Intelligent tutoring systems and learning outcomes: A meta-analysis

Ma, Wenting; Adesope, Olusola O.; Nesbit, John C.; Liu, Qing

doi:10.1037/a0037123

Cited by 840Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning MethodsVisual and Cognitive Learning Processes

Intelligent tutoring systems and learning outcomes: A meta-analysis

Ma, Wenting, Adesope, Olusola O., Nesbit, John C., Liu, Qing·2014·Journal of Educational Psychology

DOI 10.1037/a0037123

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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.

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Every learner has unique learning contexts based on their prior knowledge about the topic, social background, economic well-being and emotional state.
“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]
Despite being limited in domain, ITS have proven to be effective in providing relevant content to students, interacting with students, and improving students' academic performance.
“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]

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Fossati, D., Eugenio, B. D., Brown, C. W., & Ohlsson, S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69132-7_13 · Cited by 25 · see citing works
The Andes Physics Tutoring System: An Experiment in Freedom
VanLehn, K. A., van de Sande, B., Shelby, R., & Gershman, S. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-14363-2_21 · Cited by 22 · see citing works
An intelligent tutoring system
Srivastava, R. P., & Sin, S.-Y. (1990) · International Phoenix Conference on Computers and Communications (IPCCC) · doi:10.1109/pccc.1990.101690 · Cited by 22 · see citing works
A multi-institutional evaluation of Intelligent Tutoring Tools in Numeric Disciplines
Patel, A., Russell, D., & Kinshuk, K. (2000) · DMU Open Research Archive (De Montfort University) · Cited by 21 · see citing works
EVALUATING THE INTELLIGENT FEATURES OF A WEB-BASED INTELLIGENT COMPUTER ASSISTED LANGUAGE LEARNING SYSTEM
Tsiriga, V., & Virvou, M. K. (2004) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213004001600 · Cited by 19 · see citing works
Automating Individualized Coaching and Authentic Role-play Practice for Brief Intervention Training
Saker, R., Amano, K., & Hayes‐Roth, B. (2010) · Methods of Information in Medicine · doi:10.3414/me9311 · Cited by 19 · see citing works
Evaluating a web based intelligent tutoring system for mathematics at German lower secondary schools
Graff, M., Mayer, P., & Lebens, M. (2008) · Education and Information Technologies · doi:10.1007/s10639-008-9062-z · Cited by 17 · see citing works
Thermo-Tutor: An Intelligent Tutoring System for thermodynamics
Mitrović, A., Williamson, C., Bebbington, A., Mathews, M., Suraweera, P., Martin, B., Thomson, D., & Holland, J. (2011) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon.2011.5773164 · Cited by 17 · see citing works
The Effect of an Intelligent Tutoring System (ITS) on Student Achievement in Algebraic Expression.
Chien, T. C., Yunus, A. S. M., Ali, W. Z. W., & Bakar, A. R. (2008) · Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) · Cited by 15 · see citing works
