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Cited by 235Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningOnline Learning and Analytics

A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda

Open access
Mustafa, Muhammad Yasir, Tlili, Ahmed, Lampropoulos, Georgios, Huang, Ronghuai, Jandrić, Petar, Zhao, Jialu, Salha, Soheil, Xu, Lin +4 more·2024·Smart Learning Environments
DOI 10.1186/s40561-024-00350-5
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Abstract

Despite the increased adoption of Artificial Intelligence in Education (AIED), several concerns are still associated with it. This has motivated researchers to conduct (systematic) reviews aiming at synthesizing the AIED findings in the literature. However, these AIED reviews are diversified in terms of focus, stakeholders, educational level and region, and so on. This has made the understanding of the overall landscape of AIED challenging. To address this research gap, this study proceeds one step forward by systematically meta-synthesizing the AIED literature reviews. Specifically, 143 literature reviews were included and analyzed according to the technology-based learning model. It is worth noting that most of the AIED research has been from China and the U.S. Additionally, when discussing AIED, strong focus was on higher education, where less attention is paid to special education. The results also reveal that AI is used mostly to support teachers and students in education with less focus on other educational stakeholders (e.g. school leaders or administrators). The study provides a possible roadmap for future research agenda on AIED, facilitating the implementation of effective and safe AIED.

AI snapshot

Written from the paper’s full text.

TL;DR

This systematic meta-review synthesizes 143 literature reviews on artificial intelligence in education (AIED) using PRISMA and the technology-enhanced learning model. It maps AIED trends, stakeholders, educational levels, subject areas, review methods, objectives, technologies, advantages, and challenges, and proposes a future research agenda.

Key findings

  • Most AIED reviews focused on China (28 studies), with the U.S. and Spain following; students were the most studied stakeholder (n=99, 53.51%), then teachers (n=73, 39.45%), while administrators (n=10, 5.45%) and researchers (n=3, 1.62%) were least studied. Higher education accounted for 31% of studies, K-12 16%, special education 2%, and early childhood education 2%.
  • Subject areas: 56% of reviews did not specify a discipline; science was 16%, health/welfare 12%, arts/humanities 7%, social science/journalism/information 4%, business/administration/law 2%, and physical education 1%. Systematic reviews were 68% of included reviews, meta-analyses 13%, and bibliometric reviews 6%.
  • AIED objectives: teaching and learning support was highest (n=95, 56.5%), followed by teaching and learning assessment (n=24, 14.3%), adaptive teaching and learning (n=21, 12.5%), student prediction (n=13, 7.7%), teacher/student profiling/modelling (n=8, 4.8%), and monitoring students (n=7, 4.2%).
  • Technologies: machine learning was most used (n=50, 23.80%), then LLMs/generative AI (n=41, 19.50%), intelligent tutoring systems (n=27, 12.85%), chatbots (n=20, 9.50%), deep learning (n=10, 4.76%), and AR/VR (n=10, 4.76%). Advantages most frequently mentioned in text were personalized learning (n=31, 16.66%) and improved teaching/learning quality (n=30, 16.12%); Table 5 lists assessment support as n=38, 21.50%. Challenges were grouped as ethical/safety (n=49), AI technologies (n=34), design (n=28), teachers (n=21), and students (n=10); data privacy was the top specific challenge (n=29, 20.42%).

Methods

  • Design: PRISMA-guided systematic review of literature reviews (meta-review), analyzed according to the technology-enhanced learning model (Hsu et al., 2012) with adjustments for AIED.
  • Data: searched Web of Science, Scopus, Taylor and Francis, IEEE Xplore, and ScienceDirect; journal papers only; search string combined AI, education, and review terms. From 1652 records, after duplicate removal 1565, title/abstract screening removed 1109, 456 full texts were assessed, 313 were excluded, leaving 143 eligible reviews.
  • Analysis: coding scheme based on the technology-enhanced learning model; two authors independently screened titles/abstracts and full texts; weekly meetings resolved disagreements by discussion.

