The DOI matches its Crossref record; 50 of the 55 references it cites were found in a registry or an index; none of the 64 sources its claims cite is checked for support yet.
AI in education: A review of personalized learning and educational technology
Open accessAbstract
The integration of Artificial Intelligence (AI) in education has ushered in a transformative era, redefining traditional teaching and learning methods. This review explores the multifaceted role of AI in education, with a particular focus on personalized learning and educational technology. The synergy between AI and education promises to address individualized needs, enhance student engagement, and optimize learning outcomes. Personalized learning, enabled by AI algorithms, tailors educational experiences to the unique needs, preferences, and pace of each student. This approach goes beyond a one-size-fits-all model, fostering a more inclusive and effective learning environment. The review delves into the diverse applications of AI-driven personalized learning, ranging from adaptive content delivery and real-time feedback to intelligent tutoring systems. It analyzes the impact of these technologies on student performance, highlighting the potential to narrow educational gaps and cater to diverse learning styles. Educational technology, powered by AI, extends beyond the classroom, encompassing online platforms, virtual reality, and interactive tools. The review explores the integration of AI in curriculum development, content creation, and assessment methods, offering insights into how these technologies augment the teaching and learning experience. Furthermore, the review examines the role of AI in automating administrative tasks, allowing educators to redirect their focus towards personalized instruction. Challenges and ethical considerations associated with the adoption of AI in education are also scrutinized. Privacy concerns, algorithmic biases, and the digital divide are discussed, emphasizing the importance of responsible AI implementation. The review underscores the need for collaborative efforts among educators, policymakers, and technologists to establish ethical guidelines and ensure the equitable distribution of AI-enhanced educational resources. This review provides a comprehensive examination of the evolving landscape of AI in education, with a spotlight on personalized learning and educational technology. As the symbiosis between AI and education continues to evolve, this synthesis of current research and trends aims to guide future developments, fostering an informed and progressive approach to the integration of AI in the educational sphere.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This narrative review examines AI in education with emphasis on personalized learning and AI-enhanced educational technology, arguing that these tools can tailor instruction, improve engagement, and support equity while raising ethical and access concerns. It calls for responsible implementation, ethical guidelines, and collaboration among educators, policymakers, and technologists.
Key findings
- AI-driven personalized learning uses algorithms to adapt content, pacing, and assessment to individual students, including adaptive content delivery, real-time feedback, and intelligent tutoring systems.
- AI-enhanced educational technology is described as transforming curriculum development, content creation, assessment, virtual reality/interactive tools, and administrative automation.
- The review identifies major challenges: privacy concerns, algorithmic biases, the digital divide, and the need for ethical guidelines and responsible AI usage.
- AI is presented as potentially advancing educational equity by addressing diverse learning styles, improving accessibility, and fostering inclusive learning environments, though no quantitative outcomes are reported.
Methods
- Design: narrative review of AI in education, focused on personalized learning and educational technology.
- Data or sample: Not stated in the paper; no systematic search strategy, databases, inclusion criteria, or study sample are described.
- Main analysis: thematic synthesis of existing research and trends, with sections on personalized learning, EdTech, challenges, equity, future directions, and recommendations.
Limitations
- The paper does not state limitations of the review itself.
- Evident from the text: it lacks a systematic review protocol, search dates, inclusion/exclusion criteria, and empirical data or quantitative outcomes; its claims are broad and largely supported by cited literature rather than original analysis.
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Claims the paper makes
24 Not checked yet · 15 Own interpretation
None of the sources cited for this claim have been checked yet.
In the paper
“This data-driven approach enables educators and curriculum developers to tailor instructional content, ensuring relevance and alignment with evolving educational standards.”
§ AI in education: A review of personalized learning and educational technology > 4. Educational Technology Enhanced by AI
What the sources say
- Not checked yetcites Ahmad, et. al., 2023
- Not checked yetcites Górriz, et. al., 2020
- Not checked yetcites Luan, et. al., 2020
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“AI algorithms analyze vast amounts of data, including students' progress, performance, and preferences, to tailor educational experiences.”
