The DOI matches its Crossref record; 127 of the 127 references it cites were found in a registry or an index.
Intelligent tutoring systems and learning outcomes: A meta-analysis
Abstract
Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g .42), non-ITS computer-based instruction (g .57), and textbooks or workbooks (g .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g –.11) or small-group instruction (g .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.
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
“Every learner has unique learning contexts based on their prior knowledge about the topic, social background, economic well-being and emotional state [41].”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [41]
In the citing paper
“Despite being limited in terms of the domain that a particular intelligent tutoring system addresses, they have proven to be effective in providing relevant content to students, interacting with students [6] and improving students’ academic performance [18, 41].”
From Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021 (2022) · cites this paper as [41]
Citations
Cites
(1988) · Cited by 16,582
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905
Wasserman, S. S., Hedges, L. V., & Olkin, I. (1988) · Journal of Educational Statistics · doi:10.2307/1164953 · Cited by 10,377
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2021) · doi:10.1002/9781119558378 · Cited by 9,799
(2000) · Cited by 9,198
The file drawer problem and tolerance for null results.
Rosenthal, R. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.3.638 · Cited by 9,068
An introduction to latent semantic analysis
Landauer, T. K., Foltz, P. W., & Laham, D. (1998) · Discourse Processes · doi:10.1080/01638539809545028 · Cited by 4,880
Fixed-and random-effects models in meta-analysis.
Hedges, L. V., & Vevea, J. L. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.486 · Cited by 2,977
The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring
BLOOM, B. S. (1984) · Educational Researcher · doi:10.3102/0013189x013006004 · Cited by 2,734 · cited 1×
“Another notable contribution of VanLehn's review was that he reported an effect size of d ϭ 0.79 for human tutoring compared with no-treatment controls, a value much smaller than the frequently cited, twosigma effect sizes reported by Bloom (1984).”
ResultInfluentialHays, W. (1983) · Contemporary Psychology · doi:10.1037/022267 · Cited by 2,399
Cited by
Sorted by citations
Items 201–300 of 840
Advancing Equity in Digital Classrooms
Iyer, L., Bharadwaj, S., Shetty, S. H., Verma, V., & Devanathan, M. (2022) · Advances in mobile and distance learning book series · doi:10.4018/978-1-6684-4364-4.ch011 · Cited by 25
Liu, F., Zhao, L., Zhao, J., Dai, Q., Fan, C., & Shen, J. (2022) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3216276 · Cited by 25
“The intelligent tutoring system (ITS) is also a computer-aided sys-tem to model learners’ psychological states to improve their learning outcomes [21], [22], [23].”
BackgroundA Review of Training and Guidance Systems in Medical Surgery
Escobar-Castillejos, D., Noguez, J., Bello, F., Neri, L., Magana, A. J., & Beneš, B. (2020) · Applied Sciences · doi:10.3390/app10175752 · Cited by 24
Adaptive teaching with technology enhances lasting learning
Sibley, L., Fabian, A., Plicht, C., Pagano, L., Ehrhardt, N., Wellert, L., Bohl, T., & Lachner, A. (2025) · Learning and Instruction · doi:10.1016/j.learninstruc.2025.102141 · Cited by 24
“For instance, Ma et al. (2014) found small to medium effects of ITS in their meta-analysis (0.36 < g < 0.41).”
InfluentialPredicting Early and Often: Predictive Student Modeling for Block-Based Programming Environments.
Emerson, A., Smith, A., Smith, C. R., Rodríguez, F. J., Min, W., Wiebe, E., Mott, B., Boyer, K. E., & Lester, J. C. (2019) · BearWorks (Missouri State University) · Cited by 24
Leite, W. L., Kuang, H., Jing, Z., Xing, W., Cavanaugh, T., & Huggins‐Manley, A. C. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104615 · Cited by 31
MathAIde: A Qualitative Study of Teachers’ Perceptions of an ITS Unplugged for Underserved Regions
Rodrigues, L., Guerino, G. C., da Silva, T. E. V., Challco, G. C., de Oliveira, L. C., da Penha, R. S., Melo, R. F., Vieira, T. M. D. A., Marinho, M., Macario, V., et al. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00397-y · Cited by 24
“As a result, empirical evidence demonstrates ITSs offer valuable benefits for learning compared to textbooks or workbooks; non-ITS computer-based instruction; teacher-led, large-group instruction; and - sometimes - human tutoring (Hillmayr et al., 2020; Ma et al., 2014; Xu et al., 2019).”
