The DOI matches its Crossref record; 62 of the 65 references it cites were found in a registry or an index; none of the 46 sources its claims cite is checked for support yet.
Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges
Open accessAbstract
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.
Overview
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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Claims the paper makes
49 Own result · 35 Not checked yet · 29 Own interpretation · 17 Own method · 15 No basis given
A result the paper reports from its own data — 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 analysis reveals a marked increase in research in 2023.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.1. Publication Year
boosted- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Only one study included students from postgraduate programs (i.e., Master's or Doctoral programs); therefore, we included it in the higher education category.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.4. Education Level
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In the paper
“Following closely, Interactive Learning Environments and Education and Information Technologies also stand out as prominent publication venues.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.3. Publication Journal
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In the paper
“Most studies examined short-term interventions; however, the growing use of AI in diverse educational contexts suggests the need for further research that explores its long-term integration.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.1. Publication Year
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In the paper
“Asian nations, particularly China and Taiwan, along with the United States, dominate the field.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.2. Country of the Study
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In the paper
“The review reveals a significant increase in research activity since 2022, reflecting the impact of generative AI tools, such as ChatGPT.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > Abstract
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In the paper
“The reviewed studies were published across 37 different indexed journals, indicating a broad and diverse interest in AI in education.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.3. Publication Journal
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In the paper
“Computers & Education emerged as the most prominent journal, highlighting its established role as a leading platform for empirical and theoretical contributions in the field of technology-enhanced learning.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.3. Publication Journal
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In the paper
“Most studies focused on higher education.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.4. Education Level
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In the paper
“This process allowed us to identify 16 different types of challenges.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.3. Challenges Associated with AI in Education
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In the paper
“Studies highlight a range of benefits, including enhanced learning outcomes, personalized instruction, and increased student motivation.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > Abstract
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In the paper
“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.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > Abstract
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In the paper
“In contrast, the number of studies conducted at the primary, secondary, and teacher education levels was notably lower and relatively evenly distributed.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.4. Education Level
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In the paper
“The studies were conducted in 35 countries from all inhabited continents.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.2. Country of the Study
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“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
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In the paper
“Other frequently reported cognitive benefits include improvements in problemsolving, knowledge retention, and critical thinking.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.2. Benefits Associated with AI in Education
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In the paper
“Specifically, many educators resist adopting AI due to skepticism, lack of trust, fear of job displacement, or a reluctance to change their teaching methods.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.3. Challenges Associated with AI in Education
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In the paper
“Ethical concerns and teacher resistance emerged as the most prominent challenges, indicating that the human dimensions of AI adoption, such as privacy, bias, and user acceptance, remain critical barriers.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.3. Challenges Associated with AI in Education
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In the paper
“Meanwhile, Embodied and Immersive Systems and Intelligent Tutoring Systems remain less frequent, despite strong evidence of their effectiveness in fostering engagement and deep learning [32].”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.6. Type of AI
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In the paper
“There is a strong dominance of language learning, followed by ICT and science education.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.5. Education Field
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In the paper
“For example, cognitive and motivational benefits of AI were frequently reported across multiple contexts and supported by converging evidence, suggesting moderate to high confidence in these themes.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 5. Limitations of the Study and Future Work
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In the paper
“The analysis shows that Generative AI was the most implemented type, followed by Conversational and NLP Agents, and Machine Learning Models.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.6. Type of AI
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“The results show a multifaceted range of challenges associated with integrating AI into educational environments, with personal and teacher-related concerns being the most frequently reported.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.3. Challenges Associated with AI in Education
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In the paper
“While all included studies were peer-reviewed”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 5. Limitations of the Study and Future Work
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In the paper
“The findings reveal a significant increase in AI-related educational research, especially from 2023 onward, coinciding with the widespread release of tools such as ChatGPT.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 6. Conclusions
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In the paper
“Occasional discrepancies in this process were resolved through consensus.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > Studies Were Included if They:
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“Before synthesizing the findings, we carefully reviewed all extracted data to ensure they were complete and consistent.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 3.3. Reporting the Review
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“A standardized data extraction form was developed by the research team to ensure consistency in coding and interpretation.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 3.2. Conducting the Review
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“The themes were identified through iterative comparison, and disagreements were resolved in consultation with the first author.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 3.2. Conducting the Review
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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
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“This protocol was developed internally by the research team during the planning stage and registered on the Open Science Framework (OSF) platform.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 3.1. Planning the Review
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In the paper
“The concentration of publications in these high-impact journals highlights the growing academic recognition of AI's transformative potential in education.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.3. Publication Journal
plaincorrelational- Asserted: passes — The authors put it forward as true.
