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Cited by 581Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

AI‐driven adaptive learning for sustainable educational transformation

Open access
Strielkowski, Wadim, Grebennikova, Veronika, Lisovskiy, Alexander, Rakhimova, Guzalbegim, Vasileva, Tatiana·2025·Sustainable Development
DOI 10.1002/sd.3221
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Abstract

This paper scrutinizes how adaptive learning technologies and artificial intelligence (AI) are transforming today's education by making it personalized, accessible, and efficient as well as leading people to accepting, addressing, and mitigating sustainable development. Recently, education witnessed a remarkable technological surge driven by various advances in technology, which can be demonstrated by the increase of the number of scientific publications on this topic from just 1 in 1990 to 636 in 2023. Ongoing digitalization and technological revolution in education together with the novel approach to respect each student's unique learning style and abilities paved the way for adaptive learning technologies represented by the innovative tools that personalize educational experiences to cater to individual learners. All of that contributes to preparing more educated and informed citizens, drives innovation, and supports economic growth necessary for achieving a sustainable future. Our bibliographic study employs VOSviewer to conduct a bibliometric analysis of a total number of 3518 selected publications using the keywords “adaptive learning” and “AI” (represented by articles, proceeding papers, and book chapters) indexed in the Web of Science (WoS) database from 1990 to 2024. Our results demonstrate that recent technological changes played a key role in transforming adaptive learning, which was rather reinforced by the “digital surge” in education brought about by the COVID‐19 pandemic. Our findings can be useful for further development in the field of adaptive education where they can be employed by the relevant stakeholders and policymakers as well as by the scholars and researchers.

AI snapshot

Written from the paper’s full text.

TL;DR

This paper reviews and bibliometrically maps AI-driven adaptive learning, arguing that it personalizes education and can support sustainable education, especially in the post-COVID era. Using 3518 Web of Science publications, it identifies three research clusters and discusses benefits alongside privacy, bias, cost, and equity challenges.

Key findings

  • The bibliometric sample contains 3518 publications indexed in Web of Science from 1990 to 2024 using “adaptive learning” and “artificial intelligence,” including 2501 articles and 762 proceeding papers; publications increased from 1 in 1990 to 636 in 2023, with a main spike around 2018–2019 enhanced by the COVID-19 “digital surge.”
  • VOSviewer keyword cluster analysis identified three main clusters: “intelligence learning” (red), “parameter prediction” (green), and “dataset classification” (blue).
  • The paper reports that adaptive learning and AI can personalize content, pacing, and feedback; improve engagement and academic performance; and support SDGs including SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action).
  • It identifies implementation challenges including algorithmic bias, privacy and data-security concerns, over-reliance on AI, reduced human interaction, infrastructure and cost barriers, the digital divide, educator resistance, and cultural/institutional barriers.

Methods

  • Bibliometric analysis using VOSviewer (Version 1.6.18) on the Web of Science database, searching for “adaptive learning” and “artificial intelligence” and retrieving 3518 indexed publications (2501 articles and 762 proceeding papers) from 1990 to 2024; the publication trend excludes 2024 to avoid incomplete-year bias.
  • Main analysis includes co-occurrence and keyword cluster analysis, network maps, density visualization, and bibliographic network analysis, supplemented by a structured literature review, case examples, and Google Trends search-interest data.

Limitations

  • Stated: the study focuses on broad implications without deeply addressing specific cultural, contextual, or socioeconomic nuances; its scope is rooted in Web of Science bibliometric data up to the knowledge cutoff, so it may miss recent developments; other databases such as Scopus, Google Scholar, ResearchGate, or Dimensions might yield more holistic results; Web of Science predominantly indexes English-language and Western publications, potentially excluding other research; bibliometric analysis is limited to indexed journals, relies on citation counts that may not reflect quality or practical relevance, and can overlook study context and content.
  • Evident from the text: the paper does not conduct a primary experimental or longitudinal test of learning outcomes; its case studies are illustrative rather than systematically sampled, and its findings are descriptive/bibliometric rather than causal.

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Integrity signals

Registry

Crossref · 10.1002/sd.3221

Cites

153 of 153 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 79 cited sources checked · 0 supported · 0 flagged

  • Findingnone given

    The number of scientific publications on this topic increased from 1 in 1990 to 636 in 2023.

    “the increase of the number of scientific publications on this topic from just 1 in 1990 to 636 in 2023”
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  • Findingown data

    The most-cited publications on adaptive learning and AI focus on e-learning and student-professor interactions in the classroom.

    “One can observe from Table 3 that even though some of the most-cited publications are (logically) quite old and some of them are review studies, the focus that can be traced is on the e-learning and student-professor interactions in the classroom.”
    Quote in the paperNot a substantial claim
  • Findingown data

    The bibliometric network analysis identified three main clusters.

    “Our results of the bibliometric network analysis demonstrate that three main clusters were identified.”
    Quote in the paperNot a substantial claim
  • Findingcitation

    Our results demonstrate that adaptive learning technologies, powered by AI, are transforming education fundamentally.

    “When it comes to tackling RQ1, our results demonstrate that adaptive learning technologies, powered by AI, are transforming education fundamentally (Cheung et al., 2021;Ouyang & Jiao, 2021).”
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    • cites Ouyang & Jiao, 2021

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  • Findingown data

    The main spike in publications on adaptive learning and AI occurred around 2018-2019.