Limitations

  • Paper states findings are limited by the searched keywords and included databases; results are drawn from covered journal papers, and relevant conferences were not covered.
  • Evident from inclusion criteria: only English-language journal articles were included; conference proceedings, book series, book chapters, books, dissertations, and non-English articles were excluded.
  • Evident: as a meta-review, findings depend on the scope and quality of the 143 included literature reviews; the paper also notes lack of research from regions such as Africa and the Arab region.

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

Integrity signals

Registry

Crossref · 10.1186/s40561-024-00350-5

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 28 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Most reviewed AIED studies focused on students (n = 99, 53.51%).

    “It is evident that most of the studies (n = 99, 53.51%) focused on students.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Bibliometric reviews accounted for 6% of included AIED reviews.

    “Figure 9 shows that among all literature reviews, systematic reviews were the most common type of reviews, accounting for 68% of the included articles, followed by meta-analyses at 13%, and bibliometric reviews at 6%.”
    Quote in the paperNot a substantial claim
  • Findingown data

    Meta-analyses accounted for 13% of included AIED reviews.

    “Figure 9 shows that among all literature reviews, systematic reviews were the most common type of reviews, accounting for 68% of the included articles, followed by meta-analyses at 13%, and bibliometric reviews at 6%.”
    Quote in the paperNot a substantial claim
  • Findingreasoning

    The most neglected stakeholders in AIED reviews are special education students and teachers.

    “The most neglected stakeholders are found to be special education students and teachers.”
    Quote in the paperSubstantial claim
  • Findingown data

    IEEE Access published 10 AIED articles.

    “The IEEE Access journal follows with 10 articles published.”
    Quote in the paperNot a substantial claim
  • May combine two claims. 21
  • Classified as probably not a substantial claim. 34

  • May combine two claims.

    Findingown data

    The systematic review included 143 literature reviews on AIED and analyzed them according to the technology-based learning model.

    “Specifically, 143 literature reviews were included and analyzed according to the technology-based learning model.”
    • Also stated in: § Search strategy · plain
      “Thus, as a total number, 143 eligible research studies remained for inclusion in the systematic review.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    China had the highest number of AIED literature reviews (28 studies), followed by the United States and Spain.

    “The figure reveals China's leadership with 28 studies, illustrating its extensive engagement with AI to redefine educational paradigms across various learning stages from early childhood to higher educational tiers.The United States and Spain follow, each demonstrating distinctive approaches to incorporating AI into their educational frameworks.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Social science, journalism and information (4%), business, administration, and law (2%), and Physical Education (1%) were less focused areas in reviewed AIED studies.

    “Finally, as shown in Fig. 6, the reviewed studies were less focused on social science, journalism and information (4%), business, administration, and law (2%) and Physical Education (1%).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    When discussing AIED, strong focus was on higher education, where less attention is paid to special education.

    “Additionally, when discussing AIED, strong focus was on higher education, where less attention is paid to special education.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Challenges were observed in the design and development of AI systems (n = 14, 9.85%) and their application in teaching and training settings (n = 14, 9.85%).

    “Furthermore, challenges were also observed in the design and development of AI systems (n = 14, 9.85%) as well as their application in teaching and training settings (n = 14, 9.85%).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

  • May combine two claims.

    Findingnone given

    Most studies are explorative, short-term and in limited domains e.g., language and academic writing.

    “As we currently stand, most studies are explorative, short-term and in limited domains e.g., language and academic writing.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

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

    Findingnone given

    There is a faint presence of research on alignment in education.

    “Equally important is the faint presence of research on alignment in education.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Findingreasoning

    There is a surge of interest in AIED across all stakeholders, particularly during 2023 and 2024.

    “First and foremost, there is a surge of interest in AIED across all stakeholders and in particular during the last two years, 2023 and 2024.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

  • May combine two claims.