§ AI in education: A review of personalized learning and educational technology > 2. Artificial Intelligence in Education: Unveiling the Significance of Personalized Learning and Educational Technology
What the sources say
- Not checked yetcites Kabudi, Pappas & Olsen, 2021
- Not checked yetcites Zhai, et. al., 2021
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Intelligent tutoring systems, powered by AI, offer personalized guidance, adapting to the pace at which students grasp concepts.”
§ AI in education: A review of personalized learning and educational technology > 2. Artificial Intelligence in Education: Unveiling the Significance of Personalized Learning and Educational Technology
What the sources say
- Not checked yetcites Kabudi, Pappas & Olsen, 2021
- Not checked yetcites Zhai, et. al., 2021
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Intelligent tutoring systems (ITS) represent a sophisticated application of AI in personalized learning.These systems leverage AI algorithms to emulate human tutors, providing tailored instruction and support.”
§ AI in education: A review of personalized learning and educational technology > 3. Personalized Learning with AI
What the sources say
- Not checked yetcites Akyuz, 2020
- Not checked yetcites Conati, et. al., 2021
- Not checked yetcites Kim & Kim, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, AI facilitates real-time data analysis, enabling educators to gain insights into student engagement, comprehension, and areas of struggle.”
§ AI in education: A review of personalized learning and educational technology > 2. Artificial Intelligence in Education: Unveiling the Significance of Personalized Learning and Educational Technology
What the sources say
- Not checked yetcites Kabudi, Pappas & Olsen, 2021
- Not checked yetcites Zhai, et. al., 2021
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“AI-driven personalized learning platforms, however, can offer a range of instructional modalities, including visual, auditory, and kinesthetic approaches.”
§ AI in education: A review of personalized learning and educational technology > 3. Personalized Learning with AI
What the sources say
- Not checked yetcites Kabudi, Pappas & Olsen, 2021
- Not checked yetcites Luan & Tsai, 2021
- Not checked yetcites Regan & Jesse, 2019
plainconditional- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“AI algorithms play a pivotal role in enabling adaptive content delivery, a cornerstone of personalized learning.These algorithms analyze vast datasets, including students' historical performance, engagement patterns, and preferences, to dynamically adjust the difficulty and format of instructional materials.”
§ AI in education: A review of personalized learning and educational technology > 3. Personalized Learning with AI
What the sources say
- Not checked yetcites Anis, 2023
- Not checked yetcites Khonturaev, 2023
- Not checked yetcites Tapalova & Zhiyenbayeva, 2022
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“ITS adapt to individual learning styles, offering personalized feedback, guidance, and additional resources as needed.”
§ AI in education: A review of personalized learning and educational technology > 3. Personalized Learning with AI
What the sources say
- Not checked yetcites Akyuz, 2020
- Not checked yetcites Conati, et. al., 2021
- Not checked yetcites Kim & Kim, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“AI-driven educational technology plays a crucial role in enhancing accessibility and breaking down barriers to education.”
§ AI in education: A review of personalized learning and educational technology > 6. Impact on Educational Equity
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“AI-powered personalized learning platforms, however, offer a tailored approach to education, recognizing and addressing individual strengths, weaknesses, and preferences (Bhimdiwala, Neri & Gomez, 2021, Holstein & Doroudi, 2021).”
§ AI in education: A review of personalized learning and educational technology > 6. Impact on Educational Equity
What the sources say
- Not checked yetcites Bhimdiwala, Neri & Gomez, 2021
- Not checked yetcites Holstein & Doroudi, 2021
plaindirect- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 15
- Probably not a substantial claim. 9
None of the sources cited for this claim have been checked yet.
In the paper
“This versatility ensures that each student can access and engage with the content in a manner that aligns with their preferred learning style, fostering a more inclusive and effective educational experience (Kabudi, Pappas & Olsen, 2021, Luan & Tsai, 2021, Regan & Jesse, 2019).”
§ AI in education: A review of personalized learning and educational technology > 3. Personalized Learning with AI
What the sources say
- Not checked yetcites Kabudi, Pappas & Olsen, 2021
- Not checked yetcites Luan & Tsai, 2021
- Not checked yetcites Regan & Jesse, 2019
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“By continuously assessing individual progress, AI ensures that learners receive content that aligns with their current proficiency levels, preventing boredom or frustration and promoting optimal learning conditions (Anis, 2023, Khonturaev, 2023, Tapalova & Zhiyenbayeva, 2022).”