    “Looking at the trends shown in Figure 5, it becomes apparent that the main spike in the publications on adaptive learning and AI occurred around 2018-2019”
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    Findingown data

    Top prolific institutions publishing on adaptive learning and AI include the University of London, University of California, and Chinese Academy of Sciences.

    “One can see very prestigious institutions such as University of London, University of California, or Chinese Academy of Sciences being among them (see Figure 7).”
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    Findingown data

    The utilization of classification methodologies discerns patterns and relationships within complex datasets.

    “Central among these is the utilization of classification methodologies to discern patterns and relationships within complex datasets.”
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  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingnone given

    The integration of VR technology in classrooms, guided by AI, is highlighted as a groundbreaking advancement.

    “The integration of VR technology in classrooms, guided by AI, is highlighted as a groundbreaking advancement.”
    Quote in the paperNot a substantial claim

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

    Findingcitation

    The results emphasize personalization of education through tailored content delivery, pacing adjustments, real-time feedback, and intelligent algorithms.

    “They emphasize the personalization of education through tailored content delivery, pacing adjustments, real-time feedback, and intelligent algorithms.”
    Quote in the paperSubstantial claim

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    Findingown data

    Issues of whether the teacher can be substituted by AI and what tutors should do to cope with new challenges are mentioned quite often in the most-cited publications.

    “The issues whether the teacher can be substituted by AI in the education process or what should tutors do to cope with the new challenges are also mentioned quite often.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Implementation of adaptive learning platforms might lead to higher pass rates and improved student retention compared to traditional teaching methods.

    “the implementation of adaptive learning platforms might lead to higher pass rates and improved student retention compared to traditional teaching methods”
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    • cites Contrino et al., 2024

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    • cites Demartini et al., 2024

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    Interpretationnone given

    Adaptive learning technologies and AI made a breakthrough in contemporary personalized education and contributed to the vision of sustainable education in the post-COVID era.

    “Adaptive learning technologies and AI made a breakthrough in contemporary personalized education and contributed to the vision of sustainable education in the post-COVID era.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Adaptive learning systems monitor students' progress in real time and adapt the pace of new material based on demonstrated mastery level, ensuring learners are neither overwhelmed nor bored.

    “The system monitors students' progress in real-time and adapts to the pace at which new material is introduced based on their demonstrated mastery level.This ensures that learners are neither overwhelmed nor bored by content that is too easy or too challenging for them”
    Quote in the paperSubstantial claim0 of 2 sources checked
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    • cites Wolff et al., 2023

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    Premisecitation

    Adaptive systems provide immediate feedback throughout the learning process, enhancing students' understanding and promoting metacognition and self-regulation.

    “Additionally, adaptive systems provide immediate feedback throughout the learning process.This timely feedback not only enhances students' understanding but also promotes metacognition and self-regulation skills as they actively reflect on their progress”
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    Premisecitation

    AI systems cannot replicate the social and emotional learning that occurs through human connections, which are vital for a well-rounded education.

    “AI systems, while efficient, cannot replicate the social and emotional learning that occurs through human connections, which are vital for a well-rounded education.”
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    • cites Markauskaite et al., 2022

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    Premisecitation

    Extensive data collection required for AI-driven educational tools raises serious privacy concerns, including risks of data breaches, unauthorized access, and misuse of sensitive student information.

    “In addition, the extensive data collection required for AI-driven educational tools raises serious privacy concerns.There is a risk of data breaches, unauthorized access, and misuse of sensitive student information.The literature emphasizes the need for robust data protection measures and clear policies to ensure that student privacy is safeguarded (Berendt et al., 2020).”
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    Premisenone given

    Implementing AI in diverse educational settings presents significant challenges; in resource-constrained environments, lack of infrastructure, training, and ongoing support can limit effective use, and high costs can be prohibitive.

    “Finally, it is apparent that implementing AI in diverse educational settings presents significant challenges.In resource-constrained environments, the lack of infrastructure, training, and ongoing support can significantly limit the effective use of AI.Additionally, the high costs associated with acquiring and maintaining AI technologies can be prohibitive for many educational institutions.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    AI can analyze large amounts of data quickly and create individualized learning paths for each student by collecting student information such as performance data, preferences, and learning patterns.

    “One significant advantage AI brings to education is its ability to analyze large amounts of data quickly.By collecting vast quantities of student information, such as performance data, preferences, and learning patterns, AI algorithms can create individualized learning paths for each student”
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    • cites Shemshack et al., 2021

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    Premisecitation

    Students who used an AI platform achieved higher pass rates compared to those who did not, illustrating the effectiveness of AI in enhancing academic performance.

    “As a result, students who used the platform achieved higher pass rates compared to those who did not, illustrating the effectiveness of AI in enhancing academic performance (Maxfielf, 2023).”
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    Premisecitation

    A personalized approach fosters greater engagement and motivation among students, leading to improved academic outcomes.

    “This personalized approach fosters greater engagement and motivation among students, leading to improved academic outcomes (Aulakh et al., 2023).”
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    Premisecitation

    Intelligent tutoring systems enhance student engagement and improve academic performance.

    “Intelligent tutoring systems (ITS) have emerged as powerful tools that not only enhance student engagement but also improve academic performance (Guo et al., 2021).”
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    Premisecitation

    AI bias in educational tools arises when algorithms produce skewed results that disadvantage certain student groups, often due to biased training data or flawed algorithm design.

    “Another critical issue is the potential for AI bias in educational tools which arises when algorithms produce skewed results that disadvantage certain student groups, often due to biased training data or flawed algorithm design (Ferrara, 2023).”
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    Premisecitation

    Adaptive learning platforms leverage AI to continuously monitor students' progress and adapt instructional content, ensuring targeted support where needed.