    Findingown data

    AI can increase students' critical thinking and creativity (n = 7, 3.76%).

    “increase their critical thinking and creativity (n = 7, 3.76%)”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

  • May combine two claims.

    Findingown data

    A prime focus was placed on how AI can support teaching and learning, how it can be integrated into assessment and monitoring tasks, and how it can be used for prediction, profiling, and modeling to offer adaptive and personalized teaching and learning.

    “A prime focus was placed on how AI can support teaching and learning, how it can be integrated into assessment and monitoring tasks, and how it can be used for prediction, profiling, and modeling to offer adaptive and personalized teaching and learning (see Fig. 10 for a summary).”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

  • Classified as probably not a substantial claim.

    Findingnone given

    There is little discussion about AI literacy, particularly critical AI literacy.

    “Within the current conversation about AI, there is little discussion about AI literacy and in particular, critical AI literacy.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

  • May combine two claims.

    Findingown data

    A prominent gap is a lack of focus on childhood, primary and secondary education, as well as on teacher training and special education.

    “A prominent gap that we observed is a lack of focus on childhood, primary and secondary education, as well as on teacher training and special education.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

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

    Findingnone given

    Integration of AI in education is still in its early stages.

    “Integration of AI in education is still in its early stages and therefore, there is a need for more studies that explore the adoption and use of AI in education to be conducted.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

  • May combine two claims.

    Findingown data

    The search process yielded 1652 articles, and after removing duplicates and screening, 143 eligible research studies remained for inclusion.

    “The search process yielded a total of 1652 articles.After removing duplicates, 1565 papers remained.Then, 1109 papers were removed based on the screening of titles and abstracts.The remaining 456 papers were considered and assessed for full texts.313 of these papers did not meet the inclusion criteria.Thus, as a total number, 143 eligible research studies remained for inclusion in the systematic”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Challenges in AIED were grouped into ethical and safety (n = 49), AI technologies (n = 34), design (n = 28), teachers (n = 21), and students (n = 10).

    “The challenges were grouped into several categories, namely ethical and safety (n = 49), AI technologies (n = 34), design (n = 28), teachers (n = 21), and students (n = 10).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

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

    Findingown data

    In the reviewed literature reviews on AI in education, the AI technique 'Fuzzy logic' had a frequency of 3 (1.42%).

    “| Fuzzy logic | 3 | 1.42% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the advantage 'Emotional support' had a frequency of 2 (1.07%).

    “| Emotional support | 2 | 1.07% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the AI technique 'Chabot' had a frequency of 20 (9.50%).

    “| Chabot | 20 | 9.50% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the advantage 'Improve creativity and critical thinking' had a frequency of 7 (3.76%).

    “| Improve creativity and critical thinking | 7 | 3.76% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the advantage 'Enhance learning outcomes' had a frequency of 15 (8.06%).

    “| Enhance learning outcomes | 15 | 8.06% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the advantage 'Improve content accessibility' had a frequency of 6 (3.22%).

    “| Improve content accessibility | 6 | 3.22% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, under the challenge category 'Students', 'AI digital literacy' had N=7 (4.92%).

    “| Students | AI digital literacy | 7 | 4.92% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the advantage 'Promote AI literacy' had a frequency of 5 (2.68%).

    “| Promote AI literacy | 5 | 2.68% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the AI technique 'Support vector machines' had a frequency of 3 (1.42%).

    “| Support vector machines | 3 | 1.42% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the advantage 'Decrease work stress' had a frequency of 4 (2.15%).

    “| Decrease work stress | 4 | 2.15% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the advantage 'Improve exam monitoring' had a frequency of 1 (0.53%).

    “| Improve exam monitoring | 1 | 0.53% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the AI technique 'Decision support system' had a frequency of 7 (3.30%).

