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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Registry

Crossref · 10.3390/mti9080084

Cites

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Statistics

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Claims the paper makes

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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
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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
Strength:plain· not yet calibratedCausal:direct· not yet calibrated
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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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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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- 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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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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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
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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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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
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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]
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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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- 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
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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
“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
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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
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“This value was 0.89, indicating almost perfect agreement [39].”
Influential
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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
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“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].”
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“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].”
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“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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Developing deep learning in science through an adaptive AI-based STEM instructional program: evidence from sixth-grade classrooms
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“…research gaps in the depth and rigor of AI applications in early education (Boulhrir & Hamash, 2025), an overall emphasis on exploratory findings with limited intervention evidence (Garzón et al., 2025), and a narrow evidence base in science education AI research (Fayzul-lina et al., 2025).”
AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs
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Addressing the Dark Side of Differentiation: Bias and Micro-Streaming in Artificial Intelligence Facilitated Lesson Planning
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“The rapid integration of artificial intelligence (AI) into educational systems has transformed how teaching and learning are conceptualised and enacted [1].”
An Analysis of University Students’ Artificial Intelligence Literacy and Acceptance Levels of Generative AI Applications Across Various Variables
Lap, N. D., & Çakır, R. (2025) · Journal of Teacher Education and Lifelong Learning · doi:10.51535/tell.1808375 · Cited by 2
Between Inclusion and Artificial Intelligence: A Study of the Training Gaps of University Teaching Staff in Spain
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Design and Evaluation of a Generative AI-Enhanced Serious Game for Digital Literacy: An AI-Driven NPC Approach
Chernbumroong, S., Intawong, K., Asawimalkit, U., Puritat, K., & Julrode, P. (2026) · Informatics · doi:10.3390/informatics13010016 · Cited by 7
“Systematic reviews consistently report that generative AI can enhance personalized feedback, adaptive scaffolding, and learner engagement when integrated with clear instructional objectives [23,24].”
Plataformas digitales de aprendizaje impulsadas por inteligencia artificial y su desempeño en la educación superior
Ramírez-Medina, R., Zapata-Peña, J., Aguilar-Chuquizuta, D., & Bastidas-Muñoz, E. M. (2026) · International Journal of Computational Innovations Intelligent Systems and AI · doi:10.64439/cisai.v2i1.32 · Cited by 6
A standardized framework for structured pedagogy and responsible knowledge (SPARK) Building for integrating artificial intelligence into 21st century classrooms
Ofem, U. J., Ajuluchukwu, E. N., Undie, S. B., Orim, F. S., Olom, P. A., Adie, J. A., Ukatu, J. O., & Ebaye, A. S. (2026) · Discover Education · doi:10.1007/s44217-026-01184-8 · Cited by 5
From disengaged to self-determined: A latent transition analysis of students’ AI learning motivation
Liang, S., Yau, K. W., Meng, H. M. L., Chiu, T. K. F., King, I., Yam, Y., & Chai, C. S. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-13998-y · Cited by 4
“The integration of AI into school curricula is no longer a speculative endeavor but an irreversible and accelerating trend (Garzón et al., 2025).”
Understanding K-12 teachers’ adoption of generative AI through an integrated technology acceptance and perceived risk framework
Zhao, J., & Yang, X. (2026) · Discover Education · doi:10.1007/s44217-026-01611-w · Cited by 6