    “Adaptive learning platforms leverage this capability by continuously monitoring students' progress and adapting instructional content accordingly.This personalized approach ensures that students receive targeted support where they need it most”
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    • cites Muñoz et al., 2022

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    Premisecitation

    Over-reliance on AI technologies can lead to diminished critical thinking and human judgment, and a potential decrease in problem-solving skills fostered through traditional educational practices.

    “Furthermore, there is also the risk of over-reliance on AI technologies, where educators and students may depend too heavily on automated systems (Zhai et al., 2024).This reliance can lead to a diminished role for critical thinking and human judgment, as well as a potential decrease in the development of problem-solving skills that are fostered through traditional educational practices.”
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    Premisecitation

    Adaptive learning systems collect vast amounts of data on student performance, allowing educators to identify knowledge gaps and tailor interventions accordingly.

    “Moreover, adaptive learning systems collect vast amounts of data on student performance, allowing educators to identify knowledge gaps and tailor interventions accordingly (Currie et al., 2022).”
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    Premisecitation

    AI systems trained on biased datasets can perpetuate or exacerbate inequalities, potentially disadvantaging students from underrepresented groups by providing less accurate or biased feedback.

    “AI systems that are trained on biased datasets can inadvertently perpetuate or even exacerbate inequalities.For instance, some studies demonstrated that AI algorithms may disadvantage students from underrepresented groups by providing less accurate or biased feedback, thus impacting their learning outcomes (Baker & Hawn, 2022;Von Winckelmann, 2023).”
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    • cites Von Winckelmann, 2023

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    Premisecitation

    Adaptive assessment systems dynamically adjust difficulty and content based on student performance, offering benefits traditional standardized tests cannot match.

    “These innovative systems utilize AI algorithms to dynamically adjust the difficulty level and content of assessments based on a student's performance.This personalized approach to testing offers numerous benefits that traditional standardized tests simply cannot match (Maghsudi et al., 2021).”
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    Premisecitation

    Adaptive assessment systems offer a more accurate representation of a student's knowledge and skills compared to traditional tests, eliminating biases associated with one-size-fits-all exams.

    “Moreover, adaptive assessment systems offer a more accurate representation of a student's knowledge and skills compared to traditional tests.These systems adapt the difficulty level of questions based on the student's responses, ensuring that each question is appropriately challenging.By tailoring the assessment to an individual's abilities, these systems eliminate the potential biases associated”
    Quote in the paperSubstantial claim0 of 1 sources checked
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    Interpretationreasoning

    Adaptive learning holds immense potential to revolutionize sustainable education by catering to the individual needs of learners.

    “Adaptive learning holds immense potential to revolutionize sustainable education by catering to the individual needs of learners.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    A data-driven approach empowers teachers to provide targeted support while optimizing resource allocation for a more efficient educational system that aligns with SDGs.

    “This data-driven approach empowers teachers to provide targeted support while optimizing resource allocation for a more efficient educational system that aligns with SDGs (Romanova & Kuzmin, 2021).”
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    Premisecitation

    AI-driven systems provide instant feedback through automated grading systems or virtual tutors, helping students identify mistakes promptly and adjust while engaging with the topic.

    “However, with AIdriven systems, learners receive instant feedback on their work through automated grading systems or virtual tutors capable of guiding at any time.This immediate response helps students identify their mistakes promptly and make necessary adjustments while still engaging with the topic at hand (Celik et al., 2022).”
    Quote in the paperSubstantial claim0 of 1 sources checked
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    Premisecitation

    Adaptive learning technologies and AI can significantly contribute to the vision of sustainable education in the post-COVID era with benefits in fostering student engagement, enhancing educational outcomes, and promoting lifelong learning.

    “It appears that adaptive learning technologies and AI can significantly contribute to the vision of sustainable education in our post-COVID era with benefits in fostering student engagement, enhancing educational outcomes, and promoting lifelong learning for a more sustainable future (Yuan et al., 2021).”
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    • Rests on an assumption: Better educated individuals make informed decisions about their daily lives and living environments, which in turn leads to more sustainable practices and policies.
    • Rests on an assumption: Adaptive learning technologies and AI promote innovation and help solve pressing global issues such as sustainability, climate change, and resource management.
    • Rests on an assumption: Education drives economic growth, which is crucial for funding and implementing sustainable initiatives.

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    Premisecitation

    AI can be used for routine administrative tasks such as preparing study schedules, progress monitoring, grading, but cannot completely replace humans; human touch remains vital for creativity, critical thinking, and social-emotional development.

    “It can be used for routine administrative tasks such as preparing study schedules and plans, progress monitoring, or grading, and marking.However, it cannot completely replace humans in any of those processes.The human touch remains vital for fostering creativity, critical thinking skills, and socialemotional development-areas where machines currently lack proficiency (Heim et al., 2023).”
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    Premisenone given

    Adaptive systems use machine learning to assess students' learning patterns and study progress in real time and report them to tutors, who can adapt teaching strategies, intervene, or adjust lectures.

    “Adaptive systems use machine learning to assess the learning patterns and study progress of the students in real time and to report them to tutors who can, in turn, quickly adapt their teaching strategies, intervene when needed or adjust their lectures according to the students' progress and understanding of the material.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Adaptive learning facilitates the development of lifelong learners equipped with skills to thrive in an ever-changing world while promoting sustainability as integral to education.

