findings suggest that responsible AI integration has the potential to bridge educational gaps, cater to individual needs, and enhance the overall quality of education.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Machine learning algorithms analyze student performance data, identifying strengths, weaknesses, and patterns of understanding.

    “Machine learning algorithms analyze student performance data, identifying strengths, weaknesses, and patterns of understanding.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Babitha, Sushma & Gudivada, 2022

      Not checked in the free run.

    • cites Gardner, O'Leary & Yuan, 2021

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    • cites Hassoun, et. al.,2023

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  • May combine two claims.

    Premisecitation

    AI systems collect and analyze vast amounts of student data, including performance metrics, learning patterns, and behavioral data, creating potential for privacy breaches.

    “As AI systems collect and analyze vast amounts of student data, including performance metrics, learning patterns, and behavioral data, the potential for privacy breaches becomes a critical issue.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Butt, et. al., 2022

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    • cites Khosravi, et. al., 2022

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    • cites Malhotra, et. al., 2021

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  • Classified as probably not a substantial claim. May combine two claims.

    Interpretationnone given

    The integration of AI in education has challenges, including privacy concerns, algorithmic biases, and the digital divide.

    “However, the integration of AI in education is not without challenges, including privacy concerns, algorithmic biases, and the digital divide, highlighting the need for responsible and ethical practices.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Chatbots can be integrated into learning platforms, providing students with on-demand assistance and fostering a more interactive and engaging learning experience.

    “Chatbots can be integrated into learning platforms, providing students with on-demand assistance, fostering a more interactive and engaging learning experience (Muniasamy, & Alasiry, 2020, Van der Vorst & Jelicic, 2019).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Muniasamy, & Alasiry, 2020

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    • cites Van der Vorst & Jelicic, 2019

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning

    AI's role in fostering inclusive learning environments extends beyond adapting content to creating a culture of diversity, equity, and inclusion.

    “AI's role in fostering inclusive learning environments goes beyond adapting content; it extends to creating a culture of diversity, equity, and inclusion.”
    Quote in the paperNot a substantial claim
19 of 19

  • The paper relies on this without support.

    Personalized learning experiences lead to increased student engagement, motivation, and a deeper understanding of the material.

    “Research indicates that personalized learning experiences lead to increased student engagement, motivation, and a deeper understanding of the material.”

  • The paper relies on this without support.

    AI algorithms that adaptively deliver content contribute to improved retention and comprehension, ultimately translating into enhanced academic outcomes.

    “AI algorithms that adaptively deliver content contribute to improved retention and comprehension, ultimately translating into enhanced academic outcomes.”

  • The paper relies on this without support.

    Real-time feedback mechanisms powered by AI contribute significantly to the enhancement of learning outcomes.

    “In the realm of personalized learning, real-time feedback mechanisms powered by AI contribute significantly to the enhancement of learning outcomes.”

  • The paper relies on this without support.

    AI-enhanced VR platforms offer immersive learning environments that enhance retention and cater to various learning styles.

    “AI-enhanced VR platforms offer immersive learning environments, allowing students to explore concepts in a three-dimensional space.”
    “This not only enhances retention but also caters to various learning styles, making complex subjects more accessible.”

  • The paper relies on this without support.

    AI in administrative functions reduces workload, minimizes errors, and enhances overall operational efficiency.

    “The implementation of AI in administrative functions reduces workload, minimizes errors, and enhances overall operational efficiency.”

  • The paper relies on this without support.

    Historically, students from marginalized communities, remote areas, or with physical disabilities have faced challenges in accessing quality education.

    “Historically, students from marginalized communities, remote areas, or with physical disabilities have faced challenges in accessing quality education.”

  • The paper relies on this without support.

    AI algorithms can analyze and adapt content to cater to different learning styles, making educational materials more accessible to a broader range of students.

    “For example, AI algorithms can analyze and adapt content to cater to different learning styles, making educational materials more accessible to a broader range of students.”

  • The paper relies on this without support.

    AI-enhanced tools such as speech-to-text and text-to-speech applications assist students with diverse needs, ensuring educational resources are available and tailored to individual requirements.