    “| Decision support system | 7 | 3.30% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the AI technique 'Exam monitoring systems' had a frequency of 4 (1.90%).

    “| Exam monitoring systems | 4 | 1.90% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the AI technique 'Natural language processing (NLP)' had a frequency of 7 (3.30%).

    “| Natural language processing (NLP) | 7 | 3.30% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the objective 'Monitoring students' had a frequency of 7 (4.2%).

    “| Monitoring students | 7 | 4.2% |”
    Quote in the paperNot a substantial claim

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

    Findingown data

    In the reviewed literature reviews on AI in education, the AI technique 'Knowledge Tracing' had a frequency of 1 (0.47%).

    “| Knowledge Tracing | 1 | 0.47% |”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    AI comes with a huge risk of bias, and efforts to address it are lagging and largely ineffective.

    “AI comes with a huge risk of bias, although some effort is being done to address it, it is so far lagging and largely ineffective.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

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

    Study design and conductnone given

    The findings of this study are limited by the searched keywords and included databases.

    “First, the findings of this study are limited by the searched keywords and included databases.”
    Quote in the paperNot a substantial claim

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

    Interpretationnone given

    Current plagiarism detection methods have been vastly unreliable.

    “However, current plagiarism detection methods have been vastly unreliable which calls for new methods of assessment.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

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

    Study design and conductown data

    Two authors independently screened the retrieved papers' titles and abstracts.

    “After searching the relevant databases, two of the authors independently screened the retrieved papers' titles and abstracts, and excluded those that were clearly not literature reviews, such as empirical, descriptive and conceptual papers.”
    Quote in the paperNot a substantial claim

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

    Study design and conductcitation

    This study follows PRISMA guidelines and strictly followed the PRISMA checklist to ensure quality assurance and replicability.

    “This study follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, that provide a standard peer-accepted methodology that uses a guideline checklist, which was strictly followed for this study, to contribute to the quality assurance of the revision process and to ensure its replicability (Page et al., 2021).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Page et al., 2021 source

      Not checked in the free run.

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

    Study design and conductnone given

    Weekly meetings were organized between the two coders during the coding process, and disagreements were resolved by discussion.

    “Weekly meetings were organized between the two coders during the coding process.During this phase, disagreements between the coders were resolved by discussion.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    The meta-review by Bond et al. (2024) limited its scope to higher education and its temporal range to the last 5 years.

    “On the other hand, the meta-review by Bond et al. (2024) limited its scope to higher education and its temporal range to the last 5 years.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Bond et al., 2024 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationcitation

    The effectiveness of AIED on learning achievement remains questionable.

    “Despite the various technologies and techniques in AIED, the effectiveness of AIED on learning achievement remains questionable (Rodway & Schepman, 2023).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Rodway & Schepman, 2023 source

      Not checked in the free run.

    • Rests on an assumption: The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.
    • Rests on an assumption: The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

  • May combine two claims.

    Study design and conductcitation

    To identify high-quality reviews and ensure reliable results, this study covered journal papers only.

    “To identify high-quality reviews and ensure reliable results, this study covered journal papers only (Tlili et al., 2024;Zawacki-Richter et al., 2019;Zheng et al., 2023).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Tlili et al., 2024

      Not checked in the free run.

    • cites Zawacki-Richter et al., 2019 source

      Not checked in the free run.

    • cites Zheng et al., 2023 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    The obtained results are drawn based on the covered journal papers.

    “Additionally, the obtained results are drawn based on the covered journal papers.”
    Quote in the paperSubstantial claim

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

    Study design and conductown data

    Two authors independently performed an eligibility assessment by screening full texts based on inclusion and exclusion criteria.

    “The two authors then independently performed an eligibility assessment by carefully screening the full texts of the remaining papers, based on the inclusion and exclusion criteria described in Table 1.”
    Quote in the paperNot a substantial claim

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

    Interpretationcitation

    AI could enhance adaptive teaching and learning when using personalized learning platforms that adapt to students' learning strategies, sequence and difficulty of tasks, time of feedback, and preferences.