    “The implementation of adaptive learning also facilitates the development of lifelong learners who are equipped with the necessary skills to thrive in an ever-changing world while promoting sustainability as an integral part of their education journey (Holman & Švejdarová, 2023).”
    Quote in the paperSubstantial claim0 of 1 sources checked
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    Premisecitation

    Schools implementing Carnegie Learning's AI-powered adaptive program reported improved test scores and greater student engagement.

    “Schools implementing Carnegie Learning's program reported improved test scores and greater student engagement (Wested., 2017).”
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    • cites Wested., 2017

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    Premisecitation

    AI-powered platforms provide tailored instruction, identify individual learning patterns, adjust curriculum, enhance student engagement, and improve overall learning outcomes.

    “By analyzing vast amounts of data, such systems can identify individual learning patterns and adjust their curriculum accordingly (Tapalova & Zhiyenbayeva, 2022).This personalized approach not only enhances student engagement but also improves overall learning outcomes.”
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    Premisecitation

    Adaptive learning has the potential to revolutionize education by personalizing instruction and tailoring content to individual learners.

    “With the ability to personalize instruction and tailor content to individual learners, adaptive learning has the potential to revolutionize education (Muca, Buonaiuto, et al., 2023).”
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    Study design and conductown data

    The study's scope is primarily rooted in bibliometric analysis based on Web of Science, and its findings are contingent on the available research literature up to the knowledge cutoff date.

    “metric analysis based on the data from WoS, and its findings are contingent on the available research literature up to the knowledge cutoff date.”
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    Premisecitation

    The collection of personal data from students raises ethical questions regarding how it will be used or shared with third parties.

    “The collection of personal data from students raises ethical questions regarding how it will be used or shared with third parties (Jones, 2019;Lutz & Newlands, 2021).”
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    • cites Lutz & Newlands, 2021

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    Premisecitation

    Student feedback can reveal how adaptive learning technologies influence engagement, motivation, and learning outcomes, while educators can provide insights into practical challenges of integrating adaptive tools into traditional classroom settings.

    “Student feedback can reveal how these technologies influence engagement, motivation, and learning outcomes, while educators can provide insights into the practical challenges of integrating adaptive tools into traditional classroom settings (Cavanagh et al., 2020).”
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    Premisecitation

    The ability of AI and adaptive learning to personalize the learning experience based on individual needs and abilities has garnered widespread recognition and adoption across various educational institutions.

    “The ability of AI and adaptive learning to personalize the learning experience based on individual needs and abilities has garnered widespread recognition and adoption across various educational institutions.”
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    Interpretationreasoning

    Adaptive learning represents a groundbreaking approach in personalized education that makes good use of technology and AI to optimize learning experiences for individual students.

    “Furthermore, it becomes clear that nowadays adaptive learning represents a groundbreaking approach in personalized education that make a good use of the power of technology and AI to optimize learning experiences for individual students.”
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    Premisecitation

    Adaptive systems employ sophisticated algorithms to analyze data and make informed decisions about what content a student should learn next or how it should be delivered.

    “With these learner profiles in hand, adaptive systems employ sophisticated algorithms to analyze the data and make informed decisions about what content a student should learn next or how it should be delivered(Wolff et al., 2023).”
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    • cites Wolff et al., 2023

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    Premisecitation

    Dynamic adaptability enhances learner engagement and promotes optimal knowledge retention.

    “As Kadaruddin (Kadaruddin, 2023) explains in relation to this finding, this dynamic adaptability enhances learner engagement and promotes optimal knowledge retention.”
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    Premisecitation

    Implementing adaptive learning technologies requires reliable internet connectivity, adequate hardware devices, software compatibility across platforms, and technical support.

    “Implementing adaptive learning technologies requires reliable internet connectivity, adequate hardware devices like computers or tablets, software compatibility across different platforms or operating systems, as well as technical support for troubleshooting issues promptly (Kesavan et al., 2022).”
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  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Active parental involvement facilitates better communication between home and school, allowing for a more coordinated approach to addressing individual learning needs.

    “Additionally, active parental involvement facilitates better communication between home and school, allowing for a more coordinated approach to addressing individual learning needs.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    Without ongoing maintenance and updates, adaptive learning technologies risk becoming outdated and less effective, which could diminish their value in educational settings.

    “Without ongoing maintenance and updates, adaptive learning technologies risk becoming outdated and less effective, which could diminish their value in educational settings.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    Our results reveal the transformative potential of AI-powered parameter prediction techniques in shaping the future of education.

    “Overall, our results reveal the transformative potential of AI-powered parameter prediction techniques in shaping the future of education.”
    Quote in the paperNot a substantial claim

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

    Interpretationnone given

    By leveraging data-driven insights and intelligent algorithms, adaptive systems offer tailored content delivery, pacing adjustments, and real-time feedback that enhance both students' engagement and achievements.

    “By leveraging data-driven insights and intelligent algorithms, adaptive systems offer tailored content delivery, pacing adjustments, and real-time feedback that enhance both students' engagements and achievements.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Some studies demonstrated that adaptive learning technologies improve immediate academic performance and have a lasting impact on students' ability to retain and apply knowledge.

    “Some studies demonstrated that these technologies not only improve immediate academic performance by providing personalized and adaptive learning experiences but also have a lasting impact on students' ability to retain and apply knowledge.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Yang et al., 2022

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    Premisecitation

    The potential dependency on technology that adaptive learning might create among students is an important consideration.

