Cited by 265Artificial Intelligence in EducationIntelligent Tutoring Systems and Adaptive LearningArtificial Intelligence in Healthcare and Education

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Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges

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Garzón, Juan, Patiño, Eddy, Marulanda, Camilo·2025·Multimodal Technologies and Interaction

DOI 10.3390/mti9080084

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Abstract

Artificial intelligence (AI) is changing how we teach and learn, generating excitement and concern about its potential to transform education. To contribute to the debate, this systematic literature review examines current research trends (publication year, country of study, publication journal, education level, education field, and AI type), as well as the benefits and challenges of integrating AI into education. This review analyzed 155 peer-reviewed empirical studies published between 2015 and 2025. The review reveals a significant increase in research activity since 2022, reflecting the impact of generative AI tools, such as ChatGPT. Studies highlight a range of benefits, including enhanced learning outcomes, personalized instruction, and increased student motivation. However, there are challenges to overcome, such as students’ ethical use of AI, teachers’ resistance to using AI systems, and the digital dependency these systems can generate. These findings show AI’s potential to enhance education; however, its success depends on careful implementation and collaboration among educators, researchers, and policymakers to ensure meaningful and equitable outcomes.

AI snapshot

Written from the paper’s full text.

TL;DR

This systematic review synthesizes 155 peer-reviewed empirical studies on AI in education, reporting a sharp rise in research since 2022 and especially 2023, alongside benefits such as enhanced learning outcomes, personalized instruction, and motivation. It also identifies persistent challenges including ethical concerns, teacher resistance, and digital dependency, concluding that AI’s success depends on careful, equitable implementation.

Key findings

Research activity increased markedly: publication counts rose from 2 studies in 2015 to 35 in 2023 and 54 in 2024, with the paper linking the spike to generative AI tools such as ChatGPT.
Most studies focused on higher education (97 studies), followed by secondary education (26), primary education (20), and teachers (12); language learning was the most represented education field (40 studies), followed by ICT (25) and science education (22).
Benefits were grouped into 22 types across cognitive, personal, social, and teacher categories; AI was predominantly associated with cognitive and personal gains, especially enhanced learning outcomes, personalized instruction, and increased student motivation.
Challenges were grouped into 16 types; the most frequently reported were ethical concerns (23 studies, 14.8%), teacher resistance (19, 12.3%), digital dependence (17, 11.0%), technical difficulties (10, 6.5%), and increased anxiety (9, 5.8%).
Studies were conducted in 35 countries, with Asian nations—particularly China and Taiwan—and the United States dominating; Latin America and Africa were relatively absent. The reviewed studies appeared across 37 indexed journals, with *Computers & Education* most prominent.

Methods

The review followed PRISMA and Kitchenham and Charters’ three-step process: planning, conducting, and reporting. Searches were run in Web of Science, Scopus, and Taylor & Francis using a defined AI/education/empirical-study search string, completed on 1 July 2025, yielding 3,406 potential studies; backward citation searching added 435.
After removing 1,838 duplicates, 2,003 studies were screened by title, abstract, and keywords; 1,432 were excluded, 571 were assessed against inclusion/exclusion criteria, and 155 studies remained for final analysis.
Data extraction covered title, authors, year, country, journal, education level, education field, AI type, benefits, and challenges. Thematic analysis used inductive coding in Microsoft Excel, with independent coding by two authors and disagreements resolved by the first author; Cohen’s Kappa was 0.89. Education levels and fields were standardized using ISCED.

Limitations

The paper states that grey literature such as conference papers, dissertations, and policy reports was excluded, potentially omitting emerging or context-specific insights; it also states that it does not deeply explore how AI applications align with specific pedagogical models or teaching strategies, nor does it thoroughly examine long-term consequences such as changes in teacher roles, student autonomy, or institutional policies.
The paper states that it did not apply a formal framework to assess certainty of evidence; confidence varies by theme, with cognitive and motivational benefits showing moderate to high confidence, while digital overdependence and ethical risks showed lower confidence due to less consistent reporting.
Evident from the text: the abstract and methods report 155 studies published between 2015 and 2025, while the conclusion reports 132 empirical studies published between 2015 and 2024, an inconsistency in the reported sample size and date range.