Finding the Best Types of Guidance for Constructing Self-Explanations of Subgoals in Programming
Margulieux, L. E., & Catrambone, R. (2018) · Journal of the Learning Sciences · doi:10.1080/10508406.2018.1491852 · Cited by 24
“…is working to replicate this feedback loop between learners and technology, such as with intelligent tutoring systems (e.g., Kulik & Fletcher, 2016; Ma, Adesope, Nesbit, & Liu, 2014), but the time-intensive development process makes personalized, elaborated feedback inaccessible for many…”
BackgroundLünich, M., Keller, B., & Marcinkowski, F. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100305 · Cited by 36
Teng, D., Wang, X., Xia, Y., Zhang, Y., Tang, L., Chen, Q., Zhang, R., Xie, S., & Yu, W. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13264-z · Cited by 30
“This finding aligns with previous research on intelligent tutoring systems, which have demonstrated positive effects on learning outcomes (VanLEHN, 2011; Ma et al., 2014).”
ONLINE FUNCTIONAL LITERACY, INTELLIGENT TUTORING SYSTEMS AND SCIENCE EDUCATION
Dolenc, K., Aberšek, B., & Aberšek, M. K. (2015) · Journal of Baltic Science Education · doi:10.33225/jbse/15.14.162 · Cited by 23
A Review of Intelligent Tutorial Systems in Computer and Web based Education
Alfaro, L., Rivera, C., Castañeda, E., Zúñiga-Cueva, J., Rivera-Chavez, M., & Fialho, F. A. P. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110295 · Cited by 23
“Based on these papers and a review of the literature on ITS assessments, the following definition is adopted [3] [14]: An ITS is a computer system that for each student performs tutoring with various functions that:”
BackgroundMethodInfluentialAdapting training in real time: An empirical test of adaptive difficulty schedules
Marraffino, M. D., Schroeder, B. L., Fraulini, N. W., Buskirk, W. L. V., & Johnson, C. I. (2021) · Military Psychology · doi:10.1080/08995605.2021.1897451 · Cited by 25
Tenório, K., Dermeval, D., Monteiro, M. D. S., Peixoto, A., & da Silva, A. P. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00274-y · Cited by 26
Durón, N. G., & Preciado, A. L. J. (2025) · The TQM Journal · doi:10.1108/tqm-10-2024-0376 · Cited by 29
Analyzing the Efficacy of ECoach in Supporting Gateway Course Success Through Tailored Support
Matz, R. L., Schulz, K. W., Hanley, E., Derry, H. A., Hayward, B., Koester, B. P., Hayward, C., & McKay, T. A. (2021) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3448139.3448160 · Cited by 23
“systems [31], here we have documented ECoach as a useful mentoring tool for improving student outcomes through motivational messaging and resources for reflecting on course performance.”
Methoddel Olmo‐Muñoz, J., González‐Calero, J. A., Diago, P. D., Arnau, D., & Arevalillo‐Herráez, M. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13228 · Cited by 32
Alhebaishi, S. M. (2017) · Journal of Education and Learning · doi:10.5539/jel.v6n3p70 · Cited by 22
“It has been further evaluated that students’ perceptions are mainly developed through informal approaches by their teachers during educational processes; also in the form of hints (Ma et al., 2014).”