    “Additionally, one more important consideration to be considered is the potential dependency on technology that adaptive learning might create among students (Wang et al., 2023).”
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    Premisecitation

    Advanced techniques such as artificial neural networks and adaptive neuro fuzzy inference demonstrate efficiency in refining parameter estimation and forecasting, presenting opportunities for educational applications.

    “According to some authors, these advanced techniques demonstrate their efficiency in refining parameter estimation and forecasting, presenting promising opportunities for educational applications (Rahman et al., 2021).”
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  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Parental involvement is crucial in enhancing the effectiveness of adaptive learning technologies.

    “Additionally, parental involvement also appears to be crucial in enhancing the effectiveness of adaptive learning technologies.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The year 2024 was deliberately excluded from the bibliometric sample to avoid bias because the publication count for that year is still ongoing.

    “We have deliberately excluded the year of 2024 in order not to bias the growing dynamics because the publication count is still ongoing.”
    Quote in the paperSubstantial claim

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    Premisecitation

    Students who engage with adaptive learning technologies often exhibit higher levels of engagement and motivation, which contributes to better educational outcomes.

    “Over time, students who engage with adaptive learning technologies often exhibit higher levels of engagement and motivation, which contributes to better educational outcomes (Yang et al., 2022).”
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    Premisecitation

    Predictive modeling uses AI to anticipate future values or conditions based on historical data and relevant parameters.

    “Central to these themes is the concept of predictive modeling, where the AI is utilized to anticipate future values or conditions based on historical data and relevant parameters (Yun et al., 2022).”
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    Study design and conductnone given

    The study predominantly focuses on broad implications of AI and adaptive learning without focusing deeply on specific cultural, contextual, or socioeconomic nuances that could impact adoption and effectiveness.

    “The study predominantly focuses on the broad implications of AI and adaptive learning without focusing deeply on the specific cultural, contextual, or socioeconomic nuances that could impact the adoption and effectiveness of these technologies.”
    Quote in the paperNot a substantial claim

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

    Interpretationcitation

    As adaptive learning systems mature alongside AI technology, there is tremendous potential to transform education.

    “Our results emphasize that as adaptive learning systems mature alongside AI technology, there is a tremendous potential to transform education, which is in accord with the outcomes of the relevant studies in this field (Alenezi, 2021;Troussas et al., 2022;Valdés & Cerdá Suárez, 2021).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Alenezi, 2021

      Not checked in the free run.

    • cites Troussas et al., 2022

      Not checked in the free run.

    • cites Valdés & Cerdá Suárez, 2021

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Premisecitation

    Robust evaluation methodologies are important to ensure the reliability and applicability of predictive models within educational settings.

    “This underlines the importance of robust evaluation methodologies to ensure the reliability and applicability of predictive models within educational settings (Zhong et al., 2021).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Zhong et al., 2021

      Not checked in the free run.

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

    Interpretationreasoning

    When parents actively engage in their child's learning process, they can reinforce the personalized strategies provided by adaptive learning technologies.

    “When parents actively engage in their child's learning process, they can reinforce the personalized strategies provided by these technologies.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    With further enhancement of adaptive learning, the integration of predictive models emerges as a potent tool for optimizing and tailoring educational experiences for learners around the world.

    “With the further enhancement of the adaptive learning, the integration of predictive models emerges as a potent tool for tackling the power of AI to optimize and tailor educational experiences for learners around the world.”
    Quote in the paperNot a substantial claim

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

    Interpretationreasoning

    The incorporation of indicators allows researchers and practitioners to assess the performance of predictive models and gauge their efficacy in diverse adaptive learning contexts.

    “The incorporation of indicators allows researchers and practitioners to assess the performance of predictive models and gauge their efficacy in diverse adaptive learning contexts.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    The findings may not fully capture the global scope of research on adaptive learning technologies, and there could be an overrepresentation of certain types of studies or research from specific regions.

    “As a result, the findings may not fully capture the global scope of research on adaptive learning technologies, and there could be an overrepresentation of certain types of studies or research from specific regions.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Bibliometric databases predominantly index journals and publications in English and from Western countries, potentially excluding significant research published in other languages or regions.
    • Rests on an assumption: Bibliometric data is often limited to indexed journals and may not capture relevant work published in non-indexed sources, conference proceedings, or emerging online platforms.
    • Rests on an assumption: Bibliometric analysis primarily focuses on quantitative measures such as citation counts, which may not fully reflect the quality, impact, or practical relevance of research.
    • Rests on an assumption: Bibliometric analysis can overlook the context and content of studies, leading to an incomplete understanding of how adaptive learning technologies are implemented and evaluated in diverse educational settings.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    Combined with AI-driven adaptive learning systems that adjust content based on individual student reactions or performance metrics gathered within VR simulations, students will have unprecedented opportunities for experiential learning.

    “Combined with AI-driven adaptive learning systems that adjust content based on individual student reactions or performance metrics gathered within VR simulations-students will have unprecedented opportunities for experiential learning.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationnone given

    The symbiotic integration of AI, ML, and neural networks fostered the evolution of predictive modeling, anomaly detection, and interactive learning interfaces within adaptive learning and e-learning environments.

    “Additionally, the symbiotic integration of AI, ML, and neural networks fostered the evolution of predictive modeling, anomaly detection, and interactive learning interfaces within adaptive learning and e-learning environments.”
    Quote in the paperSubstantial claim

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

    Interpretationnone given

    With machine learning algorithms analyzing vast amounts of data collected from various sources, educators can gain deeper insights into students' progress and adapt teaching strategies accordingly.