    “Furthermore, AI-enhanced tools, such as speech-to-text and text-to-speech applications, assist students with diverse needs, ensuring that educational resources are not only available but also tailored to individual requirements.”

  • The paper relies on this without support.

    AI technologies can facilitate the development of learning materials that incorporate diverse perspectives, backgrounds, and cultural contexts.

    “AI technologies can facilitate the development of learning materials that incorporate diverse perspectives, backgrounds, and cultural contexts.”

  • The paper relies on this without support.

    AI can assist educators in identifying and addressing implicit biases in teaching materials and methodologies.

    “Additionally, AI can assist educators in identifying and addressing implicit biases in teaching materials and methodologies.”

  • The paper relies on this without support.

    VR and AR immersive technologies enhance learning by providing interactive and three-dimensional experiences.

    “These immersive technologies enhance learning by providing interactive and three-dimensional experiences.”
11 of 11

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The growth reflects a paradigm shift rather than a temporary spike.

    “This reflects a paradigm shift rather than a temporary spike, reinforcing findings from recent reviews that describe AI as an increasingly integral component of pedagogical design, personalized learning, and assessment strategies [11,32,42].”

    From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [32]

  • There is strong evidence that Embodied and Immersive Systems and Intelligent Tutoring Systems are effective in fostering engagement and deep learning.

    “Meanwhile, Embodied and Immersive Systems and Intelligent Tutoring Systems remain less frequent, despite strong evidence of their effectiveness in fostering engagement and deep learning [32].”

    From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [32]

Cites

Flagged first, then by citations

5 of the 55 references couldn’t be matched to a known paper.

Research works printed but not linked: 7

  1. Anis, M. (2023). Leveraging Artificial Intelligence for Inclusive English Language Teaching: Strategies and Implications for Learner Diversity. Journal of Multi-disciplinary Educational Research, 12(6).
  2. Babitha, M. M., Sushma, C., & Gudivada, V. K. (2022). Trends of Artificial Intelligence for online exams in education. International journal of Early Childhood special Education, 14(01), 2457-2463.
  3. Haider, U. (2023). Innovative Pedagogy: Melding Interdisciplinary and Artificial Intelligence in Education. Journal Environmental Sciences And Technology, 2(1), 176-183.
  4. Khanna, S., & Srivastava, S. (2020). Patient-Centric Ethical Frameworks for Privacy, Transparency, and Bias Awareness in Deep Learning-Based Medical Systems. Applied Research in Artificial Intelligence and Cloud Computing, 3(1), 16-35.
  5. Khonturaev, S. I. (2023). The Evolution Of Artificial Intelligence: A Comprehensive Exploration For Higher Education. Best Journal of Innovation in Science, Research and Development, 2(11), 700-706.
  6. Mustafa, D. (2023). Smart Classrooms, Bright Minds: The Intersection of Education and Artificial Intelligence. Journal Environmental Sciences And Technology, 2(1), 150-158.
  7. Singh, R. J. (2023). Transforming Higher Education: The Power of Artificial Intelligence. International Journal of Multidisciplinary Research in Arts, Science and Technology, 1(3), 13-18.

Cited by

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Related

Authors
Ayeni, O. O., Hamad, N. M. A., Chisom, O. N., Osawaru, B., & Adewusi, O. E.
Affiliations
  • Lead City University (NG)
  • National Examinations Council (NG)
Corresponding author
Oyebola Olusola Ayeni
Venue
GSC Advanced Research and Reviews
Publisher
GSC Online Press
Published
2024-02-28
Volume
18
Issue
2
Pages
261-271
ISSN
2582-4597 (ISSN-L)
DOI
10.30574/gscarr.2024.18.2.0062
OpenAlex
W4392772072
Cited by
436
References
55
Citations by year
2026: 149 · 2025: 221 · 2024: 66
FWCI
163.29
Type
journal article
Language
en
Open access
Licence
cc-by-nc-sa
Locations
GSC Advanced Research and Reviews (publishedVersion, cc-by-nc-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
artificial intelligence in education, personalized learning, educational technology, adaptive learning, intelligent tutoring systems, real-time feedback, student engagement, curriculum development, automated assessment, administrative automation, algorithmic bias, student privacy, digital divide, ethical AI, educational equity

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