BackgroundAttitude of University Students and Teachers towards Instructional Role of Artificial Intelligence
Hussaın, İ. (2020) · International Journal of Distance Education and E-Learning · doi:10.36261/ijdeel.v5i2.1057 · Cited by 21
Child-driven, machine-guided: Automatic scaffolding of constructionist-inspired early literacy play
Sysoev, I., Gray, J. H., Fine, S., Makini, S. P., & Roy, D. K. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104434 · Cited by 30
Design and implementation of an intelligent tutoring system in the view of learner autonomy
Le, H., & Jia, J. (2022) · Interactive Technology and Smart Education · doi:10.1108/itse-12-2021-0210 · Cited by 21
Intelligent tutoring systems: a history and an example of an ITS for science
Gobert, J. D., Pedro, M. S., Li, H., & Lott, C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818630-5.10058-2 · Cited by 24
Cognitive load considerations for Augmented Reality in network security training
Herbert, B. M., Wigley, G., Ens, B. M., & Billinghurst, M. (2021) · Computers & Graphics · doi:10.1016/j.cag.2021.09.001 · Cited by 23
Yapay Zekâ ve Geleceğin Meslekleri
Yavuz, N., & Ülgen, B. (2021) · TRT Akademi · doi:10.37679/trta.969285 · Cited by 26
Chevalère, J., Lazarides, R., Yun, H. S., Henke, A., Lazarides, C., Niels Pinkwart, C., & Hafner, V. V. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104741 · Cited by 28
Motivational and metacognitive feedback in SQL-Tutor*
Hull, A., & du Boulay, B. (2015) · Computer Science Education · doi:10.1080/08993408.2015.1033143 · Cited by 20
“While ITS is not as effective as one-to-one human tuition, this gap is narrowing as research progresses and they generally show an improvement to learning compared when not used (VanLehn, 2011) and even in some instances involving human tutors (Ma, Adesope, Nesbit, & Liu, 2014).”
González, A. N. V., Kapalo, K. A., Koh, S. L., & LaViola, J. J. (2017) · Multimodal Technologies and Interaction · doi:10.3390/mti1040030 · Cited by 20
“Because ITS are designed to present personalized information to the end user, it follows that studies have demonstrated that this form of instruction may lead to better learning outcomes than reading a paper manual or watching a video tutorial alone [9].”
BackgroundTuti, T., Winters, N., Edgcombe, H., Muinga, N., Wanyama, C., English, M., & Paton, C. (2020) · Journal of Medical Internet Research · doi:10.2196/17100 · Cited by 20
“The high drop-off rate with each subsequent round of play despite a lack of maximization of the learning gains (Multimedia Appendices 14-16) was disconcerting and limited the assessment of learners’skill mastery over time.”
BackgroundInfluentialNickl, M., Sommerhoff, D., Radkowitsch, A., Huber, S. A., Bauer, E., Ufer, S., Plass, J. L., & Seidel, T. (2024) · Learning and Instruction · doi:10.1016/j.learninstruc.2024.101994 · Cited by 21
“These findings align with the meta-analysis of Belland et al. (2017), which similarly did not corroborate the advantages of adaptive scaffolding suggested by prior theoretical (e.g., Corno, 2008) and empirical work in different contexts (e.g., Ma, Adesope, Nesbit, & Liu, 2014).”
How adaptive social robots influence cognitive, emotional, and self-regulated learning
Ackermann, H., Lange, A. L., Hafner, V. V., & Lazarides, R. (2025) · Scientific Reports · doi:10.1038/s41598-025-91236-0 · Cited by 27
“One example is intelligent tutoring systems (ITS), which provide personalized learning by adapting to learners’ cognitive and psychological states 25 .”
Sharma, K., Olsen, J. K., Aleven, V., & Rummel, N. (2020) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12467 · Cited by 19
Ni, A., Cheung, A., & Shi, J. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1025761 · Cited by 25
Chevalère, J., Yun, H. S., Henke, A., Niels Pinkwart, C., Hafner, V. V., & Lazarides, R. (2023) · Learning and Instruction · doi:10.1016/j.learninstruc.2023.101799 · Cited by 20
“…level), a model of the domain knowledge (e.g., knowledge that needs to be conveyed and the difficulty level of specific tasks), and a model of the tutor (e.g. teaching methods tailored to specific needs), and tailor learning content and instructional feedback to the learner (Ma et al., 2014).”