    “However, with machine learning algorithms analyzing vast amounts of data collected from various sources like quizzes, assignments, discussions, or even facial expressions during online classes, educators can gain deeper insights into students' progress and adapt teaching strategies accordingly.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Our analysis underscores that these technologies not only personalize learning but also align with the broader goals of sustainable education, particularly in the post-COVID era.

    “Our analysis underscores that these technologies not only personalize learning but also align with the broader goals of sustainable education, particularly in the post-COVID era (Deroncele-Acosta et al., 2023; Whalley et al., 2021).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Deroncele-Acosta et al., 2023

      Not checked in the free run.

    • cites Whalley et al., 2021

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    The alignment of adaptive learning technologies with sustainable education is evidenced by tailored content delivery and real-time feedback mechanisms, which are instrumental in creating a more dynamic and responsive educational environment.

    “This alignment is evidenced by the tailored content delivery and real-time feedback mechanisms, which are instrumental in creating a more dynamic and responsive educational environment (Liu & Yu, 2023).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Liu & Yu, 2023

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

    Interpretationnone given

    The ascent of adaptive learning and AI technologies in education offers a paradigm shift, necessitating careful policy considerations.

    “When it comes to the practical implications of our research, it is quite apparent that the ascent of adaptive learning and AI technologies in education offers a paradigm shift, necessitating careful policy considerations.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: AI systems often require access to large amounts of personal data to function effectively.
    • Rests on an assumption: Algorithmic bias can inadvertently perpetuate or exacerbate existing inequalities if AI systems are trained on biased data.
    • Rests on an assumption: Increasing reliance on AI in education could reduce the role of human educators, potentially impacting the development of social and emotional skills in students.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    AI-powered tools hold immense potential to revolutionize education in the coming years through their ability to personalize learning experiences, offer real-time feedback, and provide targeted support.

    “With their ability to personalize learning experiences, offer real-time feedback, and provide targeted support, AI-powered tools hold immense potential to revolutionize education in the coming years.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    AI can greatly enhance assessment methods by providing more accurate and comprehensive evaluations.

    “Furthermore, AI can greatly enhance assessment methods by providing more accurate and comprehensive evaluations.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Traditional exams often fall short in measuring a student's true understanding and mastery of a subject.
67 of 67

  • The paper relies on this without support.

    Better educated individuals make informed decisions about their daily lives and living environments, which in turn leads to more sustainable practices and policies.

    “Better educated individuals make informed decisions about their daily lives and living environments, which in turn leads to more sustainable practices and policies.”

  • The paper relies on this without support.

    Adaptive learning technologies and AI promote innovation and help solve pressing global issues such as sustainability, climate change, and resource management.

    “In addition, they promote innovation and solving most pressing global issues such as sustainability, climate change, and resource management.”

  • The paper relies on this without support.

    Education drives economic growth, which is crucial for funding and implementing sustainable initiatives.

    “Finally, they help to shape up education that drives economic growth, which is crucial for funding and implementing sustainable initiatives.”

  • The paper relies on this without support.

    Adaptive learning allows personalized instruction, enabling students to learn at their own pace and style.

    “Firstly, it allows for personalized instruction, enabling students to learn at their own pace and style.”

  • The paper relies on this without support.

    Adaptive learning promotes self-directed learning as students take ownership of their education by setting goals and monitoring progress.

    “Additionally, this approach promotes self-directed learning as students take ownership of their education by setting goals and monitoring progress.”

  • The paper relies on this without support.

    Adaptive learning empowers educators to tailor educational experiences according to individual needs, preferences, and learning styles.

    “By integrating technology, data analytics, and personalized instruction, adaptive learning empowers educators to tailor educational experiences according to individual needs, preferences, and learning styles.”

  • The paper relies on this without support.

    Adaptive learning platforms continuously assess students' progress and adapt instructional content accordingly.

    “Through the utilization of intelligent algorithms, adaptive learning platforms continuously assess students' progress and adapt instructional content accordingly.”

  • The paper relies on this without support.

    Adaptive learning contributes to a more sustainable educational environment by leveraging digital resources and reducing bureaucracy and paperwork.

    “Additionally, by leveraging digital resources and reducing bureaucracy and paperwork, adaptive learning contributes to a more sustainable educational environment.”

  • The paper relies on this without support.

    The high cost of implementing adaptive learning systems can hinder widespread adoption.

    “Additionally, the high cost of implementing adaptive learning systems can hinder widespread adoption.”

  • The paper relies on this without support.

    Adaptive learning can promote sustainability by reducing paper waste through digital resources and fostering eco-friendly practices and integration for students regardless of physical location or background.

    “Thence, adaptive learning can promote sustainability by reducing paper waste through digital resources and fostering eco-friendly practices as well as integration for students regardless of their physical location or background.”

  • The paper relies on this without support.

    In many regions, especially developing countries, limited access to reliable internet connectivity, digital devices, and technical support poses significant barriers to effective implementation of adaptive learning technologies.

    “In many regions, especially in developing countries, limited access to reliable internet connectivity, digital devices, and technical support poses significant barriers to the effective implementation of these technologies.”

  • The paper relies on this without support.

    Students from disadvantaged backgrounds, rural areas, or lower-income families may lack devices, internet connectivity, or digital literacy skills needed to engage with adaptive learning tools.

    “Students from disadvantaged backgrounds, rural areas, or lower-income families may lack the devices, internet connectivity, or digital literacy skills needed to effectively engage with adaptive learning tools.”

  • The paper relies on this without support.