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Integrity signals

Registry

Crossref · 10.3390/mti9080084

Cites

62 of 62 references verified0 citation errors · 0 not checkable

Statistics

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Claims the paper makes

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Probably not a substantial claim. 31

In the paper
“Studies in this area often involve teaching students about AI itself or using AI to support algorithmic thinking, coding instruction, or learning analytics [56].”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.5. Education Field
What the sources say
- Not checked yetcites [56] Huang, 2024, Enhancing Computational Thinking Skills Through Artificial Intelligence Education at a STEAM High School, Sci. Educ. source
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
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It may state two separate claims as one.
In the paper
“The predominance of higher education in AI-related research aligns with prior reviews in the field of educational technology, which attribute this trend to the increased availability of digital infrastructure, funding mechanisms, and institutional autonomy in universities [36].”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.4. Education Level
What the sources say
- Not checked yetcites [36] Guo, 2024, Artificial Intelligence in Education Research during 2013–2023: A Review Based on Bibliometric Analysis, Educ. Inf. Technol. source
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“This diversity suggests that the topic transcends traditional disciplinary boundaries, attracting attention from both educational technology specialists and researchers in broader pedagogical and computational domains [37].”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.3. Publication Journal
What the sources say
- Not checked yetcites [37] Wang, 2024, Artificial Intelligence in Education: A Systematic Literature Review, Expert Syst. Appl. source
Strength:hedged· not yet calibratedCausal:direct· not yet calibrated
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Without including diverse educational contexts, it is difficult to ensure that AI solutions are adaptable, ethical, and relevant on a global scale [46,47].”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.2. Country of the Study
What the sources say
- Not checked yetcites [46] Holmes, W., Bialik, M., and Fadel, C. (2023). Artificial Intelligence in Education. Data Ethics: Building Trust: How Digital Technologies can Serve Humanity, Globethics Publications. source
- Not checked yetcites [47] Yambal, S., and Waykar, Y.A. (2025). Future of Education Using Adaptive AI, Intelligent Systems, and Ethical Challenges, IGI Global Scientific Publishing. source
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Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Educational systems differ widely in terms of language, infrastructure, curricula, and student needs.
None of the sources cited for this claim have been checked yet.
In the paper
“This systematic review adopted the PRISMA statement approach [34] and followed the recommendations of Kitchenham and Charters [35].”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 3. Methods
What the sources say
- Not checked yetcites [34] Page, 2021, The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews, Syst. Rev. source
- Not checked yetcites [35] Kitchenham, B., and Charters, S. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering Version 2.3.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Without such guidance, there is a risk that AI may be applied inconsistently or inequitably as its adoption increases [43,44].”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.1. Publication Year
What the sources say
- Not checked yetcites [43] Costa, C.J., Aparicio, M., Aparicio, S., and Aparicio, J.T. (2024). The Democratization of Artificial Intelligence: Theoretical Framework. Appl. Sci., 14. source
- Not checked yetcites [44] Zou, 2025, Navigating the Future: Establishing a Framework for Educators’ Pedagogic Artificial Intelligence Competence, Eur. J. Educ. source
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None of the sources cited for this claim have been checked yet.
In the paper
“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.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.2. Benefits Associated with AI in Education
What the sources say
- Not checked yetcites [11] Strielkowski, 2024, AI-driven Adaptive Learning for Sustainable Educational Transformation, Sustain. Dev. source
- Not checked yetcites [57] Xu, 2024, AI in Education: Enhancing Learning Experiences and Student Outcomes, Appl. Comput. Eng. source
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This trend is not surprising given that many AI applications originate within ICT-related fields and are often conducted by technically proficient educators or within computing departments.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.5. Education Field
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Many AI applications originate within ICT-related fields and are often conducted by technically proficient educators or within computing departments.
None of the sources cited for this claim have been checked yet.
In the paper
“These findings are consistent with previous studies [64,65], which emphasize that trust, transparency, and ethical governance are foundational for the successful use of AI in education.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.3. Challenges Associated with AI in Education
What the sources say
- Not checked yetcites [64] Shukla, 2024, Principles Governing Ethical Development and Deployment of AI, Int. J. Eng. Bus. Manag. source
- Not checked yetcites [65] Fu, 2024, Navigating the Ethical Terrain of AI in Education: A Systematic Review on Framing Responsible Human-Centered AI Practices, Comput. Educ. Artif. Intell. source
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Compared to earlier studies [19,37], this analysis offers a deeper categorization and highlights the tension between AI's potential and its limitations.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.3. Challenges Associated with AI in Education
What the sources say
- Not checked yetcites [19] Dimitriadou, 2023, A Critical Evaluation, Challenges, and Future Perspectives of Using Artificial Intelligence and Emerging Technologies in Smart Classrooms, Smart Learn. Environ. source
- Not checked yetcites [37] Wang, 2024, Artificial Intelligence in Education: A Systematic Literature Review, Expert Syst. Appl. source
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None of the sources cited for this claim have been checked yet.
In the paper
“These findings align with research by Younas et al. [59] and Wang et al. [37], which have also found that intelligent tutoring systems and gamified AI applications promote student interest and self-directed learning.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.2. Benefits Associated with AI in Education
What the sources say
- Not checked yetcites [59] Younas, 2025, A Comprehensive Systematic Review of AI-Driven Approaches to Self-Directed Learning, IEEE Access source
- Not checked yetcites [37] Wang, 2024, Artificial Intelligence in Education: A Systematic Literature Review, Expert Syst. Appl. source
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“Our classification of AI benefits into cognitive, personal, social, and teacher domains offers a more comprehensive framework than earlier reviews [16][17][18][19][20][21], which often focused narrowly on learning outcomes.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.2. Benefits Associated with AI in Education
What the sources say
- Not checked yetcites [16][17][18][19][20][21]
Load-bearingStrength:plain· not yet calibrated
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given that teachers play a central role in mediating the integration of AI into classrooms, this gap underlines a critical area for future research.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.4. Education Level
Load-bearingStrength:plain· not yet calibrated
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Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Teachers play a central role in mediating the integration of AI into classrooms.
In the paper
“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.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.3. Challenges Associated with AI in Education
What the sources say
- Not checked yetcites [11] Strielkowski, 2024, AI-driven Adaptive Learning for Sustainable Educational Transformation, Sustain. Dev. source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“These results align with studies that demonstrate how AI can streamline administrative duties, generate personalized learning analytics, and support instructional planning [15,17].”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.2. Benefits Associated with AI in Education
What the sources say
- Not checked yetcites [15] Holmes, 2022, State of the Art and Practice in AI in Education, Eur. J. Educ. source
- Not checked yetcites [17] Zhai, 2021, A Review of Artificial Intelligence (AI) in Education from 2010 to 2020, Complexity source
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53 of 53