BackgroundMethodBach, K. M., Hofer, S. I., & Bichler, S. (2025) · Learning and Individual Differences · doi:10.1016/j.lindif.2025.102781 · Cited by 33
Mazon, C., Clément, B., Roy, D., Oudeyer, P., & Sauzeon, H. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11129-x · Cited by 25
“The efficacy of ITS with neurotypical children has been demonstrated in several studies, and several meta-analyses confirmed that ITS elicited positive effects on learning (Kulik & Fletcher, 2016; Ma, et al., 2014; VanLehn, 2011).”
BackgroundResultInfluentialAIED: From Cognitive Simulations to Learning Engineering, with Humans in the Middle
McNamara, D. S. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00349-y · Cited by 18
“Some of these systems leverage artificial intelligence (AI), but most are hybrid computational approaches to adapting the learning experience (Fournier-Viger et al., 2010 ; Ma et al., 2014 ).”
VanLehn, K., Chung, G. K. W. K., Grover, S., Madni, A., & Wetzel, J. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0093-5 · Cited by 22
“This situation, which was reviewed earlier, suggests using step-based tutoring to teach the cognitive skill of model construction because such systems are often effective at teaching cognitive skills (Kulik and Fletcher 2015; Ma et al. 2014; Steenbergen-Hu and Cooper 2013, 2014; VanLehn 2006, 2011).”
BackgroundeLearning Informatics: From Automation of Educational Activities to Intelligent Solutions Building
Ivanova, M. (2020) · Informatics in Education · doi:10.15388/infedu.2020.13 · Cited by 19
“Information visualization is placed between data and information and “provides methods and tools with which to organize and represent the data to finally produce information” (Mazza, 2009).”
Mentigo: An Intelligent Agent for Mentoring Students in the Creative Problem Solving Process
Zha, S., Liu, Y., Zheng, C., Xu, J., Yu, F., Gong, J., & Xu, Y. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713952 · Cited by 19
“Tutors focus on helping students master specific academic content or skills and facilitate an effective learning environment within small groups [21].”
Liu, B., Zhang, W., & Wang, F. (2026) · Education Sciences · doi:10.3390/educsci16010140 · Cited by 33
“This finding aligns with previous meta-analyses of educational technology (Ma et al., 2014; Al-Smadi, 2023).”
Alqahtani, F., & Ramzan, N. (2019) · Sensors · doi:10.3390/s19030460 · Cited by 17
“A review by Ma et al. [13] suggests that several studies between 1997 and 2010, place the learning outcome of ITS help more with course-specific evaluation than generalized testing.”
BackgroundDeskilling of Teaching and the Case for Intelligent Tutoring Systems
Hughes, J. (2021) · Journal of Ethics and Emerging Technologies · doi:10.55613/jeet.v31i2.90 · Cited by 21
“…tutoring systems in K-12 and higher education (Kulik & Fletcher, 2017; 528 Ma et al., 2014): “These results held across grade levels (elementary through higher 529 education), content domains, and study quality (e.g., randomized controlled trials and 530 quasi-experiments)” (Ma et al., 2014).”
BackgroundResultInfluentialFeng, S., Zhang, L., Wang, S., & Cai, Z. (2023) · Computers & Education · doi:10.1016/j.compedu.2023.104864 · Cited by 19
Zheng, J., Lajoie, S. P., Wang, T., & Li, S. (2023) · Metacognition and Learning · doi:10.1007/s11409-023-09352-z · Cited by 20
“A meta-analysis of these intelligent tutoring systems verified the effectiveness of scaffolding on problem-solving across various student levels (Ma et al., 2014 ).”
BackgroundArtificial Intelligence in Anatomy Teaching and Learning: A Literature Review
Pandurangam, G., Gurajala, S., & Nagajyothi, D. (2024) · National Journal of Clinical Anatomy · doi:10.4103/njca.njca_103_24 · Cited by 17
Intelligent Tutoring for Team Training: Lessons Learned from US Military Research
Freeman, J., & Zachary, W. W. (2018) · Research on managing groups and teams · doi:10.1108/s1534-085620180000019013 · Cited by 18
Knogler, M., Hetmanek, A., & Seidel, T. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.873995 · Cited by 24
Joshi, D. R., Adhikari, K. P., Khanal, J., Belbase, S., & Khanal, B. P. (2023) · Cogent Education · doi:10.1080/2331186x.2023.2230394 · Cited by 26
“Several studies on digital tools in school settings have been conducted (e.g., Cheung & Slavin, 2013; Ma et al., 2014; Steenbergen-Hu & Cooper, 2013).”