    Educators may resist adaptive learning technologies due to lack of familiarity, fear of obsolescence, or concerns about time and effort required for training.

    “Educators may resist these technologies due to a lack of familiarity, fear of obsolescence, or concerns about the time and effort required for training.”

  • The paper relies on this without support.

    Institutional concerns about financial investment, potential disruption to established processes, and uncertainty about long-term benefits can hinder adoption of adaptive learning technologies.

    “From an institutional perspective, concerns about the financial investment, potential disruption to established processes, and uncertainty about long-term benefits can hinder the adoption of these technologies.”

  • The paper relies on this without support.

    Cultural attitudes towards technology, education, and authority can shape how students, educators, and institutions perceive and interact with adaptive learning tools.

    “Cultural attitudes towards technology, education, and authority can shape how students, educators, and institutions perceive and interact with these tools.”

  • The paper relies on this without support.

    In cultures emphasizing teacher-centered instruction, there may be resistance to more autonomous, student-centered learning models promoted by adaptive technologies.

    “For instance, in cultures that emphasize teacher-centered instruction, there may be resistance to the more autonomous, student-centered learning models promoted by adaptive technologies.”

  • The paper relies on this without support.

    Cultural variations in communication styles, learning preferences, and role of feedback can affect how effectively students engage with adaptive learning technologies.

    “Furthermore, cultural variations in communication styles, learning preferences, and the role of feedback can affect how effectively students engage with these technologies.”

  • The paper relies on this without support.

    Resistance to change is one of the foremost challenges in implementing adaptive learning.

    “One of the foremost challenges is resistance to change.”

  • The paper relies on this without support.

    Educational systems have been structured around traditional teaching methods for centuries, and many educators may be hesitant to embrace new technologies.

    “Educational systems have been structured around traditional teaching methods for centuries, and many educators may be hesitant to embrace new technologies.”

  • The paper relies on this without support.

    Creating tailored digital content demands considerable resources, expertise, and time.

    “However, creating such content demands considerable resources, expertise, and time.”

  • The paper relies on this without support.

    The initial investment required for software licenses, hardware upgrades, and infrastructure installation can be substantial.

    “The initial investment required for software licenses, hardware upgrades, and the installation of necessary infrastructure can be substantial.”

  • The paper relies on this without support.

    Training and professional development for educators represent an essential but often overlooked cost.

    “Moreover, training and professional development for educators represent an essential but often overlooked cost, as teachers must be adequately prepared to use adaptive learning tools effectively.”

  • The paper relies on this without support.

    Data privacy concerns pose a significant challenge in implementing adaptive learning technologies.

    “Data privacy concerns also pose a significant challenge in implementing adaptive learning technologies.”

  • The paper relies on this without support.

    Without adequate training, the full potential of adaptive learning technologies may not be realized, limiting their impact on educational outcomes.

    “Without adequate training, the full potential of these technologies may not be realized, limiting their impact on educational outcomes.”

  • The paper relies on this without support.

    Implementing adaptive learning technologies can be costly, requiring significant investments in infrastructure, training, and ongoing maintenance.

    “Implementing adaptive learning technologies can be costly, requiring significant investments in infrastructure, training, and ongoing maintenance.”

  • The paper relies on this without support.

    Data privacy and security are major concerns, as sensitive student information must be protected from breaches.

    “Data privacy and security are major concerns, as sensitive student information must be protected from breaches.”

  • The paper relies on this without support.

    The effectiveness of adaptive learning technologies heavily depends on the quality of data and algorithms used, which can vary widely.

    “The effectiveness of these technologies heavily depends on the quality of data and algorithms used, which can vary widely.”

  • The paper relies on this without support.

    Adaptive learning technologies help develop critical thinking and problem-solving skills by offering tailored challenges and feedback, preparing students for future educational and career success.

    “Furthermore, adaptive learning technologies help develop critical thinking and problem-solving skills by offering tailored challenges and feedback, preparing students for future educational and career success.”

  • The paper relies on this without support.

    AI systems often require access to large amounts of personal data to function effectively.

    “AI systems often require access to large amounts of personal data to function effectively, which raises questions about how this data is collected, stored, and used.”

  • The paper relies on this without support.

    Algorithmic bias can inadvertently perpetuate or exacerbate existing inequalities if AI systems are trained on biased data.

    “Another ethical issue is algorithmic bias, where AI systems can inadvertently perpetuate or exacerbate existing inequalities if they are trained on biased data.”

  • The paper relies on this without support.

    Increasing reliance on AI in education could reduce the role of human educators, potentially impacting the development of social and emotional skills in students.

    “Additionally, the increasing reliance on AI in education could reduce the role of human educators, potentially impacting the development of social and emotional skills in students.”

  • The paper relies on this without support.

    Bibliometric databases predominantly index journals and publications in English and from Western countries, potentially excluding significant research published in other languages or regions.

    “These databases predominantly index journals and publications that are in English and from Western countries, potentially excluding significant research published in other languages or regions.”

  • The paper relies on this without support.

    Bibliometric data is often limited to indexed journals and may not capture relevant work published in non-indexed sources, conference proceedings, or emerging online platforms.

    “Bibliometric data is often limited to indexed journals and may not capture relevant work published in non-indexed sources, conference proceedings, or emerging online platforms.”

  • The paper relies on this without support.

    Bibliometric analysis primarily focuses on quantitative measures such as citation counts, which may not fully reflect the quality, impact, or practical relevance of research.