“This anxiety regarding the potential obsolescence of their role is not merely about automation but a deeper unease about the redefinition of their professional identity in an AI-augmented classroom, a transition that requires careful management and teacher support [13].”
Human–AI Learning: Architecture of a Human–AgenticAI Learning System
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Educación primaria y secundaria y los principios éticos del uso de la inteligencia artificial
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Aligning the Operationalization of Digital Competences with Perceived AI Literacy: The Case of HE Students in IT Engineering and Teacher Education
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Концепция Образования 5.0 как ответ на трансформацию современной индустрии и рынка труда
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An analysis on the functions and educational implications of AI digital mathematics textbooks in Korean elementary schools
Park, M. (2025) · The Mathematical Education · doi:10.63311/mathedu.25.64411 · Cited by 1
Peran Teknologi Pembelajaran terhadap Motivasi dan Kognisi Peserta Didik: Tinjauan dalam Konteks Psikologi Pendidikan
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“P ENDAHULUAN Perkembangan teknologi informasi dan komunikasi telah membawa perubahan signifikan dalam pendidikan modern, khususnya dalam pembelajaran digital yang memanfaatkan sistem manajemen pembelajaran (LMS), media interaktif, gamifikasi, dan kecerdasan buatan (AI) (Garzón et al., 2025).”
Inteligencia artificial y educación universitaria. ¿Generando realidad o simulando episteme?
Bermeo-Paucar, J. B.-P., Pérez-Martínez, L., & Antúnez, J. V. V. (2025) · European Public & Social Innovation Review · doi:10.31637/epsir-2025-2792 · Cited by 1
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Developing the CHAT Framework for Ethical Instructor-AI Integration: A Mixed Methods Study in Trinidad and Tobago
Baksh, S. (2026) · Review of Artificial Intelligence in Education · doi:10.37497/rev.artif.intell.educ.v7ii.62 · Cited by 1
How university teachers’ digital literacy influences their innovative ability: a system dynamics theoretical modeling and simulation study
Mao, Z., Tong, S., Jiang, C., Yan, S., & Bai, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1665337 · Cited by 5
Transformations in Learning and Social Relations in the Age of Artificial Intelligence
Alesanco, E. B., & Cuartas, R. S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4480-5.ch013 · Cited by 1
Artificial Intelligence, Assessment Integrity, and Professionalism in Medical Education: Global Disruption and Lessons from the Gulf Cooperation Council Region
Mir, M. M., Alharthi, M. H., Alfaifi, J. A., Sohail, S. K., Mir, S. M., Raina, N. T., Wani, J. I., Wani, S. J., Aziz, S., Patel, A. A., et al. (2026) · International Medical Education · doi:10.3390/ime5010027 · Cited by 1
AI-Empowered education: a systematic review of applied tools
Ananin, D. P., Suvirova, A. Y., Sheveleva, N. A., & Lesin, S. M. (2026) · Вестник Тамбовского университета. Серия: Гуманитарные науки · doi:10.20310/1810-0201-2026-31-1-122-137 · Cited by 1
Mapping the landscape of artificial intelligence and data literacy in education: A topic modelling and text mining approach
Sun, T., Hu, R., & Yang, N. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2026.2643165 · Cited by 1
Barriers to artificial intelligence adoption among instructors of IT-related disciplines in higher education: a survey-based study
Gorbunova, N., Ворсин, Э. И., SPIRINA, Y., Zhumagulova, S., & Popova, N. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1804254 · Cited by 5
How AI-Assisted Decision-Making Paradigms and Explainability Shape Human-AI Collaboration
Wang, Y., Ni, Q., Wei, T., Xu, H., Liu, L., & He, L. (2026) · Sustainability · doi:10.3390/su18073516 · Cited by 5
AI for Equity and Global Fluency
Bozkurt, D., Cesur, K., & Ashirkhanova, K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6325-7.ch005 · Cited by 1
Feasibility of implementing a multicultural curriculum through artificial intelligence: perspectives of educational science experts
Qin, H., & Zhou, Z. (2026) · Scientific Reports · doi:10.1038/s41598-026-48953-x · Cited by 1
The effectiveness of GAI-assisted teaching methods in medical education: a systematic review and meta-analysis
Ma, X., Liu, X., & Sun, H. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1813108 · Cited by 1
Designing AI in education programs: Structures, enablers, and constraints in higher education in the United States
Zhu, M., & Wang, C. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14011-2 · Cited by 1
“For example, Garzón et al. (2025), in a systematic review of 155 studies, identified consistent evidence that AI in education positively influences engagement and learning outcomes across both K-12 and higher education contexts.”
Digital and Artificial Intelligence Technologies in Transnational Technical and Vocational Education and Training: A Scoping Review
Rasi, S. (2026) · World Vocational and Technical Education · doi:10.1515/wvte-2026-0003 · Cited by 3
“Broader reviews identify recurring beneﬁts, risks, and implementation challenges but also point to persistent gaps in vocational and transnational contexts, reinforcing the need for sector-speciﬁc synthesis (Garzón et al. 2025).”
From Generative AI-Supported Learning to Perceived Sustainability Judgment Capability in Accounting Education
Alomar, E. H. (2026) · Sustainability · doi:10.3390/su18105059 · Cited by 1
Artificial intelligence in education: mapping research trends, engagement and cognitive disengagement with evidence from global and Asian contexts