Comparative Effectiveness of Carnegie Learning's "Cognitive Tutor Bridge to Algebra" Curriculum: A Report of a Randomized Experiment in the Maui School District. Research Report.
Cabalo, J. V., Ma, B., & Jaciw, A. P. (2007) · Cited by 15 · see citing works
Tutoring Bilingual Students With an Automated Reading Tutor That Listens: Results of a Two-Month Pilot Study
Poulsen, R. (2005) · Cited by 12 · see citing works
A method for learning scenario determination and modification in intelligent tutoring systems
Kozierkiewicz, A., & Nguyên, N. T. (2011) · International Journal of Applied Mathematics and Computer Science · doi:10.2478/v10006-011-0005-2 · Cited by 12 · see citing works
THE EFFECT OF THE CARNEGIE ALGEBRA TUTOR ON STUDENT ACHIEVEMENT AND ATTITUDE IN INTRODUCTORY HIGH SCHOOL ALGEBRA
Smith, J. E. (2001) · VTechWorks (Virginia Tech) · Cited by 11 · see citing works
What is our effect size: Evaluating the educational influence of a web-based intelligent authoring shell
Stankov, S., Glavinić, V., & Grubišić, A. (2017) · doi:10.13140/rg.2.2.29526.96322 · Cited by 10 · see citing works
The Virtual Sand Table: Intelligent Tutoring for Field Artillery Training
Wisher, R. A., Macpherson, D. H., Abramson, L. J., Thronton, D. M., & Dees, J. J. (2001) · doi:10.21236/ada392596 · Cited by 9 · see citing works
A Metacognitive ACT-R Model of Students’ Learning Strategies in Intelligent Tutoring Systems
Roll, I., Baker, R. S., Aleven, V., & Koedinger, K. R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30139-4_98 · Cited by 8 · see citing works
An Assessment for Learning System Called ACED: Designing for Learning Effectiveness and Accessibility. Research Report. ETS RR-07-26.
Shute, V. J., Hansen, E. G., & Almond, R. G. (2007) · ETS Research Report Series · Cited by 8 · see citing works
Computer-assisted oral reading helps third graders learn vocabulary better than a classroom control – about as well as one-on-one human-assisted oral reading
Aist, G., Mostow, J., Tobin, B., Burkhead, P., Corbett, A. T., Cuneo, A., & Junker, B. W. (2001) · Cited by 7 · see citing works
Interactive reconstructive student modeling: A machine‐learning approach
Mitrović, A., & Djordjević‐Kajan, S. (1995) · International Journal of Human-Computer Interaction · doi:10.1080/10447319509526132 · Cited by 6 · see citing works
Techniques for Creating Computer-Based Instructional Text: Programming Languages, Authoring Languages, and Authoring Systems
Hannum, W. H. (1986) · Educational Psychologist · doi:10.1207/s15326985ep2104_4 · Cited by 5 · see citing works
Evaluation of the effectiveness of a computer-assisted intelligent tutoring system model developed to improve specific learning skills of special needs students
Radwan, Z. R. (1997) · Cited by 4 · see citing works
SI-APRENDE: An Intelligent Learning System Based on SCORM Learning Objects for Training Power Systems Operators
Argotte, L., Figueroa, G., & Noguez, J. (2011) · Studies in computational intelligence · doi:10.1007/978-3-642-21332-8_5 · Cited by 3 · see citing works
Scope and Standards
Bradley, D. A. (2010) · Journal for Nurses in Staff Development · doi:10.1097/nnd.0b013e3181de15ea · Cited by 3 · see citing works
DISCOVER: AN INTELLIGENT DISCOVERY PROGRAMMING SYSTEM
Ramadhan, H. A. (2000) · Cybernetics & Systems · doi:10.1080/019697200124937 · Cited by 3 · see citing works

