Application and theory gaps during the rise of artificial intelligence in education
Open accessAbstract
Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the absence of a comprehensive review on it, this research aims to conduct a comprehensive and systematic review of influential AIEd studies. We analyzed 45 articles in terms of annual distribution, leading journals, institutions, countries/regions, the most frequently used terms, as well as theories and technologies adopted. We also evaluated definitions of AIEd from broad and narrow perspectives and clarified the relationship among AIEd, Educational Data Mining, Computer-Based Education, and Learning Analytics. Results indicated that: 1) there was a continuingly increasing interest in and impact of AIEd research; 2) little work had been conducted to bring deep learning technologies into educational contexts; 3) traditional AI technologies, such as natural language processing were commonly adopted in educational contexts, while more advanced techniques were rarely adopted, 4) there was a lack of studies that both employ AI technologies and engage deeply with educational theories. Findings suggested scholars to 1) seek the potential of applying AI in physical classroom settings; 2) spare efforts to recognize detailed entailment relationships between learners’ answers and the desired conceptual understanding within intelligent tutoring systems; 3) pay more attention to the adoption of advanced deep learning algorithms such as generative adversarial network and deep neural network; 4) seek the potential of NLP in promoting precision or personalized education; 5) combine biomedical detection and imaging technologies such as electroencephalogram, and target at issues regarding learners’ during the learning process; and 6) closely incorporate the application of AI technologies with educational theories.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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 increasing focus on AI in education reflects the potential of AI to not only enhance educational outcomes but also improve clinical efficiencies and patient care.
“This increasing focus reflects the potential of AI to not only enhance educational outcomes but also improve clinical efficiencies and patient care.34”
From The Impact of Artificial Intelligence-Assisted Learning on Nursing Students' Ethical Decision-making and Clinical Reasoning in Pediatric Care (2024) · cites this paper as [34]
Section 1.1 describes AI’s potential to support personalized learning and improve teaching, which supports the educational part of the claim. The paper does not say that AI improves clinical efficiency or patient care; its medical references do not establish those outcomes. (LLM, conf=0.88)
Experimental — accuracy not yet measuredArtificial intelligence is increasingly used in professional contexts such as education, healthcare delivery, and marketing.
“Artificial intelligence (AI) is permeating more and more areas of daily life, and is increasingly being used in professional context such as education (Chen et al., 2020;Hwang et al., 2020), healthcare delivery (Matheny, Whicher, & Israni, 2020;Noorbakhsh-Sabet et al., 2019;Maddox, Rumsfeld, & Payne, 2019), or marketing (Vlačić, Corbo, Costa e Silva, & Dabić, 2021;Verma, Sharma, Deb, & Maitra, 202”
From Artificial intelligence literacy in higher and adult education: A scoping literature review (2022) · cites this paper as Chen et al., 2020
Many AI-Edtech have a weak connection to theoretical pedagogical perspectives.
“An existing limitation with many AI-Edtech is that they have been shown to have a weak connection to theoretical pedagogical perspectives Chen, Xie, Zou, and Hwang (2020); Zawacki-Richter, Marín, Bond, and Gouverneur (2019).”
From Impact of AI assistance on student agency (2024) · cites this paper as Chen, Xie, Zou, and Hwang (2020)
Chen et al. and Liao et al. used TAM to assess educators' acceptance of AI-driven educational tools, emphasizing the role of perceived usefulness in shaping positive attitudes and perceptions.
“Chen et al. [11] and Liao et al. [28] used TAM to assess educators' acceptance of AI-driven educational tools, emphasizing the role of perceived usefulness in shaping positive attitudes and perceptions.”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859 · see citing works
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554 · see citing works
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,182 · see citing works
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,726 · see citing works
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works
(2012) · Cited by 9,062 · see citing works
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694 · see citing works
Plans and Situated Actions: The Problem of Human-Machine Communication
Kantowitz, B. H., & Suchman, L. A. (1990) · The American Journal of Psychology · doi:10.2307/1423221 · Cited by 5,306 · see citing works
Some Studies in Machine Learning Using the Game of Checkers
Samuel, A. L. (1959) · IBM Journal of Research and Development · doi:10.1147/rd.33.0210 · Cited by 4,443 · see citing works
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215 · see citing works
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480 · see citing works
Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140 · see citing works
Educational Data Mining: A review of the state of the art
Romero, C., & Ventura, S. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2053532 · Cited by 2,251 · see citing works
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,785 · see citing works
Plans and Situated Actions: The Problem of Human Machine Communication.