Abhishek, N., & Martis, R. (2026) · Responsible Enterprise Pedagogy · doi:10.1108/resep-04-2026-0009 · Cited by 4
From tool to scaffold: structured human–AI collaboration and its effects on academic writing and digital critical thinking among Saudi EFL learners
Alshehri, O. A. O., Lamouchi, A., Abdellatif, M. S., Al-Dosari, M. N. A., Mekheimer, M., & Nemt-allah, M. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1830103 · Cited by 2
“…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).”
Influential
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Methodological foundations of studying digital competence of users and trust in artificial intelligence in the educational environment
Yahudina, T. S., & Воробьева, К. А. (2026) · Психолог · doi:10.25136/2409-8701.2026.3.79567 · Cited by 1
AI Educational Engagement Patterns and Their Association With Attitudes Toward AI Integration in Anatomical Science Education: A Cross‐Sectional Study
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AI-enabled assessment in higher education: Institutional culture and student meta-skill readiness across university tiers
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A Five-Step Faculty Development Checklist for Artificial Intelligence in Nursing Education
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Naseer, S., Khan, A., & Ahmad, N. (2026) · International Journal of Emerging Multidisciplinaries Social Science · doi:10.54938/ijemdss.2026.05.1.735 · Cited by 1
“Garzón”
An Explainable Educational Data Mining Framework for Misconception Detection and Differentiated Instruction
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AI Usage as a Mediator Between Study Habits, Academic Procrastination, and Academic Self-EsteemPreprint
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The Pedagogical Stance of AI
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AI Writing Assistants in Higher Education: A Comprehensive Review of Impact, Literacy, and Policy
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Aprendizaje Basado en Proyectos y Asistentes de Inteligencia Artificial: Innovación Pedagógica para el Desarrollo del Pensamiento Computacional en Entornos STEM.
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Integrating Artificial Intelligence in Vocational Higher Education: A Case Study at Politeknik Penerbangan Makassar
Raharjo, M. A., Rossydi, A., Burhanuddin, F., Octavianie, A., Idyaningsih, N., & Sukarman, S. (2025) · IJORER International Journal of Recent Educational Research · doi:10.46245/ijorer.v6i6.1107 · Cited by 1
THE IMPACT OF ARTIFICIAL INTALLIGANCE ON MODERN EDUCATION
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Artificial Intelligence in Physics Classrooms: Comparative Perspectives on ChatGPT and DeepSeek as Learning Supports
Kotsis, K. T. (2025) · EIKI Journal of Effective Teaching Methods · doi:10.59652/xf577b82
“The study emphasizes insights derived from classroom-oriented and discipline-specific literature rather than generic descriptions of AI capabilities, aiming to provide actionable theoretical guidance for educators and policymakers (Garzón et al., 2025).”
Integration of Artificial Intelligence in the Student Training of Engineering Programmes
Daneva, M. Y., Donev, V. I., & Simeonova-Ingilizova, M. D. (2025) · International Conference on Communications, Information, Electronic and Energy Systems (CIEES) · doi:10.1109/ciees66347.2025.11300221
“According to 13], AI tools are most frequently implemented through chatbots, virtual assistants, and intelligent learning management systems, while Garzón, Patiño, and Marulanda [14] emphasize benefits related to personalization and access.”
Exploring the Determinants of Continuous Participation in Virtual International Design Workshops Mediated by AI-Driven Digital Humans
Fu, Y., Yang, C. K., Wang, Z., & Mu, J. (2025) · Information · doi:10.3390/info17010024
Designing and experiencing place-based education with Socratic AI: Findings from a field workshop
Cuomo, S., Biagini, G., & Gracci, F. (2025) · Media Education · doi:10.36253/me-18958
“While the field is still evolving, this growing body of work suggests that, when carefully designed, AI can move beyond content delivery to act as a dialogic partner in inquiry, supporting deeper and more self-regulated learning (Garzón et al., 2025).”
Enhancing Educational Outcomes through the Integration of Virtual Learning Environment
Alwaalan, A., & Manuel, P. (2026) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v21i01.59363
Primary School Science Teachers’ Perceptions, Practices and Challenges in Developing Instructional Materials through AI
Mumtaz, B., Naseem, A., & Bashir, I. (2026) · Qlantic Journal of Social Sciences and Humanities · doi:10.55737/qjssh.vii-i.26461
Experiences of Medical Imaging and Radiation Sciences students regarding an assessment based on an artificial intelligence-generated research concept paper
Lewis, S. (2026) · Radiography · doi:10.1016/j.radi.2026.103367 · Cited by 1
Perspectivas estudiantiles sobre el uso de la Inteligencia Artificial en el Módulo 16 de Prepa en Línea SEP
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Teachers' Artificial Intelligence Anxiety and Psychological Well-Being: The Mediating Role of Artificial Intelligence Self-Efficacy and AttitudePreprint
Aydın, B., Saruhan, Y., & SARUHAN, U. (2026) · Research Square · doi:10.21203/rs.3.rs-8947045/v1
Adoption and Impact of Artificial Intelligence in Higher Education: A Study of Colleges in the Western Suburbs of Mumbai
Dubey, M. S. (2025) · International Journal of Research & Technology · doi:10.64882/ijrt.v13.is4.832