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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 · see citing works
“Several meta-analytic studies have demonstrated the effectiveness of intelligent tutoring systems to promote learning outcomes (Ma, Adesope, Nesbit, & Liu, 2014; Steenbergen-Hu & Cooper, 2014; VanLehn, 2011).”
Background
AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking
Gerlich, M. (2025) · Societies · doi:10.3390/soc15010006 · Cited by 1,248 · see citing works
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
Artificial 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 · see citing works
Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review
Kulik, J. A., & Fletcher, J. D. (2016) · Review of Educational Research · doi:10.3102/0034654315581420 · Cited by 930 · see citing works
“Finally, the meta-analysis by Ma et al. (2014) analyzed 107 findings from 73 separate reports.”
Method
Explainable Artificial Intelligence in education
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100074 · Cited by 863 · see citing works
Effects of artificial intelligence-enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom
Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023) · Computers & Education · doi:10.1016/j.compedu.2022.104684 · Cited by 605 · see citing works
Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods
Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2018.1558257 · Cited by 605 · see citing works
The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis
Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., & Reiss, K. M. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103897 · Cited by 574 · see citing works
“The assumption of a novelty effect is not in line with the result of Ma et al. (2014), who found that interventions with durations of less than four weeks denote less-positive student learning outcomes.”
BackgroundResultInfluential
Seven HCI grand challenges
Stephanidis, C., Salvendy, G., Antona, M., Chen, J. Y. C., Dong, J., Duffy, V. G., Fang, X., Fidopiastis, C., Fragomeni, G., Fu, L. P., et al. (2019) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2019.1619259 · Cited by 524 · see citing works
“ITSs provide individualized instruction and have the potential for significantly higher achievement outcomes than other modes of instruction (except small-group human tutoring and individual human tutoring when results were comparable) (Ma, Adesope, Nesbit, & Liu, 2014).”
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
A review of AI teaching and learning from 2000 to 2020
Ng, D., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. (2022) · Education and Information Technologies : Official Journal of the IFIP technical committee on Education · doi:10.1007/s10639-022-11491-w · Cited by 409 · see citing works
Differentiated Instruction in Secondary Education: A Systematic Review of Research Evidence
Smale-Jacobse, A. E., Meijer, A., Helms‐Lorenz, M., & Maulana, R. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02366 · Cited by 560 · see citing works
“Review studies show that such adaptive ICT applications may considerably improve student achievement (Ma et al., 2014; Van der Kleij et al., 2015; Kulik and Fletcher, 2016; Shute and Rahimi, 2017).”
Background
Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021
Chaudhry, M. A., & Kazim, E. (2022) · AI and Ethics · doi:10.1007/s43681-021-00074-z · Cited by 425 · see citing works
“According to Luckin [37] from University College London, ‘AI would provide a fairer, richer assessment system that would evaluate students across a longer period of time and from an evidence-based, value-added perspective’.”
BackgroundInfluential
Synthesizing Results From Empirical Research on Computer-Based Scaffolding in STEM Education
Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2017) · Review of Educational Research · doi:10.3102/0034654316670999 · Cited by 346 · see citing works
“63 in humanities and social science (Ma et al., 2014).”
BackgroundMethodInfluential
Power to the Teachers: An Exploratory Review on Artificial Intelligence in Education
Lameras, P., & Arnab, S. (2021) · Information · doi:10.3390/info13010014 · Cited by 291 · see citing works
“Ma et al. [79] conducted a meta-analysis that compared the outcomes on ITSs that were assimilated by students for developing subject domain understandings to those from non-ITS learning environments.”
Result
The interplay of learning, analytics and artificial intelligence in education: A vision for hybrid intelligence
Cukurova, M. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13514 · Cited by 302 · see citing works
The Cognitive-Affective-Social Theory of Learning in digital Environments (CASTLE)
Schneider, S., Beege, M., Nebel, S., Schnaubert, L., & Rey, G. D. (2021) · Educational Psychology Review · doi:10.1007/s10648-021-09626-5 · Cited by 351 · see citing works
When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction
Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2020.1808794 · Cited by 277 · see citing works
“For example, developers have created these systems to present content, pose questions, assign tasks, provide hints, answer questions, or suggest improvements in the behavior or attitudes of learners (Ma et al., 2014).”
Background
Inducing Self-Explanation: a Meta-Analysis
Bisra, K., Liu, Q., Nesbit, J. C., Salimi, F., & Winne, P. H. (2018) · Educational Psychology Review · doi:10.1007/s10648-018-9434-x · Cited by 278 · see citing works
“Computer-based instruction (CBI) was defined as offering response feedback with no student modeling, and intelligent tutoring systems (ITS) were defined as modeling student knowledge to adapt feedback, task selection, or prompts (Ma et al. 2014).”