BackgroundEcheverría, G. S. G., Alvarez, A. F. Y., Espinosa, M. J. A., Aguayo, E. M. L., & Rodriguez, P. M. L. (2023) · Ciencia Latina Revista Científica Multidisciplinar · doi:10.37811/cl_rcm.v7i4.6967 · Cited by 16
A Real-time Multimodal Intelligent Tutoring Emotion Recognition System (MITERS)
Khediri, N., Ammar, M. B., & Kherallah, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16424-4 · Cited by 16
“ThemaincomponentsofanITSare: (1) Domain Model known also as expert module contains the knowledge and skills that the tutor is attempting to teach [32].”
Development of Ophthalmic Microsurgery Training in Augmented Reality
Menozzi, M., Ropelat, S., Köfler, J., & Huang, Y.-Y. (2020) · Klinische Monatsblätter für Augenheilkunde · doi:10.1055/a-1119-6151 · Cited by 16
“According to a meta-analysis [6], training progress based on an ITS is clearly superior to progress in a non-ITS training.”
MethodWhat to do and what to avoid on the use of gamified intelligent tutor system for low-income students
Joaquim, S., Bittencourt, I. I., Silva, R. D. A., Espinheira, P. L., & Reis, M. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10728-4 · Cited by 16
Rosenbaum, S., Moberg, J., Chesire, F., Mugisha, M., Ssenyonga, R., Ochieng, M., Simbi, C. M. C., Nakyejwe, E., Ngatia, B., Gabriel Rada, G., et al. (2023) · F1000Research · doi:10.12688/f1000research.132580.1 · Cited by 22
Adaptive Practice Quizzing in a University Lecture: A Pre-Registered Field Experiment
Heitmann, S., Obergassel, N., Fries, S., Grund, A., Berthold, K., & Roelle, J. (2021) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2021.07.008 · Cited by 16
The Adaptive Learning Landscape
Natriello, G. (2017) · Teachers College Record The Voice of Scholarship in Education · doi:10.1177/016146811711900307 · Cited by 16
11 Artificial Intelligence, Migration and Mobility: Implications for Policy and Practice
Beduschi, A., & McAuliffe, M. (2022) · World Migration Report · doi:10.1002/wom3.32 · Cited by 17
Computer-based feedback matters when relevant prior knowledge is not activated
Shirah, J. F., & Sidney, P. G. (2023) · Learning and Instruction · doi:10.1016/j.learninstruc.2023.101796 · Cited by 15
The Role of Artificial Intelligence in Education: A Systematic Literature Review
Bakti, I. K., Zulkarnain, Yarun, A., Rusdi, Syaifudin, M., & Syafaq, H. (2023) · JURNAL IQRA · doi:10.25217/ji.v8i2.3194 · Cited by 27
“Conversely, the majority of other studies concentrate on various specific topics related to artificial intelligence and technology within the realm of education (Heffernan and Heffernan 2014; Ma et al. 2014; Steenbergen-Hu and Cooper 2014).”
BackgroundCopur-Gencturk, Y., & Orrill, C. (2023) · Journal of Mathematics Teacher Education · doi:10.1007/s10857-023-09615-1 · Cited by 14
“In their meta-analysis, Ma and colleagues (Ma et al., 2014), analyzed 107 articles about intel-ligent-tutoring systems using this approach to feedback.”
InfluentialToribio, N. F. (2023) · Journal of Namibian Studies History Politics Culture · doi:10.59670/jns.v33i.3132 · Cited by 17
“Students engaged in the process of learning possess distinct learning contexts that are shaped by their pre-existing knowledge pertaining to the subject matter, their social background, their economic status, and their emotional well-being (Chaudhry & Kazim, 2022; Ma et al., 2014).”