Artificial Intelligence in Science Teaching: An Inductive Typology of Teacher Use and Its Alignment with Artificial Intelligence Literacy FrameworksPreprint
Karampelas, K. (2026) · Research Square · doi:10.21203/rs.3.rs-9062130/v1 · Cited by 2
Rethinking brain and child development research in the age of artificial intelligence
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Theeb, A., Abdullah, N. A., Fenjan, A., & Keikhosrokiani, P. (2026) · Discover Education · doi:10.1007/s44217-026-01400-5
Validating an AI-assisted comentoring model for identifying at-risk students and for academic mentoring: a study protocol
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A Survey on AI-Based Answer Sheet Evaluation and Result Management Systems
kokare, V., & Dhotre, D. R. J. A. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-32022
Dynamic-rag: a multimodal emotion-aware prompting and retrieval-augmented LLM framework for autism support in adaptive learning systems
Ayat, N. E., Boutalline, M., Tannouche, A., Ouazri, C., & Yahyaoui, M. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02715-z
“Despite this progress, most adaptive learning platforms still rely on cognitive indicators such as accuracy, completion rate, or task performance (Garzón et al. 2025).”
A PCA-K-Means-Random Forest Framework for Analyzing AI-Enabled Educational Governance in Higher Education
Sun, Y. (2025) · International Conference on Computer Science and Management Technology (ICCSMT) · doi:10.1145/3795154.3795408
“Existing research on AI in higher education has primarily focused on instructional technologies, learning analytics, and intelligent tutoring systems [4], while studies on AI-enabled educational governance and management remain relatively limited.”
Artificial intelligence in education: How has research evolved over time? A longitudinal bibliometric analysis
Soto, F. G. C., Flores, V. I. V., Gavedia-Garcia, G., Bravo-Montoya, J., & Huaman-Fritas, J. (2026) · Iberoamerican Journal of Science Measurement and Communication · doi:10.47909/ijsmc.353 · Cited by 1
Construction and Application of an Artificial Intelligence-Based Interdisciplinary Project-Based Learning Model
Yang, Y., Zheng, S., & Cheng, M. Y. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-16705-7_26
Development of Deep Learning Approach-Based Teaching Materials for Fostering Peace-Loving Character in Elementary School Students
Giwangsa, S. F., nurob, F. S., Maulidah, N., et al. (2026) · IJORER International Journal of Recent Educational Research · doi:10.46245/ijorer.v7i2.1189
“These materials are designed to support structured discussions, culturally diverse collaborative tasks, and authentic problem-solving activities that enable students to internalize peace-related values within real social contexts (Garzón, Patiño, & Marulanda, 2025; Saleh, 2025).”
DESENVOLVIMENTO TECNOLÓGICO DE UMA PLATAFORMA PARA ANÁLISE DA FLUÊNCIA LEITORA COM USO DE INTELIGÊNCIA ARTIFICIAL
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Tan, W.-L. (2026) · International Journal of Modern Education · doi:10.35631/ijmoe.829018
TEACHING INNOVATION IN THE ERA OF GENERATIVE ARTIFICIAL INTELLIGENCE: A CONCEPTUAL FRAMEWORK FOR FUTURE-READY PEDAGOGY
Ahmad, K. A., Malik, A. F. A., Mansor, A. R., & Jayadi, P. (2026) · International Journal of Modern Education · doi:10.35631/ijmoe.829059
Student's Perspectives on Databases With Applications and View of Artificial Intelligence
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“Near these were also challenges, including students’ ethical use of AI, teachers’ fear of using generative AI, and digital outcome dependency [9].”
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“While AI can be a useful supportive tool, research emphasizes that teachers remain irreplaceable in transmitting values, developing social skills, and motivating students [24].”
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Syafri, U. A., Omar, Z. B., Asker, N. B. M., & Hassan, M. Y. B. (2026) · International Journal of Ethics Education · doi:10.1007/s40889-026-00245-8 · Cited by 5
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Fudholi, D. R., & Capiluppi, A. (2026) · IEEE International Conference on Software Analysis, Evolution and Reengineering - Companion (SANER-C) · doi:10.1109/saner-c67878.2026.00019
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Álvarez, A. B. A., Cueva, L. M. P., & Cepeda, J. P. V. (2026) · Revista Científica Multidisciplinar G-nerando · doi:10.66473/rcmg.v7i1.1103
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Idoga, P. E., & Tukur-Bello, F. (2026) · Discover Education · doi:10.1007/s44217-026-01545-3
“Tools like smart tutoring systems, virtual learning helpers, and personalized feedback methods create tailored learning paths that adjust to each student’s progress, motivation, and previous knowledge [19, 31].”
Teacher Competency and Readiness for Artificial Intelligence Integration in High School Education
Mahilum, J.-M., & Lausa, S. M. (2026) · Psychology and Education A Multidisciplinary Journal · doi:10.70838/pemj.560201
A Multimodal Temporal Diagnostic and Personalized Intervention Framework for Sustainable Teacher Preparation: Evidence from TPACK Development in Pre-Service Kindergarten Teachers
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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

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

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

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Claims the paper makes

Claims other papers make about this one

Self-check

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