The paper relies on this without support.
Teachers play a central role in mediating the integration of AI into classrooms.
“Given that teachers play a central role in mediating the integration of AI into classrooms, this gap underlines a critical area for future research.”
The paper relies on this without support.
The release of ChatGPT and subsequent generative AI systems marked a milestone in education, ushering in the post-ChatGPT era.
“The release of ChatGPT and subsequent similar generative AI systems, such as DeepSeek, Claude, Gemini, and Copilot, has marked a milestone in education, ushering in what might be called the post-ChatGPT era.”
The paper relies on this without support.
Educational systems differ widely in terms of language, infrastructure, curricula, and student needs.
“Educational systems differ widely in terms of language, infrastructure, curricula, and student needs.”
The paper relies on this without support.
Many AI applications originate within ICT-related fields and are often conducted by technically proficient educators or within computing departments.
“This trend is not surprising given that many AI applications originate within ICT-related fields and are often conducted by technically proficient educators or within computing departments.”
The paper relies on this without support.
Educators play a central role in mediating the implementation of AI tools.
“This is notable given the central role of educators in mediating the implementation of AI tools.”
The paper relies on this without support.
Teachers are essential mediators of AI adoption.
“Given that teachers are essential mediators of AI adoption, more work is needed to empower educators with tools that enhance, not replace, their professional agency and pedagogical expertise.”

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Conclusion reports a different review size and date range
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The conclusion says the review examined 132 studies published through 2024, whereas the abstract and methods report 155 included studies published through 2025. The 132 figure matches the sum of the annual counts shown for 2015-2024, which explicitly exclude 2025 studies, but it is not the size of the full review.
Five dimensions claimed, but six listed
Minor
The sentence says five dimensions were examined but names six: year, country, journal, educational level, education field, and AI type. The research-question description also lists all six.