Woolgar, S., & Suchman, L. A. (1989) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2073874 · Cited by 1,600 · see citing works
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557 · see citing works
Educational data mining: A survey from 1995 to 2005
Romero, C., & Ventura, S. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.04.005 · Cited by 1,490 · see citing works
(2016) · Open Research Online (The Open University) · Cited by 1,410 · see citing works
The State of Educational Data Mining in 2009: A Review and Future Visions
Baker, R. S., & Yacef, K. (2009) · Journal of Educational Data Mining · doi:10.5281/zenodo.3554658 · Cited by 1,296 · see citing works
Evolution and Revolution in Artificial Intelligence in Education
Roll, I., & Wylie, R. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-016-0110-3 · Cited by 1,229 · see citing works
What can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131 · see citing works
Intelligent Tutoring Goes To School in the Big City
Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (2018) · doi:10.1184/r1/6470153 · Cited by 1,105 · see citing works
Detecting rumors from microblogs with recurrent neural networks
Ma, J., Gao, W. G., Mitra, P., Kwon, S., Jansen, B. J. J., Wong, K., & Cha, M. (2016) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 1,102 · see citing works
Learning analytics and educational data mining
Siemens, G., & Baker, R. S. (2012) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2330601.2330661 · Cited by 1,091 · see citing works
Data mining in course management systems: Moodle case study and tutorial
Romero, C., Ventura, S., & Salcines, E. G. (2007) · Computers & Education · doi:10.1016/j.compedu.2007.05.016 · Cited by 1,061 · see citing works
2014 International Conference on Computational Science
Abramson, D., Lees, M., Krzhizhanovskaya, V. V., Dongarra, J. J., & Sloot, P. M. A. (2014) · UvA-DARE (University of Amsterdam) · Cited by 1,013 · see citing works
Romero, C., & Ventura, S. (2013) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1075 · Cited by 869 · see citing works
Engaging online learners: The impact of Web-based learning technology on college student engagement
Chen, P.-S. D., Lambert, A. D., & Guidry, K. R. (2010) · Computers & Education · doi:10.1016/j.compedu.2009.11.008 · Cited by 785 · see citing works
Chan, K. S., & Zary, N. (2019) · JMIR Medical Education · doi:10.2196/13930 · Cited by 765 · see citing works
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., & Shen, D. G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66179-7_48 · Cited by 751 · see citing works
The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions
Whitehill, J., Serpell, Z. N., Lin, Y.-C., Foster, A., & Movellan, J. R. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2316163 · Cited by 683 · see citing works
Heffernan, N. T., & Heffernan, C. L. (2014) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-014-0024-x · Cited by 616 · see citing works
25years at Knowledge-Based Systems: A bibliometric analysis
Cobo, M. J., Martínez, M. Á., Gutiérrez-Salcedo, M., Fujita, H., & Herrera‐Viedma, E. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.12.035 · Cited by 581 · see citing works
Virtual and remote labs in education: A bibliometric analysis
Heradio, R., de la Torre, L., Galán, D., Cabrerizo, F. J., Herrera‐Viedma, E., & Dormido, S. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.03.010 · Cited by 576 · see citing works
LEARNING BY TEACHING: A NEW AGENT PARADIGM FOR EDUCATIONAL SOFTWARE
Biswas, G., Leelawong, K., Schwartz, D. L., Vye, N. J., & The Teachable Agents Group at Vande (2005) · Applied Artificial Intelligence · doi:10.1080/08839510590910200 · Cited by 463 · see citing works
Kasabov, N. K. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.01.006 · Cited by 452 · see citing works
Evaluating Bayesian networks’ precision for detecting students’ learning styles
García, P., Amandi, A., Schiaffino, S., & Campo, M. (2005) · Computers & Education · doi:10.1016/j.compedu.2005.11.017 · Cited by 445 · see citing works
Arıcı, F., Yıldırım, P., Çaliklar, Ş., & Yılmaz, R. M. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103647 · Cited by 442 · see citing works
The production and recognition of emotions in speech: features and algorithms
Pierre-Yves, O. (2003) · International Journal of Human-Computer Studies · doi:10.1016/s1071-5819(02)00141-6 · Cited by 439 · see citing works
Teachable robots: Understanding human teaching behavior to build more effective robot learners
Thomaz, A. L., & Breazeal, C. (2007) · Artificial Intelligence · doi:10.1016/j.artint.2007.09.009 · Cited by 408 · see citing works
Chen, X., Zou, D., Cheng, G., & Xie, H. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103855 · Cited by 406 · see citing works
Self-citationDropout prediction in e-learning courses through the combination of machine learning techniques
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. G. (2009) · Computers & Education · doi:10.1016/j.compedu.2009.05.010 · Cited by 401 · see citing works
Hinojo-Lucena, F.-J., Aznar-Díaz, I., Cáceres-Reche, M.-P., & Romero-Rodríguez, J.-M. (2019) · Education Sciences · doi:10.3390/educsci9010051 · Cited by 400 · see citing works
A Bibliometric Review of Research on Higher Education for Sustainable Development, 1998–2018
Hallinger, P., & Chatpinyakoop, C. (2019) · Sustainability · doi:10.3390/su11082401 · Cited by 394 · see citing works
The Effects of Discipline on Deep Approaches to Student Learning and College Outcomes
Laird, T. F. N., Shoup, R., Kuh, G. D., & Schwarz, M. J. (2008) · Research in Higher Education · doi:10.1007/s11162-008-9088-5 · Cited by 387 · see citing works