§ AI in education: A review of personalized learning and educational technology > 3. Personalized Learning with AI
What the sources say
- Not checked yetcites Anis, 2023
- Not checked yetcites Khonturaev, 2023
- Not checked yetcites Tapalova & Zhiyenbayeva, 2022
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“It transcends the limitations of traditional onesize-fits-all approaches, tailoring educational experiences to the unique aptitudes, learning styles, and progress trajectories of individual students (Miao, et. al., 2021, Pedro, et. al.,2019, Tan, 2023).”
§ AI in education: A review of personalized learning and educational technology > 1. Introduction
What the sources say
- Not checked yetcites Miao, et. al., 2021
- Not checked yetcites Pedro, et. al.,2019
- Not checked yetcites Tan, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The effectiveness of intelligent tutoring systems lies in their ability to address specific learning gaps, reinforce concepts, and adaptively respond to the evolving needs of each student (Akyuz, 2020, Conati, et. al., 2021, Kim & Kim, 2020).”
§ AI in education: A review of personalized learning and educational technology > 3. Personalized Learning with AI
What the sources say
- Not checked yetcites Akyuz, 2020
- Not checked yetcites Conati, et. al., 2021
- Not checked yetcites Kim & Kim, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“From revolutionizing curriculum development to enhancing assessment methods, virtual reality experiences, and administrative efficiency, AI's impact is far-reaching, shaping the future of education and preparing learners for the challenges of the 21st century.”
§ AI in education: A review of personalized learning and educational technology > 4. Educational Technology Enhanced by AI
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Rests on an assumption: AI-enhanced VR platforms offer immersive learning environments that enhance retention and cater to various learning styles.
- Rests on an assumption: AI in administrative functions reduces workload, minimizes errors, and enhances overall operational efficiency.
None of the sources cited for this claim have been checked yet.
In the paper
“AI-powered assessment tools provide timely and actionable feedback, allowing educators to tailor their instructional strategies and address specific learning gaps (Babitha, Sushma & Gudivada, 2022, Gardner, O'Leary & Yuan, 2021, Hassoun, et. al.,2023).”
§ AI in education: A review of personalized learning and educational technology > 4. Educational Technology Enhanced by AI
What the sources say
- Not checked yetcites Babitha, Sushma & Gudivada, 2022
- Not checked yetcites Gardner, O'Leary & Yuan, 2021
- Not checked yetcites Hassoun, et. al.,2023
plaindirect- Asserted: passes — The authors put it forward as true.
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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 adaptability is particularly impactful in reducing achievement gaps and fostering a more equitable educational landscape.”
§ AI in education: A review of personalized learning and educational technology > 6. Impact on Educational Equity
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. 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
“By embracing the principles of flexibility, adaptability, and student agency, AIdriven personalized learning not only enhances academic outcomes but also addresses the diverse needs of learners.”
§ AI in education: A review of personalized learning and educational technology > 3. Personalized Learning with AI
plaindirect- 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.
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It may state two separate claims as one.
- Rests on an assumption: Personalized learning experiences lead to increased student engagement, motivation, and a deeper understanding of the material.
- Rests on an assumption: AI algorithms that adaptively deliver content contribute to improved retention and comprehension, ultimately translating into enhanced academic outcomes.
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
“Educational technology enhanced by AI, from adaptive content delivery to virtual reality applications, has contributed to creating more engaging and accessible learning environments.”
§ AI in education: A review of personalized learning and educational technology > 8. Recommendation
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: VR and AR immersive technologies enhance learning by providing interactive and three-dimensional experiences.
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
“In conclusion, the integration of AI in education brings forth a transformative wave of opportunities and challenges.Privacy concerns, algorithmic biases, the digital divide, and the need for ethical guidelines underscore the importance of approaching AI implementation with careful consideration.”
§ AI in education: A review of personalized learning and educational technology > 5. Challenges and Ethical Considerations
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“The ability of AI to recognize and accommodate individual needs fosters a sense of belonging, promoting an inclusive educational experience that empowers all learners (Knox, Wang & Gallagher, 2019, Salas-Pilco, Xiao & Oshima, 2022).”