BackgroundAdaptive training instructional interventions: A meta-analysis
Fraulini, N. W., Marraffino, M. D., Garibaldi, A. E., Johnson, C. I., & Whitmer, D. E. (2024) · Military Psychology · doi:10.1080/08995605.2024.2377884 · Cited by 18
“Moreover, ITS have been reviewed extensively in several recent literature reviews (e.g., Han et al., 2019; Kulik & Fletcher, 2016; Ma et al., 2014; Mousavinasab et al., 2021).”
Dixit, A. S., & Jatav, S. (2024) · Journal of Management Development · doi:10.1108/jmd-01-2024-0009 · Cited by 17
An AI-powered conversational system for college students learning English as a second language
Dai, L., & Wu, F. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13640-3 · Cited by 32
“A study by Heffernan and Koedinger (2002) shows that chatbots increase learner engagement and Mousavinasab et al. (2021) affirms how much students who use both have increased their use and involvement in the system in practicing their speaking and learning of a language (Ma et al., 2014).”
Cloud curriculum resource management platform based on Hadoop
Zhao, Y. L., & Liu, H. (2020) · Measurement and Control · doi:10.1177/0020294020948088 · Cited by 14
“32 Ma et al. 33 compared the learning effect with the intelligent tutoring system (ITS) and without the ITS, and proved that ITS was a relatively effective learning tool.”
BackgroundChevalère, J., Cazenave, L., Berthon, M., Martinez, R., Mazenod, V., Borion, M., Pailler, D., Rocher, N., Cadet, R., Lenne, C., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259664 · Cited by 14
Digitale Lernumgebungen – Konzepte, Forschungsergebnisse und Unterrichtspraxis
Röth, J. (2022) · Springer eBooks · doi:10.1007/978-3-662-65281-7_6 · Cited by 14
Intelligent tutoring system using expert knowledge and Kohonen maps with automated training
de Carvalho, S. D., de Melo, F. R., Flôres, E. L., Pires, S. R., & Loja, L. F. B. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04767-0 · Cited by 14
Borchers, C., Fleischer, H., Schanze, S., Scheiter, K., & Aleven, V. A. W. M. M. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105364 · Cited by 24
Kong, K., Isleem, H. F., Aluvalu, R., Tejani, G. G., & Metwally, A. S. M. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01333-0 · Cited by 20
“(i) A multi‑site MRT/JITAI study to causally estimate the effect of each intervention family and optimize (\epsilon)‑greedy parameters by state [32].”
InfluentialBetts, A. L., Thai, K.-P., & Gunderia, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77857-6_3 · Cited by 19
Jabbour, J., Kleinbard, K., Miller, O., Haussman, R., & Reddi, V. J. (2025) · Workshop on Computer Architecture Education (WCAE) · doi:10.1145/3743646.3750010 · Cited by 24
“Early systems like MATHia [15], ALEKS [16], and DreamBox [17] pio-neered personalized learning using rule-based reasoning and data-driven adaptation, particularly in STEM [18, 19].”
Effect of Non-mandatory Use of an Intelligent Tutoring System on Students’ Learning
Mitrović, A., & Holland, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-52237-7_31 · Cited by 13
“Other recent meta-analyses of reported evaluations of ITSs show similar findings [5, 6].”
ResultiSTART StairStepper—Using Comprehension Strategy Training to Game the Test
Arner, T., McCarthy, K. S., & McNamara, D. S. (2021) · Computers · doi:10.3390/computers10040048 · Cited by 14
“Motivation to persist in the practice necessary to improve reading comprehension skills may be bolstered by the affordances of ITSs [48], particularly those with anthropomorphized feedback mechanisms (e.g., pedagogical agents) [68] and game-based learning and assessment [39,40,88].”
BackgroundAnwar, A., Haq, I. U., Mian, I. A., Shah, F., Alroobaea, R., Hussain, S., Ullah, S. S., & Umar, F. (2022) · Mobile Information Systems · doi:10.1155/2022/6006467 · Cited by 18
“[34] analyzed in their studies the effects of ITS on-learning outcomes.”
