The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Personalized education and Artificial Intelligence in the United States, China, and India: A systematic review using a Human-In-The-Loop model
Open accessAbstract
The traditional “one size fits all” education system has been largely criticized in recent years on the ground of its lacking the capacity to meet individual student needs. Global education systems are leaning towards a more personalized, student-centered approach. Innovations like Big Data, Machine Learning, and Artificial Intelligence (AI) have given the modern-day technology to accommodate the distinctive features of human beings - smart machines and computers have been built to understand individual-specific needs. This opens an avenue for “personalization” in the education sector. From, mushrooming of Education Technology (EdTech) start-ups to government funding in AI research, it is evident that the next generation educational reforms would take a quantum leap forward piloted by Big Data analysis and AI. The objective of this paper is to organize the vast literature on the use of AI for personalization of education and to shed light on the key themes by which an AI-driven approach makes structural modifications to the existing education system. To this effect, the paper employed a systematic review using a Human-In-The-Loop natural language processing model of past two years' literature (2019–2021) in English language from IEEE Xplore on countries China, India and the USA. This process yielded more than 2000 search results at first and these were eventually shortlisted to 353 relevant papers for in-depth analysis. Being the pioneers in EdTech innovations, insights from research done in these three countries provides valuable input for the development of global education systems and research. The findings bring forward AI's success in catering to specific learning requirements, learning habits, and learning abilities of students and guiding them into optimized learning paths across all three countries. Not just that, it is also evident from the literature that AI augments educational content, customizes it for any individual according to their needs, and raises the flag of caution for anticipated learning difficulties. This recalibrates the role of instructors as well as optimizes the teaching-learning environment for a better learning experience. The upward trajectory of educational development with AI opens a new horizon of personalized education for the future generation, but also comes with its challenges. Data privacy issues, availability of digital resources, and affordability constraints have been reported in the recent literature as impediments in the way of promoting such technologies for day-to-day practice.
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“The use of personalised learning approaches leads to improved student engagement, better understanding of the material, and enhanced academic performance [26, 30–33].”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [33]
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“The use of AI can make tutoring more efficient by providing personalized content that matches the learner's ability (Bhutoria, 2022).”
From The Future of Artificial Intelligence in Special Education Technology (2023) · cites this paper as Bhutoria, 2022
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Cites
AI technologies for education: Recent research & future directions
Zhang, K., & Aslan, A. B. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100025 · Cited by 923
Skinner, B. F. (1958) · Science · doi:10.1126/science.128.3330.969 · Cited by 777
Fryer, L. K., Ainley, M., Thompson, A., Gibson, A., & Sherlock, Z. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.05.045 · Cited by 448
Chatbot for university related FAQs
Ranoliya, B. R., Raghuwanshi, N., & Singh, S. (2017) · 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2017.8126057 · Cited by 425
2019 Chinese Control And Decision Conference (CCDC)
(2019) · doi:10.1109/ccdc46241.2019 · Cited by 226
Hwang, G.-J., Sung, H.-Y., Chang, S.-C., & Huang, X.-C. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100003 · Cited by 183
Booth, A. (2016) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.13140/rg.2.1.2318.0562 · Cited by 152
Intelligent agent supported personalization for virtual learning environments
Dongming Xu, A., & Wang, H. (2005) · Decision Support Systems · doi:10.1016/j.dss.2005.05.033 · Cited by 133
Data-Driven Personalization of Student Learning Support in Higher Education
Liu, D. Y.-T., Bartimote-Aufflick, K., Pardo, A., & Bridgeman, A. J. (2017) · Studies in systems, decision and control · doi:10.1007/978-3-319-52977-6_5 · Cited by 128
Complexity of ICT in education: A critical literature review and its implications
Fu, J. S. (2013) · The International Journal of Education and Development using Information and Communication Technology (The University of the West Indies) · Cited by 117
Research works printed but not linked: 1
- Popay, 2006, Guidance on the conduct of narrative synthesis in systematic reviews, A product from the ESRC methods programme Version, 1, b92
Cited by
Sorted by citations
New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023) · Sustainability · doi:10.3390/su151612451 · Cited by 1,089
Alahi, M. E. E., Sukkuea, A., Tina, F. W., Nag, A., Kurdthongmee, W., Suwannarat, K., & Mukhopadhyay, S. C. (2023) · Sensors · doi:10.3390/s23115206 · Cited by 740
“Bhutoria [163] conducted a literature review on the application of AI in personalized education and identified key themes that suggest structural modifications to the current education system.”
BackgroundMethodYusuf, A., Pervin, N., & Román-González, M. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00453-6 · Cited by 554
“…(AI) tools in HE (e.g., Chu et al., 2022; Crompton & Burke, 2023) owing to their potential benefits, including improved personalized learning (Bhutoria, 2022), automation of repetitive tasks (de la Torre-López et al., 2023), and provision of efficient administrative processes (Parycek et…”
BackgroundMarzuki, Widiati, U., Rusdin, D., Darwin, & Indrawati, I. (2023) · Cogent Education · doi:10.1080/2331186x.2023.2236469 · Cited by 524
Sallam, M., Salim, N., Barakat, M., & Al-Tammemi, A. (2023) · Narra J · doi:10.52225/narra.v3i1.103 · Cited by 331
“Such an approach can help in achieving a holistic learning experience, with an opportunity for interactive tools that can be helpful to improve the achievement of intended learning outcomes [39].”
Background
Metadata
- Authors
- Bhutoria, A.
- Affiliations
- Indian Institute of Management Calcutta (IN)
- Corresponding author
- Bhutoria, Aditi
- ORCID
- Bhutoria, Aditi
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 3
- Pages
- 100068
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4224912036
- Cited by
- 677
- References
- 38
- Citations by year
- 2026: 128 · 2025: 251 · 2024: 210 · 2023: 74 · 2022: 14
- FWCI
- 17.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Educational Technology and Pedagogy
- Keywords
- personalized education, artificial intelligence in education, educational technology, adaptive learning paths, teacher role, data privacy, digital divide, educational reform, United States, China, India
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