Lowry, P. B., Curtis, A., & Lowry, M. R. (2004) · Journal of Business Communication · doi:10.1177/0021943603259363 · Cited by 365 · see citing works
Understanding Bibliometric Parameters and Analysis
Choudhri, A. F., Siddiqui, A., Khan, N. R., & Cohen, H. L. (2015) · Radiographics · doi:10.1148/rg.2015140036 · Cited by 353 · see citing works
A comparative analysis of machine learning techniques for student retention management
Delen, D. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.06.003 · Cited by 334 · see citing works
Towards Biologically Plausible Deep LearningPreprint
Bengio, Y., Lee, D.-H., Bornschein, J., Mesnard, T., & Lin, Z. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.04156 · Cited by 295 · see citing works
On the origins of bibliometrics
Godin, B. (2006) · Scientometrics · doi:10.1007/s11192-006-0086-0 · Cited by 290 · see citing works
Koedinger, K. R., & Corbett, A. T. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816833.006 · Cited by 284 · see citing works
Citation Classics in Anesthetic Journals
Baltussen, A., & Kindler, C. H. (2004) · Anesthesia & Analgesia · doi:10.1213/01.ane.0000096185.13474.0a · Cited by 261 · see citing works
Academic Analytics and Data Mining in Higher Education
Baepler, P., & Murdoch, C. (2010) · International Journal for the Scholarship of Teaching and Learning · doi:10.20429/ijsotl.2010.040217 · Cited by 258 · see citing works
Eguchi, A. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2015.05.013 · Cited by 252 · see citing works
Wang, F. H. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.06.012 · Cited by 252 · see citing works
Exploring four decades of research in Computers & Education
Zawacki‐Richter, O., & Latchem, C. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.04.001 · Cited by 250 · see citing works
Kotsiantis, S. B. (2011) · Artificial Intelligence Review · doi:10.1007/s10462-011-9234-x · Cited by 247 · see citing works
The use of gamification in education: a bibliometric and text mining analysis
Martí-Parreño, J., Méndez-Ibáñez, E., & Alonso-Arroyo, A. (2016) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12161 · Cited by 247 · see citing works
Exploring Two Decades of Research on Classroom Dialogue by Using Bibliometric Analysis
Song, Y., Chen, X., Hao, T., Liu, Z., & Lan, Z. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.04.002 · Cited by 245 · see citing works
Self-citationAutomated essay evaluation: the criterion online writing service
Burstein, J. C., Chodorow, M. S., & Leacock, C. (2004) · doi:10.1609/aimag.v25i3.1774 · Cited by 240 · see citing works
The most frequently cited adsorption research articles in the Science Citation Index (Expanded)
Fu, H., Wang, M.-H., & Ho, Y. (2012) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2012.04.051 · Cited by 239 · see citing works
Preventing Student Dropout in Distance Learning Using Machine Learning Techniques
Kotsiantis, S. B., Pierrakeas, C. J., & Pintelas, P. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45226-3_37 · Cited by 228 · see citing works
Handhelds go to school: lessons learned
Tatar, D. G., Roschelle, J., Vahey, P., & Penuel, W. R. (2003) · Computer · doi:10.1109/mc.2003.1231192 · Cited by 224 · see citing works
Evaluating research: from informed peer review to bibliometrics
Abramo, G., & D’Angelo, C. A. (2011) · Scientometrics · doi:10.1007/s11192-011-0352-7 · Cited by 220 · see citing works
Teacherbot: interventions in automated teaching
Bayne, S. (2015) · Teaching in Higher Education · doi:10.1080/13562517.2015.1020783 · Cited by 215 · see citing works
Utilizing early engagement and machine learning to predict student outcomes
Gray, C., & Perkins, D. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.12.006 · Cited by 215 · see citing works
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Cattolico, M., USA, G. G. A. E. C. C. 2. S. W., Association for Computing Machinery-Digital Library, et al. (2006) · doi:10.1145/1143997 · Cited by 208 · see citing works
Learning while searching in constraint-satisfaction-problems
Dechter, R. (1986) · Cited by 206 · see citing works
Kotsiantis, S. B., Patriarcheas, K., & Xenos, M. (2010) · Knowledge-Based Systems · doi:10.1016/j.knosys.2010.03.010 · Cited by 200 · see citing works
Cukurova, M., Kent, C., & Luckin, R. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12829 · Cited by 198 · see citing works
Ninaus, M., Greipl, S., Kiili, K. J. M., Lindstedt, A., Huber, S., Klein, E., Karnath, H., & Moeller, K. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103641 · Cited by 189 · see citing works
A bibliometric review: Energy consumption and greenhouse gas emissions in the residential sector
Geng, Y., Chen, W., Liu, Z., Chiu, A. S. F., Han, W., Liu, Z., Zhong, S., Qian, Y., You, W., & Cui, X. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.05.091 · Cited by 176 · see citing works
A Kendama Learning Robot Based on Bi-directional Theory
Miyamoto, H., Schaal, S., Gandolfo, F., Gomi, H., Koike, Y., Osu, R., Nakano, E., Wada, Y., & Kawato, M. (1996) · Neural Networks · doi:10.1016/s0893-6080(96)00043-3 · Cited by 171 · see citing works
Multi-Valued and Universal Binary Neurons
Aizenberg, I. N., Aizenberg, N. N., & Vandewalle, J. (2000) · doi:10.1007/978-1-4757-3115-6 · Cited by 171 · see citing works
Chen, X., Zou, D., & Xie, H. (2020) · British Journal of Educational Technology · doi:10.1111/bjet.12907 · Cited by 168 · see citing works
Self-citationDeepQA: improving the estimation of single protein model quality with deep belief networks
Cao, R., Bhattacharya, D., Hou, J., & Cheng, J. J. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1405-y · Cited by 157 · see citing works
How deep is knowledge tracing?Preprint
Khajah, M. M., Lindsey, R., & Mozer, M. C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.02416 · Cited by 155 · see citing works
Liao, H., Tang, M., Li, Z., & Lev, B. (2018) · Omega · doi:10.1016/j.omega.2018.11.005 · Cited by 150 · see citing works
Pliakos, K., Joo, S.-H., Park, J. Y., Cornillie, F., Vens, C., & Van den Noortgate, W. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.04.009 · Cited by 149 · see citing works
Chuang, K., Wang, M.-H., & Ho, Y. (2011) · Scientometrics · doi:10.1007/s11192-011-0365-2 · Cited by 148 · see citing works
Collaborative Writing Support Tools on the Cloud
Calvo, R. A., O'Rourke, S. T., Jones, J., Yacef, K., & Reimann, P. (2010) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2010.43 · Cited by 147 · see citing works
A bibliometric analysis of natural language processing in medical research
Chen, X., Xie, H., Wang, F. L., Liu, Z., Xu, J., & Hao, T. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0594-x · Cited by 139 · see citing works
Self-citationChi, M., VanLehn, K. A., Litman, D. J., & Jordan, P. (2011) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-010-9093-1 · Cited by 135 · see citing works
Sparse factor analysis for learning and content analytics
Lan, A., Waters, A. E., Studer, C., et al. (2014) · doi:10.5555/2627435.2670314 · Cited by 134 · see citing works
Trends of mobile learning: A review of the top 100 highly cited papers
Lai, C. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12884 · Cited by 134 · see citing works
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 · see citing works
Some useful tactics to modify, map and mine data from intelligent tutors
Mostow, J., & Beck, J. E. (2006) · Natural Language Engineering · doi:10.1017/s1351324906004153 · Cited by 132 · see citing works
Comparison of Machine Learning Methods for Intelligent Tutoring Systems
Hämäläinen, W., & Vinni, M. (2006) · Lecture notes in computer science · doi:10.1007/11774303_52 · Cited by 122 · see citing works
A computational model for developing semantic web-based educational systems
Bittencourt, I. I., Costa, E. D. B., Silva, M., et al. (2009) · Knowledge-Based Systems · doi:10.1016/j.knosys.2009.02.012 · Cited by 120 · see citing works
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works
Parallel computing method of deep belief networks and its application to traffic flow prediction
Zhao, L., Zhou, Y., Lu, H., et al. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.10.025 · Cited by 113 · see citing works
Levitt, J. M., & Thelwall, M. A. (2008) · Scientometrics · doi:10.1007/s11192-007-1927-1 · Cited by 112 · see citing works
Chen, X., Yu, G., Cheng, G., & Hao, T. (2019) · Journal of Computers in Education · doi:10.1007/s40692-019-00149-1 · Cited by 103 · see citing works
Self-citationTran, B. X., Latkin, C. A., Vu, G. T., Nguyen, H. L. T., Nghiem, S. H., Tan, M.-X., Lim, Z. K., Ho, C. S. H., & Ho, R. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16152699 · Cited by 101 · see citing works
COMPARISON OF UNIVERSITIES’ SCIENTIFIC PERFORMANCE USING BIBLIOMETRIC INDICATORS
Wang, M.-H., Hui, Z. F., & Ho, Y. (2011) · Malaysian Journal of Library & Information Science · Cited by 101 · see citing works
Optimization and Learning in Neural Networks for Formation and Control of Coordinated Movement
Kawato, M. (1993) · The MIT Press eBooks · doi:10.7551/mitpress/1477.003.0047 · Cited by 100 · see citing works
The 100 Most Frequently Cited Articles in Ophthalmology Journals
Ohba, N. (2007) · Archives of Ophthalmology · doi:10.1001/archopht.125.7.952 · Cited by 100 · see citing works
Building Domain Ontologies from Text for Educational Purposes
Zouaq, A., & Nkambou, R. (2008) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2008.12 · Cited by 100 · see citing works
Recognizing entailment in intelligent tutoring systems
Nielsen, R. D., Ward, W., & Martin, J. (2009) · Natural Language Engineering · doi:10.1017/s135132490999012x · Cited by 99 · see citing works
The evaluation of Learning Management Systems using an artificial intelligence fuzzy logic algorithm
Çavuş, N. (2009) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2009.07.009 · Cited by 99 · see citing works
Chen, S., & Wu, S. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01695-4 · Cited by 96 · see citing works
Applying evolutionary computation to the school timetabling problem: The Greek case
Beligiannis, G. N., Moschopoulos, C. N., Kaperonis, G. P., et al. (2006) · Computers & Operations Research · doi:10.1016/j.cor.2006.08.010 · Cited by 95 · see citing works
An analysis of students’ gaming behaviors in an intelligent tutoring system: predictors and impacts
Müldner, K., Burleson, W., van de Sande, B., & VanLehn, K. A. (2011) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-010-9086-0 · Cited by 95 · see citing works
SmartTutor: An intelligent tutoring system in web-based adult education
Cheung, B., Hui, L. C. K., Zhang, J., & Yiu, S. (2003) · Journal of Systems and Software · doi:10.1016/s0164-1212(02)00133-4 · Cited by 90 · see citing works
A neural fuzzy system with linguistic teaching signals
Lin, C., & Lu, Y.-C. (1995) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.388172 · Cited by 90 · see citing works
Cetintas, S., Si, L., Xin, Y. P., & Hord, C. (2009) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2009.44 · Cited by 90 · see citing works
Ivanović, L., & Ho, Y. (2017) · Educational Review · doi:10.1080/00131911.2017.1415297 · Cited by 86 · see citing works
Artificial Intelligence and Its Implications in Higher Education.
Ocaña-Fernández, Y., Valenzuela-Fernández, L. A., & Garro-Aburto, L. L. (2019) · Cited by 82 · see citing works
A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification
Wei, X., Lin, H., Yang, L., & Yu, Y. (2017) · Information · doi:10.3390/info8030092 · Cited by 81 · see citing works
Automatic Student Plagiarism Detection: Future Perspectives
Mozgovoy, M., Kakkonen, T., & Cosma, G. (2010) · Journal of Educational Computing Research · doi:10.2190/ec.43.4.e · Cited by 78 · see citing works
Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results
Merceron, A., & Yacef, K. (2004) · The Journal of Interactive Learning Research · Cited by 77 · see citing works
A system for formative assessment and monitoring of students' progress
Rodrigues, F., & Oliveira, P. (2014) · Computers & Education · doi:10.1016/j.compedu.2014.03.001 · Cited by 71 · see citing works
Freebairn, L., Occhipinti, J., Kelly, P., McDonnell, G., & Rychetnik, L. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0707-6 · Cited by 70 · see citing works
Romero, C., Zafra, A., Luna, J. M., & Ventura, S. (2012) · Expert Systems · doi:10.1111/j.1468-0394.2012.00627.x · Cited by 69 · see citing works
Co-evolving recurrent neurons learn deep memory POMDPs
Gomez, F. J., & Schmidhuber, J. (2005) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1068009.1068092 · Cited by 69 · see citing works
Botrel, L., Holz, E. M., & Kübler, A. (2015) · Brain-Computer Interfaces · doi:10.1080/2326263x.2015.1100038 · Cited by 68 · see citing works
Sullivan, F. R., & Keith, P. K. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12875 · Cited by 65 · see citing works
An enhanced Bayesian model to detect students’ learning styles in Web‐based courses
García, P. S., Schiaffino, S., & Amandi, A. (2007) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2007.00262.x · Cited by 61 · see citing works
Moridis, C. N., & Economides, A. A. (2009) · Computers & Education · doi:10.1016/j.compedu.2009.04.002 · Cited by 59 · see citing works
Luna, J. M., Romero, C., Romero, J. R., & Ventura, S. (2014) · Applied Intelligence · doi:10.1007/s10489-014-0603-4 · Cited by 59 · see citing works
Supporting teachers in adaptive educational systems through predictive models: A proof of concept
Gaudioso, E., Montero, M. A., & Hernández‐del‐Olmo, F. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.07.052 · Cited by 57 · see citing works
Opportunities for Natural Language Processing Research in Education
Burstein, J. C. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00382-0_2 · Cited by 56 · see citing works
Artificial Intelligence in Education - a Promise, a Threat or a Hype?
Humble, N., & Mozelius, P. (2019) · DiVA (University of Gävle) · doi:10.34190/eciair.19.005 · Cited by 56 · see citing works
A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems
Tsiriga, V., & Virvou, M. K. (2004) · User Modeling and User-Adapted Interaction · doi:10.1023/b:user.0000043396.14788.cc · Cited by 54 · see citing works
Machine Learning: A Constraint-Based Approach
Gori, M. (2017) · Cited by 53 · see citing works
Bywater, J., Chiu, J. L., Hong, J., & Sankaranarayanan, V. (2019) · Comput. Educ. · doi:10.1016/j.compedu.2019.05.004 · Cited by 53 · see citing works
A Two-Phase Fuzzy Mining and Learning Algorithm for Adaptive Learning Environment
Tsai, C.-J., Tseng, S. S., & Lin, C. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45718-6_47 · Cited by 51 · see citing works
Knowledge-Based Web-Enabled Agents and Intelligent Tutoring Systems
Piramuthu, S. (2005) · IEEE Transactions on Education · doi:10.1109/te.2005.854574 · Cited by 51 · see citing works
Rojas, E. M., & Mukherjee, A. (2006) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)0887-3801(2006)20:3(165 · Cited by 51 · see citing works
Deep Neural Networks and How They Apply to Sequential Education Data
Tang, S., Peterson, J. C., & Pardos, Z. A. (2016) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2876034.2893444 · Cited by 49 · see citing works
Generating Educational Game Levels with Multistep Deep Convolutional Generative Adversarial Networks
Park, K., Mott, B., Min, W., Boyer, K. E., Wiebe, E., & Lester, J. C. (2019) · IEEE Conference on Games (CoG) · doi:10.1109/cig.2019.8848085 · Cited by 49 · see citing works
Solving effectively the school timetabling problem using particle swarm optimization
Tassopoulos, I. X., & Beligiannis, G. N. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.12.013 · Cited by 47 · see citing works
Proceedings of the Third (2016) ACM Conference on Learning @ Scale
(2016) · doi:10.1145/2876034 · Cited by 47 · see citing works
Verdú, E., Verdú, M. J., Regueras, L. M., Fernández, J. P. D. C., & Garcia, R. A. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.115 · Cited by 43 · see citing works
Tan, D., Ji, S., & Jin, M. (2012) · IEEE Transactions on Education · doi:10.1109/te.2012.2212707 · Cited by 40 · see citing works
Deep learning, education and the final stage of automation.
Peters, M. A. (2018) · Educational Philosophy and Theory · doi:10.1080/00131857.2017.1348928 · Cited by 39 · see citing works
Finite element mesh design expert system
Dolšak, B. (2002) · Knowledge-Based Systems · doi:10.1016/s0950-7051(01)00168-x · Cited by 38 · see citing works
Martínez, M., & Sá, C. M. (2019) · Journal of Studies in International Education · doi:10.1177/1028315319888890 · Cited by 38 · see citing works
Bibliometric analysis of literature in pharmacy education: 2000–2016
Sweileh, W., Al-Jabi, S. W., Zyoud, S., & Sawalha, A. (2018) · International Journal of Pharmacy Practice · doi:10.1111/ijpp.12429 · Cited by 34 · see citing works
Artificial Intelligence and Education
Florea, A. M., & Radu, S. (2019) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs.2019.00069 · Cited by 32 · see citing works
Artificial Intelligence in Medical Education
Garg, T. (2020) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.08.017 · Cited by 32 · see citing works
The characteristics of highly cited researchers in Africa
Confraria, H., Blanckenberg, J. P., & Swart, C. (2018) · Research Evaluation · doi:10.1093/reseval/rvy017 · Cited by 29 · see citing works
Ventura, M. D. (2017) · ICST Transactions on e-Education and e-Learning · doi:10.4108/eai.26-7-2017.152906 · Cited by 28 · see citing works
Analysis of New Advances in the Application of Artificial Intelligence to Education
Han, L. (2018) · International Conference on Education, E-learning and Management Technology (EEMT) · doi:10.2991/iceemt-18.2018.118 · Cited by 28 · see citing works
Research on Application of Artificial Intelligence in Medical Education
Zhao, H., Li, G., & Feng, W. (2018) · doi:10.1109/esaic.2018.00085 · Cited by 26 · see citing works
Hughes‐Roberts, T., Brown, D. J., Standen, P., et al. (2018) · British Journal of Educational Technology · doi:10.1111/bjet.12722 · Cited by 26 · see citing works
Bidirectional Theory Approach to Integration
Kawato, M. (1996) · The MIT Press eBooks · doi:10.7551/mitpress/1479.003.0024 · Cited by 24 · see citing works
2019 IEEE Conference on Games (CoG)
(2019) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2887122 · Cited by 23 · see citing works
Quality assessment of library and information science school faculties
Brace, W. S. (1992) · Education for Information · doi:10.3233/efi-1992-10204 · Cited by 20 · see citing works
Twitter As a Music Education Tool to Enhance the Learning Process: Conversation Analysis
Ventura, M. D. (2018) · Educational communications and technology yearbook · doi:10.1007/978-981-10-8896-4_7 · Cited by 19 · see citing works
Kurtz, G., & Sponder, B. (2010) · International Journal for the Scholarship of Teaching and Learning · doi:10.20429/ijsotl.2010.040101 · Cited by 18 · see citing works
Arden, S., Pentimonti, J. M., Cooray, R., et al. (2017) · Journal of Learning Disabilities · doi:10.1177/0022219417720931 · Cited by 16 · see citing works
Artificial Intelligence: Past, Present and Future
Soldatenko, D. M. (2020) · RePEc: Research Papers in Economics · Cited by 14 · see citing works
The Top 100 Highly Cited Original Articles on Immunotherapy for Childhood Leukemia
Zhong, Q., Li, B., Zhu, Q.-Q., et al. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.01100 · Cited by 13 · see citing works
Highly cited publications in World War II: a bibliometric analysis
Ho, Y., & Hartley, J. R. (2016) · Scientometrics · doi:10.1007/s11192-016-2199-4 · Cited by 12 · see citing works
Karmani, P., Chandio, A. A., Korejo, I. A., & Chandio, M. S. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-6052-7_5 · Cited by 12 · see citing works
New Media for Educational Change
(2018) · Educational communications and technology yearbook · doi:10.1007/978-981-10-8896-4 · Cited by 11 · see citing works
Artificial Intelligence as a Disruptive Technology in Education
Zovko, V., & Gudlin, M. (2019) · Cited by 6 · see citing works
Applications and Innovations in Intelligent Systems IX
(2002) · Springer eBooks · doi:10.1007/978-1-4471-0149-9 · Cited by 5 · see citing works
Optimization and Learning in Neural Networks for Formation and Control of Coordinated Movement
Meyer, D. E., & Kornblum, S. (1993) · Cited by 2 · see citing works
The impact of Computers & Education measured beyond traditional bibliographical metrics
Echeverría, A., Nussbaum, M., Albers, C. J., et al. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.05.018 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- Garfield, E., & Merton, R. K. (1979). Citation indexing: Its theory and application in science, technology and humanities (Vol. 8). New York: Wiley.
- Kohavi, R., & Provost, F. (1998). Glossary of terms journal of machine learning. In Obtido.
- Mitchell, T. (1997). Machine learning. WBC/McGraw-Hill, Boston: MA.
- Natividad, G., Spector, J. M., & Evangelopoulos, N. (2018). An analysis of two decades of educational technology publications: Who, what and where. Springer.
- Poole, D. I., Goebel, R. G., & Mackworth, A. K. (1998). Computational intelligence and knowledge. New York: Oxford University Press.
Cited by
Sorted by citations
State of the art and practice in <scp>AI</scp> in education
Holmes, W., & Tuomi, I. (2022) · European Journal of Education · doi:10.1111/ejed.12533 · Cited by 1,093 · see citing works
Revolutionizing education with AI: Exploring the transformative potential of ChatGPT
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13152 · Cited by 975 · see citing works
“Although the influence of AI on education is still uncertain (Holmes, 2021), it has considerable potential to improve learning, teaching, pedagogical innovations, assessment, and educational administration (Chen et al., 2020).”
BackgroundInfluentialArtificial Intelligence in Education: The Three Paradigms
Ouyang, F., & Jiao, P. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100020 · Cited by 991 · see citing works
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 · see citing works
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 · see citing works
Ouyang, F., Zheng, L., & Jiao, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10925-9 · Cited by 831 · see citing works
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023) · Sustainability · doi:10.3390/su151712983 · Cited by 784 · see citing works
Chatbots in Education and Research: A Critical Examination of Ethical Implications and Solutions
Kooli, C. (2023) · Sustainability · doi:10.3390/su15075614 · Cited by 666 · see citing works
Artificial intelligence literacy in higher and adult education: A scoping literature review
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100101 · Cited by 658 · see citing works
Ali, O., Abdelbaki, W., Shrestha, A., Elbasi, E., Alryalat, M. A. A., & Dwivedi, Y. K. (2023) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2023.100333 · Cited by 568 · see citing works
Impact of AI assistance on student agency
Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024) · Computers & Education · doi:10.1016/j.compedu.2023.104967 · Cited by 548 · see citing works
“An existing limitation with many AI-Edtech is that they have been shown to have a weak connection to theoretical pedagogical perspectives Chen, Xie, Zou, and Hwang (2020); Zawacki-Richter, Marín, Bond, and Gouverneur (2019).”
BackgroundArtificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities
Su, J., Ng, D. T. K., & Chu, S. K. W. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100124 · Cited by 489 · see citing works
Fathi, J., Rahimi, M., & Derakhshan, A. (2024) · System · doi:10.1016/j.system.2024.103254 · Cited by 557 · see citing works
Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00420-7 · Cited by 413 · see citing works
Liang, J.-C., Hwang, G.-J., Chen, M.-R. A., & Darmawansah, D. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2021.1958348 · Cited by 378 · see citing works
Self-citationHuman-centered Artificial Intelligence in education: Seeing the invisible through the visible
Yang, S. J. H., Ogata, H., Matsui, T., & Chen, N.-S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100008 · Cited by 379 · see citing works
Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., et al. (2023) · Zenodo · doi:10.5281/zenodo.7636568 · Cited by 350 · see citing works
Towards hybrid <scp>human‐AI</scp> learning technologies
Molenaar, I. (2022) · European Journal of Education · doi:10.1111/ejed.12527 · Cited by 348 · see citing works
Artificial intelligence in early childhood education: A scoping review
Su, J., & Yang, W. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100049 · Cited by 357 · see citing works
Kohnke, L., Moorhouse, B. L., & Zou, D. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100156 · Cited by 309 · see citing works
Self-citationSaif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108097 · Cited by 301 · see citing works
Chang, C., Hwang, G., & Gau, M. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13158 · Cited by 294 · see citing works
Self-citationShahzad, M. F., Xu, S., & Zahid, H. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12949-9 · Cited by 204 · see citing works
Affordances and challenges of artificial intelligence in K-12 education: a systematic review
Crompton, H., Jones, M. V., & Burke, D. (2024) · Journal of Research on Technology in Education · doi:10.1080/15391523.2022.2121344 · Cited by 213 · see citing works
Zheng, L., Niu, J., Zhong, L., & Gyasi, J. F. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2021.2015693 · Cited by 183 · see citing works
Lin, P.-Y., Chai, C.-S., Jong, M. S.-Y., Dai, Y., Guo, Y., & Qin, J. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100006 · Cited by 171 · see citing works
Al-Abdullatif, A. M. (2024) · Education Sciences · doi:10.3390/educsci14111209 · Cited by 169 · see citing works
Retrieval-augmented generation for educational application: A systematic survey
Li, Z., Wang, Z., Wang, W., Hung, K., Xie, H., & Wang, F. L. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100417 · Cited by 151 · see citing works
Self-citationThe integration of AI in nursing: addressing current applications, challenges, and future directions
Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1545420 · Cited by 143 · see citing works
Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework
Rana, M. M., Siddiqee, M. S., Sakib, M. N., & Ahamed, M. R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e37569 · Cited by 143 · see citing works
Sallam, M., Salim, N. A., Al-Tammemi, A. B., Barakat, M., Fayyad, D., Hallit, S., Harapan, H., Hallit, R., & Mahafzah, A. (2023) · Cureus · doi:10.7759/cureus.35029 · Cited by 130 · see citing works
Li, T., Ji, Y., & Zhan, Z. (2024) · Asia Pacific Journal of Education · doi:10.1080/02188791.2024.2305163 · Cited by 133 · see citing works
Guo, L., Wang, D., Gu, F., Li, Y., Wang, Y., & Zhou, R. (2021) · Asia Pacific Education Review · doi:10.1007/s12564-021-09697-7 · Cited by 124 · see citing works
Hwang, W.-Y., Nurtantyana, R., Purba, S. W. D., Hariyanti, U., Indrihapsari, Y., & Surjono, H. D. (2023) · Journal of Educational Computing Research · doi:10.1177/07356331221137253 · Cited by 104 · see citing works
A review of machine learning methods used for educational data
Ersozlu, Z., Taheri, S., & Koch, I. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12704-0 · Cited by 96 · see citing works
“This method was widely utilised for learning analytics and investigating the attitudes and behaviours of students in group learning settings (Chen et al., 2020).”
Triola, M. M., & Rodman, A. (2025) · Academic Medicine · doi:10.1097/acm.0000000000005963 · Cited by 92 · see citing works
Jia, F., Sun, D., Ma, Q., & Looi, C.-K. (2022) · Sustainability · doi:10.3390/su142315527 · Cited by 90 · see citing works
Artificial Intelligence in Personalised Learning: A Bibliometric Analysis
Li, K. C., & Wong, B. T.-M. (2023) · Interactive Technology and Smart Education · doi:10.1108/itse-01-2023-0007 · Cited by 88 · see citing works
Hwang, G.-J., Tang, K.-Y., & Tu, Y.-F. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2022.2086579 · Cited by 87 · see citing works
Self-citationWiboolyasarin, W., Wiboolyasarin, K., Suwanwihok, K., Jinowat, N., & Muenjanchoey, R. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100228 · Cited by 68 · see citing works
Wang, D. (2024) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v25i3.7646 · Cited by 62 · see citing works
Tripathi, T., Sharma, S. R., Singh, V., Bhargava, P., & Raj, C. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1651217 · Cited by 51 · see citing works
Zheng, L., Fan, Y., Gao, L., Huang, Z., Chen, B., & Long, M. (2025) · Journal of Research on Technology in Education · doi:10.1080/15391523.2024.2304066 · Cited by 50 · see citing works
Karkoulian, S., Sayegh, N., & Sayegh, N. (2025) · Journal of Academic Ethics · doi:10.1007/s10805-024-09543-6 · Cited by 53 · see citing works
“A further gap that drove this study was the previous literature’s inadequate examination of instructors’ opinions of the obstacles and opportunities of applying ChatGPT, as well as the ways in which their understanding and practice of this method might be enhanced (Chen et al., 2020).”
Marzano, D. (2026) · Technology, Knowledge and Learning · doi:10.1007/s10758-025-09853-7 · Cited by 51 · see citing works
Shin, H., De Gagne, J. C., Kim, S. S., & Hong, M. (2024) · CIN: Computers, Informatics, Nursing · doi:10.1097/cin.0000000000001177 · Cited by 47 · see citing works
Tan, X., Cheng, G., & Ling, M. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100461 · Cited by 27 · see citing works
Ethical use of artificial intelligence and new technologies in Education 5.0
Smyrnaiou, Z., Liapakis, A., & Bougia, A. (2023) · Journal of Artificial Intelligence, Machine Learning and Data Science · doi:10.51219/jaimld/anastasios-liapakis/15 · Cited by 22 · see citing works
Impact of Artificial Intelligence Versus Traditional Instruction for Language Learning: A Survey
Dhanapal, C., Asharudeen, N., & Alfaruque, S. Y. (2024) · World Journal of English Language · doi:10.5430/wjel.v14n2p182 · Cited by 19 · see citing works
Arroyo-Machado, W., Ma, J., Chen, T., Johnson, T. P., Islam, S., Michalegko, L., & Welch, E. (2025) · PLOS One · doi:10.1371/journal.pone.0330416 · Cited by 15 · see citing works
Seung, Y., Basham, J. D., Kim, T., & Lohoefener, J. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70144 · Cited by 7 · see citing works
Evaluating Trust in AI, Human, and Co-produced Feedback Among Undergraduate Students
Zhang, A., Gao, Y., Suraworachet, W., Nazaretsky, T., & Cukurova, M. (2025) · arXiv · doi:10.48550/arxiv.2504.10961 · Cited by 7 · see citing works
Ahmad, S., Ahmed, A., Bhutta, S. M., & Ansari, A. N. (2024) · The Evolution of Artificial Intelligence in Higher Education · doi:10.1108/978-1-83549-486-820241007 · Cited by 4 · see citing works
HEIs teachers' and students' current experience of AI introduction in teaching and learning
Pinto, C. M. A., Mendonça, J. M. P., Babo, L., Queirós, R., Cruz, M., & Mascarenhas, D. (2024) · 2024 5th International Conference in Electronic Engineering, Information Technology &amp; Education (EEITE) · doi:10.1109/eeite61750.2024.10654407 · Cited by 2 · see citing works
Gulen, H. N., & Kucuk, S. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14020-1 · Cited by 3 · see citing works
Mirza, K. B., & Arif, M. (2026) · Education for Information · doi:10.1177/01678329261460361
- Authors
- Chen, X., Xie, H., Zou, D., & Hwang, G.-J.
- Affiliations
- Education University of Hong Kong (HK)
- Lingnan University (HK)
- National Taiwan University of Science and Technology (TW)
- Corresponding author
- Xie, Haoran
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2020
- Volume
- 1
- Pages
- 100002
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W3084223432
- Cited by
- 1,003
- References
- 169
- Citations by year
- 2026: 201 · 2025: 254 · 2024: 253 · 2023: 154 · 2022: 94 · 2021: 45 · 2020: 2
- FWCI
- 28.99
- 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)
- Funders
- Lingnan University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- artificial intelligence in education, educational theory, deep learning in education, natural language processing in education, intelligent tutoring systems, educational data mining, learning analytics, computer-assisted instruction, generative adversarial networks, personalized education, theory-practice gap
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.
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 ›
Are you an author, or do you see a problem with this page? Request a review