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“Although the form was not formally piloted, it was collaboratively refined during the initial phase of data extraction.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 3.2. Conducting the Review
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“The second and third authors independently coded the extracted qualitative data manually using Microsoft Excel.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 3.2. Conducting the Review
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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
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In the paper
“The uneven geographic representation raises concerns about whether AI tools can effectively meet the diverse needs of educational systems worldwide.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.2. Country of the Study
plainconditional- Asserted: passes — The authors put it forward as true.
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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
plainconditional- Asserted: passes — The authors put it forward as true.
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- 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.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“In cases where certain descriptive details, such as the country of origin, were not explicitly provided, we used the first author's affiliation as a proxy.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 3.3. Reporting the Review
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although several reviews have explored AI's role in education, most are limited to pre-ChatGPT publications or narrow scopes focused on specific applications, user groups, pedagogical models, or types of AI systems.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“If the second and third authors disagreed on any specific paper, the first author reviewed the paper and made the final decision to maintain consistency and rigor.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > Studies Were Included if They:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
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 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
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To support meaningful comparisons across studies, we standardized education levels and subject areas using the International Standard Classification of Education (ISCED).”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 3.3. Reporting the Review
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Third, although the results raised ethical concerns and digital dependency, we do not thoroughly examine the long-term consequences of AI integration, such as changes in teacher roles, student autonomy, or institutional policies.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 5. Limitations of the Study and Future Work
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, this review did not apply a formal framework to assess the certainty of the evidence for each outcome.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 5. Limitations of the Study and Future Work
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“These tools offer solutions to language learning challenges, such as vocabulary acquisition, grammar correction, and pronunciation feedback, which may explain their popularity among researchers.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.5. Education Field
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As such, the findings may slightly overrepresent more established academic perspectives and underrepresent practitioner-driven innovations.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 5. Limitations of the Study and Future Work
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
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.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, while the review identifies general benefits and challenges of AI in education, it does not deeply explore how different AI applications align with specific pedagogical models or teaching strategies across disciplines and contexts.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 5. Limitations of the Study and Future Work
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“Additionally, implementing AI in these contexts often requires adaptations to pedagogy and content, which may dissuade rapid experimentation.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.4. Education Level
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“These dualities, where certain factors are reported as strengths and weaknesses, highlight the importance of considering contextual, technological, and individual variables when evaluating AI's impact.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.3. Challenges Associated with AI in Education
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
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
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“These gaps point to potential missed opportunities for innovation.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1.5. Education Field
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. 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
“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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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.
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
“From a cognitive perspective, digital dependence and increased anxiety point to potential unintended consequences of AI overuse, such as reduced creativity and heightened psychological stress, particularly when AI systems are poorly designed or overly focused on assessment.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.3. Challenges Associated with AI in Education
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“These findings suggest that AI is widely perceived as a powerful tool for personalizing instruction, adapting to individual learning needs, and fostering student engagement.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.2. Benefits Associated with AI in Education
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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
“Additionally, benefits like digital literacy and accessibility demonstrate AI's role in equipping students with essential 21st-century skills and making learning more inclusive for diverse populations.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.2. Benefits Associated with AI in Education
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
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.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, the central boundary condition is that learning benefits depend on how learners interact with mediation—when students engage with AI outputs by questioning, comparing drafts, rejecting or adapting suggestions, and justifying compositional choices, the interaction constitutes active mediated learning rather than passive text transfer (Garzón et al., 2025; Ilgun Dibek et al., 2025).”