    “Additionally, bibliometric analysis primarily focuses on quantitative measures such as citation counts, which may not fully reflect the quality, impact, or practical relevance of the research.”

  • The paper relies on this without support.

    Bibliometric analysis can overlook the context and content of studies, leading to an incomplete understanding of how adaptive learning technologies are implemented and evaluated in diverse educational settings.

    “Furthermore, this method can overlook the context and content of the studies, leading to an incomplete understanding of how adaptive learning technologies are implemented and evaluated in diverse educational settings.”

  • The paper relies on this without support.

    Traditional exams often fall short in measuring a student's true understanding and mastery of a subject.

    “Traditional exams often fall short in measuring a student's true understanding and mastery of a subject.”
36 of 36

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?

  • AI has already shown potential to support learning by providing personalised feedback, scaffolding writing processes, and automating routine tasks.

    “In educational settings, AI has already shown potential to support learning by providing personalised feedback, scaffolding writing processes, and automating routine tasks (Kasneci et al., 2023; Strielkowski et al., 2025).”

    From Participatory Co-Design and Evaluation of a Novel Approach to Generative AI-Integrated Coursework Assessment in Higher Education (2025) · cites this paper as Strielkowski et al., 2025

    Section 2.2 (page unavailable) says AI systems give learners immediate feedback and can handle routine tasks such as scheduling and grading. Section 2.6 describes support tailored to individual students. The passages do not establish the claim that AI scaffolds writing processes. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • 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 [11]

  • Previous studies by Strielkowski et al. and Xu emphasize the transformative potential of AI in improving academic outcomes through adaptive learning environments.

    “This aligns with previous studies, such as those by Strielkowski et al. [11] and Xu [57], which emphasize the transformative potential of AI in improving academic outcomes through adaptive learning environments.”

    From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [11]

  • Researchers such as Strielkowski et al. caution against overly technocentric approaches that neglect pedagogical and emotional considerations.

    “These challenges have been highlighted by researchers such as Strielkowski et al. [11], who caution against overly technocentric approaches that neglect pedagogical and emotional considerations.”

    From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [11]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Bibliometric study period ends in both 2023 and 2024

    Minor

    The methods describe the bibliometric analysis as covering 1990-2023, while the abstract says the selected publications cover 1990-2024. The stated study periods disagree.

  • Figure 7 institution date range disagrees with the text

    Minor

    The text gives 2000-2024 for the most prolific institutions, but the Figure 7 caption gives 1990-2024.

2 of 2

Cites

1 retracted

Flagged first, then by citations

2 of the 155 references couldn’t be matched to a known paper.

Research works printed but not linked: 14

  1. 10.1007/s10639-022-11323-xe · DOI
  2. Chen X., 2020, Two decades of artificial intelligence in education, Educational Technology & Society, 25, 28
  3. Currie C., 2022, A toolbox of adaptations for online delivery of SEL programming, Journal of Online Learning Research, 8, 7
  4. 10.1108/aaouj-09-2020-007 · DOI
  5. Li F., 2021, Progress, challenges and countermeasures of adaptive learning, Educational Technology & Society, 24, 238
  6. Matthew U. O., 2021, Contemporary development in E‐learning education, cloud computing technology & internet of things, EAI Endorsed Transactions on Cloud Systems, 7, e3
  7. Maxfielf C., 2023, Arizona State University to use adaptive learning technology, Eetimes
  8. Muñoz J. L. R., 2022, Systematic review of adaptive learning technology for learning in higher education, Eurasian Journal of Educational Research, 98, 221
  9. Smith J., 2022, Human‐computer interaction: Enhancing user experience and productivity, International Multidisciplinary Journal of Science, Technology, and Business, 1, 1
  10. Tzenios N., 2020, Examining the impact of EdTech integration on academic performance using random Forest regression, ResearchBerg Review of Science and Technology, 3, 94
  11. WestEd. (2017).Carnegie Math Pathways Graduates to Larger Home in WestEd.https://www.wested.org/wested-bulletin/news/carnegie-math-pathways-graduates-to-larger-home-in-wested
  12. Wongvorachan T., 2022, Artificial intelligence: Transforming the future of feedback in education, Journal of Applied Testing Technology, 23, 95
  13. Yun C., 2022, Predictive analytics: A survey, trends, applications, Opportunities' and challenges for Smart City planning, International Journal of Computer Science and Information Technology, 23, 226
  14. 10.1155/2021/8812542 · DOI · Paper page

Cited by

Sorted by citations

Related

Authors
Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T.
Affiliations
  • Department Agricultural and Resource Economics University of California Berkeley California USA
  • Kuban State University (RU)
  • Financial University (RU)
  • Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (UZ)
  • Moscow State Pedagogical University (RU)
ORCID
Venue
Sustainable Development
Publisher
Wiley
Published
2024-10-03
Volume
33
Issue
2
Pages
1921-1947
ISSN
  • 0968-0802 (ISSN-L)
  • 1099-1719
DOI
10.1002/sd.3221
OpenAlex
W4403102965
Semantic Scholar
1c693b461b73c567a4b290fe707c3b7f66025cde
Cited by
581
References
155
Citations by year
2026: 333 · 2025: 240 · 2024: 8
FWCI
217.91
Type
journal article
Language
en
Open access
Licence
Locations
Sustainable Development (publishedVersion, cc-by-nc-nd)
Funders
Kuban Science Foundation (MFI‐20.1/80)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
adaptive learning, artificial intelligence in education, personalized learning, educational technology, digitalization of education, COVID-19 pandemic, bibliometric analysis, research trends

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

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