BackgroundHilz, A., Guill, K., Roloff, J., Aldrup, K., & Köller, O. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12780 · Cited by 19
Kessler, A., Stein, M. K., & Schunn, C. D. (2015) · Technology Knowledge and Learning · doi:10.1007/s10758-015-9248-6 · Cited by 19
“Hallmarks of these tutors are: (1) they track skills through students’ actions; (2) feedback is tailored to the particular skills they appear to be missing; and (3) practice focuses on topics for skills which the students have not developed (Ma et al. 2014).”
BackgroundMaximizing the Affordances of Contemporary Technologies in Education: Promises and Possibilities
Adesope, O., & Rud, A. G. (2018) · Springer eBooks · doi:10.1007/978-3-319-89680-9_1 · Cited by 14
Self-citationChen, A., Liu, H., Li, K. C., & Jia, J. (2023) · Sustainability · doi:10.3390/su152014709 · Cited by 13
“These systems have been made for many subjects like mathematics, medicine, law, and reading to help learners gain specific skills and learn how to think about their own learning [27].”
BackgroundAI in Teaching and Learning and Intelligent Tutoring Systems
Khine, M. S. (2024) · Springer eBooks · doi:10.1007/978-981-97-9350-1_4 · Cited by 20
AI-Based Smart Education System to Enhanced the Learning of Students
Barde, A., Thakur, R., Patel, S., Sinah, N., & Barde, S. (2024) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset62108.2024.10743859 · Cited by 12
“Researchers have also raised questions about algorithmic bias and discrimination in AI-powered educational systems [4].”
BackgroundTan, L.-P., Gong, S., Wang, Y., Guo, X., Xu, X., & Wang, Y. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10001-x · Cited by 12
“The integration of self-explanation in DLEs is enhanced through the incorporation of a range of media resources, such as videos (Pi et al., 2020), intelligent tutoring systems (Rau et al., 2015), games (Hsu et al., 2012), and simulations (Ma et al., 2014).”
Hannan, B., & Eynon, R. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105312 · Cited by 12
Personalized Learning and Online Instruction
Svenningsen, L., Bottomley, S., & Pear, J. J. (2018) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-5225-3940-7.ch008 · Cited by 13
The Role of Artificial Intelligence in the Education Process of Political Science Field
Khalifa, M. (2022) · ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) · doi:10.1109/icetsis55481.2022.9888844 · Cited by 12
“[4] [5] These systems can also foster a more positive learning environment than traditional classroom settings.”
BackgroundCo-designing Gamified Intelligent Tutoring Systems with Teachers
Dermeval, D., & Bittencourt, I. I. (2020) · Revista Brasileira de Informática na Educação · doi:10.5753/rbie.2020.28.0.73 · Cited by 12
“A broader definition (Ma et al., 2014) qualify ITS as a computer system that for each student: (i) performs tutoring functions, for example, by presenting information to be learned, by asking questions or assigning learning tasks, by providing feedback or hints, by answering questions posed by…”
BackgroundGenerative AI and Higher Education Assessments: A Competency-Based Analysis
Tenakwah, E. S., Boadu, G., Tenakwah, E. J., Parzakonis, M., Brady, M., Kansiime, P., Said, S., Ayilu, R., Radavoi, C., & Berman, A. (2023) · doi:10.21203/rs.3.rs-2968456/v1 · Cited by 11
Marougkas, A., Troussas, C., Krouska, A., & Sgouropoulou, C. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00392-3 · Cited by 11
Using Technology to Address Individual Differences in Learning
Antonenko, P. D., Dawson, K., Cheng, L., & Wang, J. (2020) · Springer eBooks · doi:10.1007/978-3-030-36119-8_6 · Cited by 10
The Feedback Principle in Multimedia Learning
Johnson, C. I., & Marraffino, M. D. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108894333.042 · Cited by 11
Conversational Agents to Support Remote Personalized Instruction for Diverse Learners
Carter, R. A., Zhang, L., Hunt, T., Emerling, C. R., Yang, S., & Rujimora, J. (2023) · TechTrends · doi:10.1007/s11528-023-00877-3 · Cited by 11
“These data and prompts allow CAs to provide structured feedback, assist learners with tasks, adapt learning tasks and materials to learner preferences, and support conversation abilities (Kulik & Fletcher, 2016; Ma et al., 2014; Rus et al., 2013).”