2 of 2

Cochrane Handbook for Systematic Reviews of Interventions
(2019) · doi:10.1002/9781119536604 · Cited by 14,828
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01626-4 · Cited by 13,874 · cited 1×
“This systematic review adopted the PRISMA statement approach [34] and followed the recommendations of Kitchenham and Charters [35].”
Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit
Cohen, J. (1968) · Psychological Bulletin · doi:10.1037/h0026256 · Cited by 8,736 · cited 1×
“This value was 0.89, indicating almost perfect agreement [39].”
Influential
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · cited 3×
“In addition to database search, we conducted backward citation searching by re-viewing the reference lists of relevant prior systematic reviews [14,16–19, 26, After removing 1838 duplicate entries, the second and third authors screened the remaining studies ( n = 2003 ) based on their titles,…”
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364 · cited 2×
“The review by Zhai et al. [17] categorized educational AI research into three dimensions: development (e.g., intelligent tutoring systems and adaptive learning models), extraction (e.g., feedback and reasoning), and application (e.g., immersive learning and gamification).”
Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100118 · Cited by 1,181 · cited 2×
“Chiu et al. [18] examined the scattered nature of research in educational AI and proposed a framework that categorizes AI applications into four key domains: learning, teaching, assessment, and administration.”
State of the art and practice in AI in education
Holmes, W., & Tuomi, I. (2022) · European Journal of Education · doi:10.1111/ejed.12533 · Cited by 1,093 · cited 2×
“These results align with studies that demonstrate how AI can streamline administrative duties, generate personalized learning analytics, and support instructional planning [15,17].”
Artificial Intelligence in Education: A Systematic Literature Review
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124167 · Cited by 977 · cited 4×
“This diversity suggests that the topic transcends traditional disciplinary boundaries, attracting attention from both educational technology specialists and researchers in broader pedagogical and computational domains [37].”
Influential
Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions
Abulibdeh, A. O., Zaidan, E., & Abulibdeh, R. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.140527 · Cited by 866 · cited 1×
“In educational contexts, AI is a branch of computer science that can perform tasks such as reasoning, decision-making, and learning, which are traditionally associated with human intelligence [4,5].”
Components of Education 4.0 in 21st Century Skills Frameworks: Systematic Review
González-Pérez, L. I., & Soledad, M. (2022) · Sustainability · doi:10.3390/su14031493 · Cited by 762 · cited 1×
“Educational environments, on the other hand, include the physical, social, and technological contexts in which learning occurs [6].”

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Cole, M., John, V.-S., Scribner, S., and Souberman, E. (1978). Mind in Society: The Development of Higher Psychological Processes, Harvard University Press.

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“…connection, even if they offer significant cognitive and motivational benefits such as individualized feedback and increased engagement (Garzón et al., 2025) Furthermore, the ISAR model (inversion, substitution, augmentation, redefinition) is high-lighted in a 2025 meta-reflection on…”
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“Hence, Khoza and Van Der Walt (2025) and Garzón et al. (2025) emphasise that educators must refine and redesign assessment to focus more on independent critical thinking and creativity, skills less easily replicated by AI, and Maluleke (2025) emphasises pedagogical innovation in enhancing the…”
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“Concerns regarding misinformation, fabricated references, algorithmic bias, explainability, and ethical accountability continue to influence researchers’ confidence in AI technologies (S. J. Kim, 2024; UNESCO, 2023; Hosseini et al., 2023; Garzón et al., 2025).”
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“Students need support they encounter directly: clear assessment rules on acceptable GenAI use, examples of critical evaluation in coursework, accessible workshops embedded in their disciplines, and encouragement from trusted instructors (Grassini, 2023; Garzón et al., 2025).”
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Digital decoupling: educational stratification and dual-track effects of AI displacement and augmentation in U.S. occupations
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Students' Voice in the Era of AI
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Using Customized Generative Chatbots to Structure Physical Education Didactics: The UniVersus Project
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Humanising artificial intelligence for enhancing engagement and retention in open and distance learning in sub-Saharan Africa
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Emerging Artificial Intelligence Technologies and Their Transformative Potential in Higher Education
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Building Students’ Growth Mindset through Artificial Intelligence Integration in Classroom Learning
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An Inductive Typology of Artificial Intelligence Use in Science Teaching and Its Alignment with AI Literacy FrameworksPreprint
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Artificial Intelligence Applications in Mental Health: A Systematic Review of Clinical Practice, Educational Transformation, and Ethical Governance
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A Multimodal Immersive Intervention for Artificial Intelligence Literacy and Metacognitive Self-Regulation in Pre-Service Teacher Education
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Authors: Garzón, J., Patiño, E., & Marulanda, C.
Affiliations: Universidad Católica de Oriente (CO)
Corresponding author: Garzón, Juan
ORCID: Garzón, Juan
Patiño, Eddy
Venue: Multimodal Technologies and Interaction
Publisher: MDPI AG
Published: 2025-08-20
Volume: 9
Issue: 8
Pages: 84
ISSN: 2414-4088 (ISSN-L)
DOI: 10.3390/mti9080084
OpenAlex: W4413401681
Semantic Scholar: 594051870a74e72b85f42c7f52be0954184c81ca
Cited by: 265
References: 65
Citations by year: 2026: 238 · 2025: 27
FWCI: 40.73
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Multimodal Technologies and Interaction (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
OSF Preprints (OSF Preprints) (submittedVersion)
Topics: Social Sciences › Education › Artificial Intelligence in Education
Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: artificial intelligence in education, generative AI, ChatGPT, personalized instruction, student motivation, learning outcomes, ethical use of AI, digital dependence, research trends, educational equity

About these checks

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.

A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges

Abstract

Overview

AI snapshot

TL;DR

Key findings

Methods

Limitations

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks