The DOI matches its Crossref record; 86 of the 87 references it cites were found in a registry or an index.
Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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
“The findings show that performance prediction, resource recommendation, automatic assessment, and improvement of learning experiences are the four main functions of AI applications in online higher education.”
From Artificial intelligence in higher education: the state of the field (2023) · cites this paper as Ouyang et. al. (2022)
In the citing paper
“Extant systematic reviews on HE highlighted the use of AIEd for prediction (viz., Chu etal., 2022; Hinojo-Lucena etal., 2019; Ouyang etal., 2022; Zawacki-Richter etal., 2019).”
From Artificial intelligence in higher education: the state of the field (2023) · cites this paper as Ouyang et al., 2022
In the citing paper
“Based on such emotional signals, AI can adapt the learning environment, such as by using motivational messages or simplifying the task at moments of frustration of the learner, or gamifying when the learner is bored [216].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [216]
In the citing paper
“higher education settings are not only about promoting cognitive skills and knowledge but also play a significant role in developing learners' socio-affective skills, which are crucial for their overall well-being ([35,37]).”
Citations
Cites
(1978) · Medical Entomology and Zoology · Cited by 53,488
Three Approaches to Qualitative Content Analysis
Hsieh, H.-F., & Shannon, S. E. (2005) · Qualitative Health Research · doi:10.1177/1049732305276687 · Cited by 44,601
Graneheim, U. H., & Lundman, B. (2004) · Nurse Education Today · doi:10.1016/j.nedt.2003.10.001 · Cited by 20,772
Bibliometric Methods in Management and Organization
Zupic, I., & Čater, T. (2015) · Organizational Research Methods · doi:10.1177/1094428114562629 · Cited by 7,495
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 · cited 3×
“…are still in the 1 3 preliminary and exploratory stage, AI is proved to be positive to enhance online instruction and learning quality by offering accurate prediction, assessment and engaging students with online materials and environments (Yang et al., 2020; Zawacki-Richter et al., 2019).”
BackgroundResultWohlin, C. (2014) · Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering · doi:10.1145/2601248.2601268 · Cited by 4,234 · cited 1×
“After the screening of the full articles, the snowballing approach was performed based on the guidelines to find the articles that were not extracted by using the search strings (Wohlin, 2014).”
MethodHollan, J., Hutchins, E., & Kirsh, D. (2000) · ACM Transactions on Computer-Human Interaction · doi:10.1145/353485.353487 · Cited by 2,300
Vision, challenges, roles and research issues of Artificial Intelligence in Education
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100001 · Cited by 1,435
A Review of Artificial Intelligence (AI) in Education from 2010 to 2020
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021) · Complexity · doi:10.1155/2021/8812542 · Cited by 1,364 · cited 1×
“For instance, existing review work has summarized the trends of AIEd (Tang et al., 2021; Xie et al., 2019; Zhai et al., 2021), applications (Alyahyan & Düştegör, 2020; Hooshyar et al., 2016; Liz-Domínguez et al., 2019; Shahiri et al., 2015), theoretical paradigms (Ouyang & Jiao, 2021) and AI…”
BackgroundApplication and theory gaps during the rise of artificial intelligence in education
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100002 · Cited by 1,003
Artificial 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
Self-citationArtificial Intelligence trends in education: a narrative overview
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.08.233 · Cited by 956
(2013) · Research & Practice in Assessment · Cited by 920
Shift happens: online education as a new paradigm in learning
Harasim, L. M. (2000) · The Internet and Higher Education · doi:10.1016/s1096-7516(00)00032-4 · Cited by 839
Predicting academic success in higher education: literature review and best practices
Alyahyan, E. A., & Düştegör, D. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-020-0177-7 · Cited by 803
A Review on Predicting Student's Performance Using Data Mining Techniques
Shahiri, A. M., Husain, W., & Rashid, N. A. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.12.157 · Cited by 801
AI-enabled adaptive learning systems: A systematic mapping of the literature
Kabudi, T., Pappas, I., & Olsen, D. H. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100017 · Cited by 734
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103599 · Cited by 713
Theory and Practice of Distance Education
Holmberg, B. (2005) · Routledge eBooks · doi:10.4324/9780203973820 · Cited by 678
Artificial intelligence innovation in education: A twenty-year data-driven historical analysis
Guan, C., Mou, J., & Jiang, Z. (2020) · International Journal of Innovation Studies · doi:10.1016/j.ijis.2020.09.001 · Cited by 665
Problem Solving and Communicative Action: A Situational Theory of Problem Solving
Kim, J., & Grunig, J. E. (2011) · Journal of Communication · doi:10.1111/j.1460-2466.2010.01529.x · Cited by 589
Hwang, G., & Tu, Y. (2021) · Mathematics · doi:10.3390/math9060584 · Cited by 558
Predicting students' final performance from participation in on-line discussion forums
Romero, C., López, M.-I., Luna, J. M., & Ventura, S. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.06.009 · Cited by 548
Costa, E. D. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. B. A. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.01.047 · Cited by 462
More than tools? Making sense of the ongoing digitizations of higher education
Castañeda, L., & Selwyn, N. (2018) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-018-0109-y · Cited by 445
Human‐centered artificial intelligence and machine learning
Riedl, M. O. (2019) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.117 · Cited by 445
Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative
Jayaprakash, S. M., Moody, E. W., Lauría, E. J. M., Regan, J. R., & Baron, J. D. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.11.3 · Cited by 424
Dropout 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 416
Intelligent web-based learning system with personalized learning path guidance
Chen, C. (2007) · Computers & Education · doi:10.1016/j.compedu.2007.08.004 · Cited by 413
Tang, K., Chang, C., & Hwang, G. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2021.1875001 · Cited by 405
Tomasevic, N. M., Gvozdenovic, N., & Vraneš, S. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103676 · Cited by 404
Hew, K. F., Hu, X. Y., Qiao, C., & Tang, Y. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103724 · Cited by 403
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
Chen, X., Xie, H., & Hwang, G. (2020) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2020.100005 · Cited by 379
Tyler-Smith, K. (2006) · Journal of online learning and teaching · Cited by 336
Deeva, G., Bogdanova, D., Serral, E., Snoeck, M., & Weerdt, J. D. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104094 · Cited by 292
Web usage mining for predicting final marks of students that use Moodle courses
Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S. (2010) · Computer Applications in Engineering Education · doi:10.1002/cae.20456 · Cited by 288
Developing early warning systems to predict students’ online learning performance
Hu, Y., Lo, C., & Shih, S.-P. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.04.002 · Cited by 278
Xu, X. M., Wang, J., Peng, H. M., & Wu, R. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.04.015 · Cited by 261
Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.12.007 · Cited by 256
Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout
Burgos, C., Campanario, M. L., de la Peña, D., Lara, J. A., Lizcano, D., & Martínez, M. A. (2017) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.03.005 · Cited by 240
Supporting Students in Online, Open and Distance Learning
Simpson, O. (2018) · Routledge eBooks · doi:10.4324/9780203417003 · Cited by 230
A systematic review of AI role in the educational system based on a proposed conceptual framework
Xu, W., & Ouyang, F. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10774-y · Cited by 230 · cited 2×
“…summarized the trends of AIEd (Tang et al., 2021; Xie et al., 2019; Zhai et al., 2021), applications (Alyahyan & Düştegör, 2020; Hooshyar et al., 2016; Liz-Domínguez et al., 2019; Shahiri et al., 2015), theoretical paradigms (Ouyang & Jiao, 2021) and AI roles in education ( Xu & Ouyang, 2021).”
BackgroundSelf-citationMultimodal data as a means to understand the learning experience
Giannakos, M. N., Sharma, K., Pappas, I. O., Kostakos, V., & Velloso, E. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.02.003 · Cited by 227
Review of ontology-based recommender systems in e-learning
George, G., & Lal, A. M. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103642 · Cited by 201
Prediction of Student’s performance by modelling small dataset size
Zohair, L. M. A. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0160-3 · Cited by 196
Predicting academic performance by considering student heterogeneity
Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D. W., & Long, Q. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.07.042 · Cited by 192
A hybrid recommendation algorithm adapted in e-learning environments
Chen, W., Niu, Z., Zhao, X., & Li, Y. (2012) · World Wide Web · doi:10.1007/s11280-012-0187-z · Cited by 183
Prediction in MOOCs: A Review and Future Research Directions
Moreno-Marcos, P. M., Alario‐Hoyos, C., Muñoz‐Merino, P. J., & Kloos, C. D. (2018) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2018.2856808 · Cited by 180
Neuendorf, K. A., & Kumar, A. (2015) · The International Encyclopedia of Political Communication · doi:10.1002/9781118541555.wbiepc065 · Cited by 176
Prediction of students’ early dropout based on their interaction logs in online learning environment
Mubarak, A. A., Cao, H., & Zhang, W. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2020.1727529 · Cited by 170
Behavior-Based Grade Prediction for MOOCs via Time Series Neural Networks
Yang, T.-Y., Brinton, C. G., Joe‐Wong, C., & Chiang, M. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2700227 · Cited by 158
Godwin, A., & Kirn, A. N. (2020) · Journal of Engineering Education · doi:10.1002/jee.20324 · Cited by 156
Personalized recommender system for e-Learning environment
Benhamdi, S., Babouri, A., & Chiky, R. (2017) · Education and Information Technologies · doi:10.1007/s10639-016-9504-y · Cited by 146
Hooshyar, D., Ahmad, R. B., Yousefi, M., Fathi, M., Horng, S., & Lim, H. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.10.013 · Cited by 140
Contrasting prediction methods for early warning systems at undergraduate level
Howard, E., Meehan, M., & Parnell, A. C. (2018) · The Internet and Higher Education · doi:10.1016/j.iheduc.2018.02.001 · Cited by 135
An early feedback prediction system for learners at-risk within a first-year higher education course
Baneres, D., Rodriguez, M. E., & Serra, M. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2912167 · Cited by 128
Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J., & Yang, S. J. H. (2020) · Interactive Learning Environments · doi:10.1080/10494820.2019.1636086 · Cited by 124
Christudas, B. C. L., Kirubakaran, E., & Thangaiah, P. R. J. (2017) · Telematics and Informatics · doi:10.1016/j.tele.2017.02.004 · Cited by 111
Virtual worlds vs books and videos in history education
Ijaz, K., Bogdanovych, A., & Trescak, T. (2017) · Interactive Learning Environments · doi:10.1080/10494820.2016.1225099 · Cited by 106
Use of Web‐based formative assessment to support student learning in a metabolism/nutrition unit
Henly, D. C. (2003) · European Journal Of Dental Education · doi:10.1034/j.1600-0579.2003.00310.x · Cited by 104
A Practical Teaching Mode for Colleges Supported by Artificial Intelligence
Yang, C., Huan, S., & Yang, Y. (2020) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v15i17.16737 · Cited by 90
Yoo, J., & Kim, J. (2013) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-013-0010-8 · Cited by 82
An online adaptive learning environment for critical‐thinking‐infused E nglish literacy instruction
Yang, Y., Gamble, J. H., Hung, Y., & Lin, T. (2013) · British Journal of Educational Technology · doi:10.1111/bjet.12080 · Cited by 79
MOOC-Rec: A Case Based Recommender System for MOOCs
Bousbahi, F., & Chorfi, H. O. (2015) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2015.06.395 · Cited by 78
The potential for student performance prediction in small cohorts with minimal available attributes
Wakelam, E., Jefferies, A., Davey, N., & Sun, Y. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12836 · Cited by 76
Predicting who will drop out of nursing courses: A machine learning exercise
Moseley, L. G., & Mead, D. (2007) · Nurse Education Today · doi:10.1016/j.nedt.2007.07.012 · Cited by 72
e‐Learning recommender system for a group of learners based on the unified learner profile approach
Dwivedi, P., & Bharadwaj, K. K. (2013) · Expert Systems · doi:10.1111/exsy.12061 · Cited by 69
Koć‐Januchta, M., Schönborn, K. J., Tibell, L. A. E., Chaudhri, V. K., & Heller, H. C. (2020) · Journal of Educational Computing Research · doi:10.1177/0735633120921581 · Cited by 69
Systematic Literature Review of Predictive Analysis Tools in Higher Education
Liz-Domínguez, M., Rodríguez, M. C., Nistal, M. L., & Mikic-Fonte, F. A. (2019) · Applied Sciences · doi:10.3390/app9245569 · Cited by 68
Effects and acceptance of precision education in an AI-supported smart learning environment
Hu, Y.-H. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10664-3 · Cited by 68
A web‐based learning and assessment system to support flexible education
Gardner, L. A., Sheridan, D., & White, D. (2002) · Journal of Computer Assisted Learning · doi:10.1046/j.0266-4909.2001.00220.x · Cited by 63
E-learning based on the adaptive learning model: case study in Serbia
Arsović, B., & Stefanović, N. (2020) · Sadhana · doi:10.1007/s12046-020-01499-8 · Cited by 63
Using Data to Understand How to Better Design Adaptive Learning
Liu, M., Kang, J., Zou, W., Lee, H., Pan, Z., & Corliss, S. B. (2017) · Technology Knowledge and Learning · doi:10.1007/s10758-017-9326-z · Cited by 59
Rico-Juan, J. R., Gallego, A., & Calvo-Zaragoza, J. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103609 · Cited by 50
Aguiar, E., Ambrose, G. A. A., Chawla, N. V., Goodrich, V., & Brockman, J. B. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.13.3 · Cited by 47
A self-adjusting e-course generation process for personalized learning
Li, J., Chang, Y.-C., Chu, C., & Tsai, C. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.09.009 · Cited by 45
The Effect of Using Automated Essay Evaluation on ESL Undergraduate Students’ Writing Skill
Aluthman, E. S. (2016) · International Journal of English Linguistics · doi:10.5539/ijel.v6n5p54 · Cited by 45
Recommender Systems and Scratch: An Integrated Approach for Enhancing Computer Programming Learning
Cobo, J., Puris, A., Hernández, P. N., Galindo, J. Á., & Benavides, D. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2901457 · Cited by 43
Sukhbaatar, O., Usagawa, T., & Choimaa, L. (2019) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v14i19.10366 · Cited by 41
Chang, T.-Y., & Ke, Y.-R. (2013) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2012.04.002 · Cited by 40
Almeda, M. V., Zuech, J., Baker, R. S., Utz, C., Higgins, G., & Reynolds, R. (2018) · Online Learning · doi:10.24059/olj.v22i1.1060 · Cited by 37
du Boulay, B. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45108-0_3 · Cited by 36
Analysing the predictive power for anticipating assignment grades in a massive open online course
Moreno-Marcos, P. M., Muñoz‐Merino, P. J., Alario‐Hoyos, C., Ayres, I. E., & Kloos, C. D. (2018) · Behaviour and Information Technology · doi:10.1080/0144929x.2018.1458904 · Cited by 35
Shaping Our Algorithms Before They Shape Us
Rowe, M. (2019) · Perspectives on rethinking and reforming education · doi:10.1007/978-981-13-8161-4_9 · Cited by 27
MapOnLearn: The Use of Maps in Online Learning Systems for Education Sustainability
Liu, S., Guo, D., Sun, J., Yu, J., & Zhou, D. (2020) · Sustainability · doi:10.3390/su12177018 · Cited by 15
Research works printed but not linked: 4
- Holmes, W., Bialik, M., & Fadel, C. (2019). Artifificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign
- Law, N. W. Y. (2019). Human development and augmented intelligence. In The 20th international conference on artificial intelligence in education (AIED 2019). Springer. Retrieved on 2021/1/1 from https://www.sciencedirect.com/science/refhub/S2666-920X(21)00014-undefined/sref31
- Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Prisma Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097.t001 · DOI
- Selwyn, N. (2016). Is technology good for education? Polity Press. Retrieved on 2021/1/10 from http://au.wiley.com/WileyCDA/WileyTitle/productCd-0745696465.html
Cited by
Sorted by citations
Artificial intelligence in higher education: the state of the field
Crompton, H., & Burke, D. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00392-8 · Cited by 2,010
“Scholars (viz., Ouyang et al., 2022) who conducted a systematic review on AIEd in HE up to 2020 noted that they missed relevant articles from their study, and other relevant journals should intentionally be examined.”
A SWOT analysis of ChatGPT: Implications for educational practice and research
Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2195846 · Cited by 1,384
Ethical principles for artificial intelligence in education
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11316-w · Cited by 1,228
“The penetration of AI in every sphere of educational practices has undeniably filtered teachers’ and students’ personal and professional development with numerous opportunities (Xu & Ouyang, 2021; Ouyang et al., 2022).”
BackgroundChatGPT in higher education: considerations for academic integrity and student learning
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.17 · Cited by 853
“…reviews focused on AI in higher education highlight that studies tend to frame AI principally as a tool for improving assignment feedback and assisting with administrative duties rather than exploring concerns relating to academic integrity (Ouyang et al., 2022; Zawacki-Ricther et al., 2019).”
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00436-z · Cited by 852
“Bib-liometric analyses have also emerged as a popular form of evidence synthesis (e.g., Lin-nenluecke et al., 2020; Zheng et al., 2022), which analyse bibliographic data to explore research trends and impact.”
Background
Metadata
- Authors
- Ouyang, F., Zheng, L., & Jiao, P.
- Affiliations
- Zhejiang University (CN)
- Corresponding author
- Ouyang, Fan
- Venue
- Education and Information Technologies
- Published
- 2022-02-26
- Volume
- 27
- Issue
- 6
- Pages
- 7893-7925
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4214643205
- Semantic Scholar
- 811c64131e009de84f0d92da6cc96e824a291bd5
- Cited by
- 831
- References
- 87
- Citations by year
- 2026: 198 · 2025: 263 · 2024: 251 · 2023: 112 · 2022: 7
- FWCI
- 19.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Education and Information Technologies (publishedVersion)
- Funders
- National Natural Science Foundation of China (62177041)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- artificial intelligence in education, online higher education, online learning, learning analytics, learning outcome prediction, student satisfaction prediction, resource recommendation, automated assessment, deep learning, genetic algorithm, student engagement, academic performance
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