    “AI could enhance adaptive teaching and learning when using personalized learning platforms that adapt to students' learning strategies, the sequence and difficulty of the task abilities, the time of feedback and students' preferences (Pliakos et al., 2019).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Pliakos et al., 2019 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    The existing AIED reviews reveal a fragmented landscape that requires an overarching perspective.

    “Additionally, they reveal a fragmented landscape that requires an overarching perspective.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Literature reviews occupy the top of scientific evidence and have authoritative power.
    • Rests on an assumption: When a systematic review concludes an intervention or a technique is effective, adoption follows on both individual and institutional levels.
    • Rests on an assumption: Analyzing systematic reviews is valuable in offering a synthesized summary of the worth or the state of the art of a field.
    • Rests on an assumption: The development of AIED is rapidly advancing.

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

    Study design and conductreasoning

    Several relevant conferences were not covered.

    “However, several relevant conferences were not covered, such as the International Conference on”
    Quote in the paperNot a substantial claim

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

    Interpretationcitation

    Combining ITS with extended reality technologies can create immersive and interactive ITS that can positively affect learning outcomes.

    “When combining ITS with extended reality technologies (e.g., augmented reality (AR), virtual reality (VR), and mixed reality (MR)), which are another category of technologies on which emphasis is being placed in the context of AIED (4.76%), immersive and interactive ITS that can positively affect learning outcomes can be created (Lampropoulos, 2023a).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Lampropoulos, 2023a

      Not checked in the free run.

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

    Interpretationnone given

    AI can enhance student monitoring by analyzing data to identify patterns, predict outcomes, and provide personalized feedback to improve learning outcomes.

    “AI can enhance this process by analyzing data to identify patterns, predict outcomes, and provide personalized feedback to improve learning outcomes.”
    Quote in the paperNot a substantial claim

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

    Interpretationcitation

    Machines are far from human intelligence and emotions and cannot undertake the role of teachers.

    “These outcomes highlight the fact that machines are far from human intelligence and emotions and cannot undertake the role of teachers (González-Calatayud et al., 2021;Zhang & Aslan, 2021).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites González-Calatayud et al., 2021 source

      Not checked in the free run.

    • cites Zhang & Aslan, 2021 source

      Not checked in the free run.

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

    Interpretationcitation

    The design of AIED applications could face challenges such as AI-based misunderstanding, misleadingness, limitations, weak connection to theoretical pedagogical perspectives, and need for deep exploration of ethical and educational approaches.

    “The design of AIED applications could face challenges such as AI-based misunderstanding, misleadingness, limitations, the weak connection to theoretical pedagogical perspectives, and the need for deep exploration of ethical and educational approaches in the application of AIED in education (Akgun & Greenhow, 2021;Zawacki-Richter et al., 2019).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Akgun & Greenhow, 2021 source

      Not checked in the free run.

    • cites Zawacki-Richter et al., 2019 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationcitation

    AI systems do not always result in correct predictions, decisions, or course of action, highlighting the need for teacher intervention and supervision.

    “Additionally, although AI systems can enrich the evaluation and assessment systems, they do not always result in correct predictions, decisions, or course of action (Chassignol et al., 2018); thus, highlighting the need for teacher intervention and supervision.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Chassignol et al., 2018 source

      Not checked in the free run.

50 of 50

  • The paper relies on this without support.

    Literature reviews occupy the top of scientific evidence and have authoritative power.

    “Literature reviews are a core research approach in developing new theories and identifying trends and gaps.Further, literature reviews occupy the top of scientific evidence and have an authoritative power.”

  • The paper relies on this without support.

    When a systematic review concludes an intervention or a technique is effective, adoption follows on both individual and institutional levels.

    “In that, when a systematic review concludes an intervention or a technique is effective, adoption follows on both individual and institutional levels.”