From From tool to scaffold: structured human–AI collaboration and its effects on academic writing and digital critical thinking among Saudi EFL learners (2026) · cites this paper as Garzón et al., 2025
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Systematic reviews of AI in education describe accelerating use of AI tools for adaptive learning, intelligent tutoring, automated assessment feedback, and writing support, with reported associations to enhanced learner engagement, higher feedback quality, and a broad range of cognitive outcomes (Garzón et al., 2025; Ilgun Dibek et al., 2025).”
From From tool to scaffold: structured human–AI collaboration and its effects on academic writing and digital critical thinking among Saudi EFL learners (2026) · cites this paper as Garzón et al., 2025
Experimental — accuracy not yet measured
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 6. Conclusions
“This systematic literature review examined 132 empirical studies published between 2015 and 2024 to identify the prevailing trends, benefits, and challenges of integrating AI in educational environments.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > Abstract
“This review analyzed 155 peer-reviewed empirical studies published between 2015 and 2025.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 4.1. Trends Characterizing the Current Research on AI in Education
“We examined five key dimensions: publication year, country of study, publication journal, educational level, education field, and AI type.”
§ Systematic Review of Artificial Intelligence in Education: Trends, Benefits, and Challenges > 1. Introduction
“This question examines trends in publication year, the country where the study was conducted, the journal in which the study was published, the educational level at which the study was implemented, the educational field involved in the study, and the type of artificial intelligence system implemented in the study.”
Citations
Cites
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].”
InfluentialArtificial 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).”
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].”
InfluentialAbulibdeh, 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].”
Research works printed but not linked: 1
- Cole, M., John, V.-S., Scribner, S., and Souberman, E. (1978). Mind in Society: The Development of Higher Psychological Processes, Harvard University Press.
Cited by
Sorted by citations
Conceptualizing the Impact of AI on Teacher Knowledge and Expertise: A Cognitive Load Perspective
Rind, I. A. (2026) · Education Sciences · doi:10.3390/educsci16010057 · Cited by 27
Mapping the evolution of AI in education: Toward a co-adaptive and human-centered paradigm
Feng, S., Zhang, H., & Gašević, D. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100513 · Cited by 30
AI and the digital divide in education
Matjie, M. A., Nethavhani, A., & Matlakala, M. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1759027 · Cited by 46
“AI should change learning for all learners across the globe (Luckin et al., 2016; Selwyn and Facer, 2013); however, that is not always the case (Garzón et al., 2025; Krause et al., 2024).”
Ansari, S. R., & Qamari, I. N. (2025) · Discover Education · doi:10.1007/s44217-025-00865-0 · Cited by 36
“These findings are consistent with Garzón et al. 2025 [23], who, in their systematic review of AI in education from 2015 to 2025, similarly reported a significant surge in publications beginning in 2022, particularly driven by generative AI applications such as ChatGPT.”
Adaptive and Personalized Learning in Higher Education: An Artificial Intelligence-Based Approach
Hernández-Herrera, J. R., Ortiz-Bejar, J., & Ortiz-Bejar, J. (2026) · Education Sciences · doi:10.3390/educsci16010109 · Cited by 22
Metadata
- Authors
- Garzón, J., Patiño, E., & Marulanda, C.
- Affiliations
- Universidad Católica de Oriente (CO)
- Corresponding author
- Garzón, Juan
- Venue
- Multimodal Technologies and Interaction
- Publisher
- MDPI AG
- Published
- 2025-08-20
- Volume
- 9
- Issue
- 8
- Pages
- 84
- ISSN
- 2414-4088 (ISSN-L)
- 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
- 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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