§ AI in education: A review of personalized learning and educational technology > 6. Impact on Educational Equity
What the sources say
- Not checked yetcites Knox, Wang & Gallagher, 2019
- Not checked yetcites Salas-Pilco, Xiao & Oshima, 2022
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“Personalized learning, driven by AI algorithms, has shown remarkable effectiveness in addressing diverse learning styles, reducing educational disparities, and fostering improved academic outcomes.”
§ AI in education: A review of personalized learning and educational technology > 8. Recommendation
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no 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
“As technology continues to advance, the synergy between AI and educational technology has the potential to create a more adaptive, personalized, and engaging learning environment.”
§ AI in education: A review of personalized learning and educational technology > 4. Educational Technology Enhanced by AI
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- 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: AI-enhanced VR platforms offer immersive learning environments that enhance retention and cater to various learning styles.
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
“The findings suggest that responsible AI integration has the potential to bridge educational gaps, cater to individual needs, and enhance the overall quality of education.”
§ AI in education: A review of personalized learning and educational technology > 8. Recommendation
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Machine learning algorithms analyze student performance data, identifying strengths, weaknesses, and patterns of understanding.”
§ AI in education: A review of personalized learning and educational technology > 4. Educational Technology Enhanced by AI
What the sources say
- Not checked yetcites Babitha, Sushma & Gudivada, 2022
- Not checked yetcites Gardner, O'Leary & Yuan, 2021
- Not checked yetcites Hassoun, et. al.,2023
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“As AI systems collect and analyze vast amounts of student data, including performance metrics, learning patterns, and behavioral data, the potential for privacy breaches becomes a critical issue.”
§ AI in education: A review of personalized learning and educational technology > 5. Challenges and Ethical Considerations
What the sources say
- Not checked yetcites Butt, et. al., 2022
- Not checked yetcites Khosravi, et. al., 2022
- Not checked yetcites Malhotra, et. al., 2021
plainconditional- Asserted: passes — The authors put it forward as true.
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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
“However, the integration of AI in education is not without challenges, including privacy concerns, algorithmic biases, and the digital divide, highlighting the need for responsible and ethical practices.”
§ AI in education: A review of personalized learning and educational technology > 8. Recommendation
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Chatbots can be integrated into learning platforms, providing students with on-demand assistance, fostering a more interactive and engaging learning experience (Muniasamy, & Alasiry, 2020, Van der Vorst & Jelicic, 2019).”
§ AI in education: A review of personalized learning and educational technology > 7. Future Directions and Innovations
What the sources say
- Not checked yetcites Muniasamy, & Alasiry, 2020
- Not checked yetcites Van der Vorst & Jelicic, 2019
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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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
“AI's role in fostering inclusive learning environments goes beyond adapting content; it extends to creating a culture of diversity, equity, and inclusion.”
§ AI in education: A review of personalized learning and educational technology > 6. Impact on Educational Equity
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Personalized learning experiences lead to increased student engagement, motivation, and a deeper understanding of the material.
“Research indicates that personalized learning experiences lead to increased student engagement, motivation, and a deeper understanding of the material.”
The paper relies on this without support.
AI algorithms that adaptively deliver content contribute to improved retention and comprehension, ultimately translating into enhanced academic outcomes.
“AI algorithms that adaptively deliver content contribute to improved retention and comprehension, ultimately translating into enhanced academic outcomes.”
The paper relies on this without support.
Real-time feedback mechanisms powered by AI contribute significantly to the enhancement of learning outcomes.
“In the realm of personalized learning, real-time feedback mechanisms powered by AI contribute significantly to the enhancement of learning outcomes.”
The paper relies on this without support.
AI-enhanced VR platforms offer immersive learning environments that enhance retention and cater to various learning styles.
“AI-enhanced VR platforms offer immersive learning environments, allowing students to explore concepts in a three-dimensional space.”
“This not only enhances retention but also caters to various learning styles, making complex subjects more accessible.”
The paper relies on this without support.
AI in administrative functions reduces workload, minimizes errors, and enhances overall operational efficiency.
“The implementation of AI in administrative functions reduces workload, minimizes errors, and enhances overall operational efficiency.”
The paper relies on this without support.