  • The paper relies on this without support.

    Analyzing systematic reviews is valuable in offering a synthesized summary of the worth or the state of the art of a field.

    “Therefore, analyzing systematic reviews is valuable in offering a synthesized summary of the worth or the state of the art of a field.”

  • The paper relies on this without support.

    The development of AIED is rapidly advancing.

    “As the development of AIED is rapidly advancing, this study conducts a systematic review of existing literature reviews on AIED that is neither restricted by time nor by educational levels.”

  • The paper relies on this without support.

    The reviewed literature reviews are sufficiently comprehensive and representative to map the state of AIED research.

    “To do so, we examined 143 literature reviews from 2012 to 2024 covering a wide geographic distribution, multiple venues, research traditions and educational levels.”

  • The paper relies on this without support.

    The categorization scheme used to group objectives, advantages, and challenges is valid and mutually exclusive.

    “The challenges were grouped into several categories, namely ethical and safety (n = 49), AI technologies (n = 34), design (n = 28), teachers (n = 21), and students (n = 10).”

  • The paper relies on this without support.

    The prevalence of topics in the reviewed literature reviews reflects the actual state of research and practice in AIED.

    “The analysis of AIED literature reviews has identified significant advantages that AIED can bring to education.”
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  • Geographic and institutional disparities profoundly shape AI writing tool adoption, with well-resourced institutions in the Global North and major Asian hubs demonstrating significantly faster integration than underfunded universities and Global South regions.

    “Geographic and institutional disparities profoundly shape AI writing tool adoption, with well-resourced institutions in the Global North and major Asian hubs demonstrating significantly faster integration than underfunded universities and Global South regions (Bahroun et al., 2023; Crompton and Burke, 2023; Mustafa et al., 2024; Reina-Parrado et al., 2025).”

    From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Mustafa et al., 2024

    Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

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

  • Assessment-support advantage has conflicting frequencies

    Major

    The discussion reports 28 studies for this advantage, while Table 5 reports 38 for the corresponding assessment-support category. The table's higher count also conflicts with the discussion's claim that personalized learning, at 31, was the most frequently mentioned advantage.

  • Subject-area discussion points to the stakeholder figure

    Minor

    The discussion cites Fig. 6 for subject-area results, but Fig. 6 is captioned as the stakeholders figure; Fig. 8 is the subject-area figure.

  • Publication-year trend points to the PRISMA figure

    Minor

    The discussion cites Fig. 2 for a trend from 2018 onward, but the captions identify Fig. 2 as the study-selection flowchart and Fig. 3 as the publication-year distribution.

3 of 3

Cites

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Cited by

Sorted by citations

Authors
Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, López-Pernas, S., & Saqr, M.
Affiliations
  • Chengdu University of Technology (CN)
  • Beijing Normal University (CN)
  • Tehničko Veleučilište u Zagrebu (HR)
  • Stanford University (US)
  • An-Najah National University (PS)
  • Indira Gandhi National Open University (IN)
  • University of Eastern Finland (FI)
Corresponding author
Tlili, Ahmed
ORCID
Venue
Smart Learning Environments
Publisher
Springer Science and Business Media LLC
Published
2024-12-09
Volume
11
Issue
1
ISSN
2196-7091 (ISSN-L)
DOI
10.1186/s40561-024-00350-5
OpenAlex
W4405157165
Semantic Scholar
b13620566ffb4dfda76eb0ce236aa6281bb8b509
Cited by
235
References
47
Citations by year
2026: 159 · 2025: 76
FWCI
13.43
Type
journal article
Language
en
Open access
Licence
Locations
  • Smart Learning Environments (publishedVersion, cc-by)
  • University of North Texas Digital Library (University of North Texas) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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
artificial intelligence in education, meta-synthesis, research agenda, higher education, special education, teacher support, educational stakeholders, AI adoption in schools

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