BackgroundEnhancing Student Engagement in Learning with Modern Web and AI Technologies
Sangarsu, R. R. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/sr231017100712 · Cited by 11
“The utilization of machine learning in these platforms continually refines the learning path based on students' interactions and performance, further personalizing the experience (Graesser et al., 2018).”
BackgroundLoor, M. A. A., Loor, A. E. Q., Loor, G. V. Q., Intriago, L. A. M., & Escobar, D. R. P. (2024) · Ciencia Latina Revista Científica Multidisciplinar · doi:10.37811/cl_rcm.v8i1.9856 · Cited by 11
You only need to change your direction: A look at the potential impact of ChatGPT on education
Kohler, K. (2024) · Technology in Language Teaching & Learning · doi:10.29140/tltl.v6n1.1103 · Cited by 19
Research Progress on Electrolyte Additives for Sodium Ion Batteries
Zhang, Z., Fan, Z., Li, J., He, H., Zhang, H., Li, Z., Lin, J., Zhang, X., & Zhang, J. (2024) · Sustainable polymer & energy · doi:10.35534/spe.2024.10003 · Cited by 11
Dada, S., Flores, C., Bastable, K., Tönsing, K. M., Samuels, A. E., Mukhopadhyay, S., Isanda, B., Bampoe, J. O., Stemela‐Zali, U., Karim, S. B., et al. (2024) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.13084 · Cited by 11
Guerino, G. C., Rodrigues, L., de Oliveira, L. K. S., Marinho, M., da Silva, T. E. V., Amorim, L., Dermeval, D., da Penha, R. S., Bittencourt, I. I., & Isotani, S. (2024) · Revista Brasileira de Informática na Educação · doi:10.5753/rbie.2024.3649 · Cited by 12
Janson, M. P., & Janke, S. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00488-9 · Cited by 15
Apprentissages scolaires et technologies numériques : une revue critique des méta-analyses
Leroux, G., Monteil, J., & Huguet, P. (2017) · L’Année psychologique · doi:10.3917/anpsy.174.0433 · Cited by 11
Sie, R. L. L., Delahunty, J., Bell, K., Percy, A., Rienties, B., Cao, T., & de Laat, M. F. (2018) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale.2018.8615283 · Cited by 12
“Systems that automatically model and assess learners, to provide them with automated, personalised feedback [38, 39].”
BackgroundDesafios no uso de Dados Abertos Conectados na Educação Brasileira
Alcantara, W., Bandeira, J., Barbosa, A., Lima, A. L. R., Ávila, T. J. T., Bittercourt, I., & Isotani, S. (2015) · doi:10.5753/desafie.2015.10036 · Cited by 11
Metadata
- Authors
- Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q.
- Affiliations
- Simon Fraser University (CA)
- Washington State University (US)
- Venue
- Journal of Educational Psychology
- Published
- 2014-11
- Volume
- 106
- Issue
- 4
- Pages
- 901-918
- ISSN
- 0022-0663 (ISSN-L)
- 1939-2176
- OpenAlex
- W2117655204
- Cited by
- 840
- References
- 127
- Citations by year
- 2026: 179 · 2025: 132 · 2024: 99 · 2023: 65 · 2022: 67 · 2021: 69 · 2020: 70 · 2019: 41 · 2018: 43 · 2017: 36 · 2016: 22 · 2015: 17
- FWCI
- 24.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Psychology (publishedVersion)
- Funders
- Social Sciences and Humanities Research Council of Canada
- Washington State University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Keywords
- intelligent tutoring systems, learning outcomes, personalized learning, computer-assisted instruction, teacher-centered instruction, small group instruction, homework help, metacognitive knowledge, feedback, misconceptions, publication bias, effect size
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