Historically, students from marginalized communities, remote areas, or with physical disabilities have faced challenges in accessing quality education.
“Historically, students from marginalized communities, remote areas, or with physical disabilities have faced challenges in accessing quality education.”
The paper relies on this without support.
AI algorithms can analyze and adapt content to cater to different learning styles, making educational materials more accessible to a broader range of students.
“For example, AI algorithms can analyze and adapt content to cater to different learning styles, making educational materials more accessible to a broader range of students.”
The paper relies on this without support.
AI-enhanced tools such as speech-to-text and text-to-speech applications assist students with diverse needs, ensuring educational resources are available and tailored to individual requirements.
“Furthermore, AI-enhanced tools, such as speech-to-text and text-to-speech applications, assist students with diverse needs, ensuring that educational resources are not only available but also tailored to individual requirements.”
The paper relies on this without support.
AI technologies can facilitate the development of learning materials that incorporate diverse perspectives, backgrounds, and cultural contexts.
“AI technologies can facilitate the development of learning materials that incorporate diverse perspectives, backgrounds, and cultural contexts.”
The paper relies on this without support.
AI can assist educators in identifying and addressing implicit biases in teaching materials and methodologies.
“Additionally, AI can assist educators in identifying and addressing implicit biases in teaching materials and methodologies.”
The paper relies on this without support.
VR and AR immersive technologies enhance learning by providing interactive and three-dimensional experiences.
“These immersive technologies enhance learning by providing interactive and three-dimensional experiences.”
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?
In the citing paper
“This reflects a paradigm shift rather than a temporary spike, reinforcing findings from recent reviews that describe AI as an increasingly integral component of pedagogical design, personalized learning, and assessment strategies [11,32,42].”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [32]
In the citing 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].”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [32]
Citations
Cites
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133
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
Bias in data‐driven artificial intelligence systems—An introductory survey
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1356 · Cited by 1,043
AI and education: guidance for policy-makers
UNESCO (2021) · doi:10.54675/pcsp7350 · Cited by 929
Artificial intelligence in education : challenges and opportunities for sustainable development
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019) · MINISTERIO DE EDUCACIÓN · Cited by 902
Artificial Intelligence in Education: AIEd for Personalised Learning Pathways
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Explainable Artificial Intelligence in education
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100074 · Cited by 863
Managing the Strategic Transformation of Higher Education through Artificial Intelligence
GEORGE, B. P., & Wooden, O. S. (2023) · Administrative Sciences · doi:10.3390/admsci13090196 · Cited by 791
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023) · Sustainability · doi:10.3390/su151712983 · Cited by 784
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Explainable Artificial Intelligence in Education
Kayyali, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9675-0.ch002
Conag, M. V. (2026) · International Journal of Sustainable and Integrated Studies · doi:10.65339/ijsis.v1.i1.15 · Cited by 1
Design and Implementation of an Auxiliary Teaching System for College Chemistry based on DFN-Net
Cui, J. (2026) · International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) · doi:10.1109/icdcece68961.2026.11645655
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TD-GAE: A multimodal large language model framework for adaptive and explainable guided art learning
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MindBoost: An AI-Powered Personalized Study Aid and Focus Management Application
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Metadata
- Authors
- Ayeni, O. O., Hamad, N. M. A., Chisom, O. N., Osawaru, B., & Adewusi, O. E.
- Affiliations
- Lead City University (NG)
- National Examinations Council (NG)
- Corresponding author
- Oyebola Olusola Ayeni
- Venue
- GSC Advanced Research and Reviews
- Publisher
- GSC Online Press
- Published
- 2024-02-28
- Volume
- 18
- Issue
- 2
- Pages
- 261-271
- ISSN
- 2582-4597 (ISSN-L)
- OpenAlex
- W4392772072
- Cited by
- 436
- References
- 55
- Citations by year
- 2026: 149 · 2025: 221 · 2024: 66
- FWCI
- 163.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- GSC Advanced Research and Reviews (publishedVersion, cc-by-nc-sa)
- 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, personalized learning, educational technology, adaptive learning, intelligent tutoring systems, real-time feedback, student engagement, curriculum development, automated assessment, administrative automation, algorithmic bias, student privacy, digital divide, ethical AI, 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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