Self-citation
Effect of Artificial Intelligence Tutoring vs Expert Instruction on Learning Simulated Surgical Skills Among Medical Students: A Randomized Clinical Trial
Fazlollahi, A. M., Bakhaidar, M., Alsayegh, A., Yilmaz, R., Winkler-Schwartz, A., Mirchi, N., Langleben, I., Ledwos, N., Sabbagh, A. J., Bajunaid, K., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.49008 · Cited by 285 · see citing works
“1) Sex Men 9 (39) 10 (43) 10 (42) Women 14 (61) 13 (57) 14 (58) Undergraduate medical training level Medicine Preparatorya 9 (39) 10 (43) 7 (29) First year 8 (35) 8 (35) 9 (38) Second year 6 (26) 5 (22) 8 (33) Institution McGill University 14 (61) 8 (35) 10 (42) University of Montreal 3 (13) 7 (30) 7 (29) University o…”
Developing Personalized Education: A Dynamic Framework
Tetzlaff, L., Schmiedek, F., & Brod, G. (2020) · Educational Psychology Review · doi:10.1007/s10648-020-09570-w · Cited by 272 · see citing works
“ITS are, by definition, computer programs that model learners ’ psychological states to provide personalized instruction (Ma et al. 2014).”
BackgroundInfluential
A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects
Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., Alonso, S., Melero, F. J., Molina, D., Fernández, B., et al. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02264-y · Cited by 276 · see citing works
Variability of teachers’ technology integration in the classroom: A matter of utility!
Backfisch, I., Lachner, A., Stürmer, K., & Scheiter, K. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104159 · Cited by 270 · see citing works
Effectiveness of computer-based scaffolding in the context of problem-based learning for STEM education: Bayesian meta-analysis
Kim, N. J., Belland, B. R., & Walker, A. E. (2018) · Educational Psychology Review · doi:10.1007/s10648-017-9419-1 · Cited by 220 · see citing works
“Hints are clues or suggestions to help students go forward (Melero et al. 2011).”
Background
A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda
Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, et al. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00350-5 · Cited by 235 · see citing works
“…models to offer student-centered, personalized, and self-paced learning opportunities to help students acquire knowledge (Graesser et al., 2012; Ma et al., 2014) by tailoring the instructional methods, approaches, and activities according to each individual learner (Mousavinasab et al., 2021).”
Artificial Intelligence as an Effective Classroom Assistant
du Boulay, B. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.93 · Cited by 213 · see citing works
A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home
Sepasgozar, S. M. E., Karimi, R., Farahzadi, L., Moezzi, F., Shirowzhan, S., Ebrahimzadeh, S. M., Hui, F. K. P., & Aye, L. (2020) · Applied Sciences · doi:10.3390/app10093074 · Cited by 217 · see citing works
La Inteligencia Artificial como recurso educativo durante la formación inicial del profesorado
del Puerto, D. A., & Esteban, P. G. (2022) · RIED Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.25.2.32332 · Cited by 207 · see citing works
Designing for Complementarity: Teacher and Student Needs for Orchestration Support in AI-Enhanced Classrooms
Holstein, K., McLaren, B. M., & Aleven, V. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-23204-7_14 · Cited by 189 · see citing works
Efficacy of adaptive e-learning for health professionals and students: a systematic review and meta-analysis
Fontaine, G., Cossette, S., Maheu-Cadotte, M.-A., Mailhot, T., Deschênes, M.-F., Mathieu-Dupuis, G., Côté, J., Gagnon, M.-P., & Dubé, V. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025252 · Cited by 193 · see citing works
“This is significant since, in the meta-analyses of Steenbergen-Hu and Cooper,(15) Ma et al,(18) and Kulik and Fletcher,(17) AEEs seem to be more effective in postsecondary students(17) 18 and for learning subjects related to biology, physiology and social science.(18) Moreover, previous meta-analyses focused on the ef…”
Background
Immersive, interactive virtual field trips promote science learning
Mead, C., Buxner, S. R., Bruce, G., Taylor, W. L., Semken, S. C., & Anbar, A. D. (2019) · Journal of Geoscience Education · doi:10.1080/10899995.2019.1565285 · Cited by 183 · see citing works
“Adaptive learning has been shown to be more effective than comparable nonadaptive designs (Kulik & Fletcher, 2016; Ma et al., 2014; VanLehn, 2011).”
Background
Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments
Alkhatlan, A., & Kalita, J. K. (2019) · International Journal of Computer Applications · doi:10.5120/ijca2019918451 · Cited by 166 · see citing works
“[22] was conducted in 2014 for the purpose of comparing the learning outcomes for those who learn by using ITSs and those who learn in non-ITS learning environments.”
Background
Effectiveness of Mobile Devices on Vocabulary Learning: A Meta-Analysis
Mahdi, H. S. (2017) · Journal of Educational Computing Research · doi:10.1177/0735633117698826 · Cited by 160 · see citing works
“On the other hand, a random-eﬀects model operates under the assumption that there is more than one true eﬀect and that the ESs could be diﬀerent from study to the other (Ma, Adesope, Nesbit, & Liu, 2014).”
Background
A meta-analysis of signaling principle in multimedia learning environments
Alpizar, D., Adesope, O. O., & Wong, R. M. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09748-7 · Cited by 157 · see citing works
“The use of educational videos (see Guo et al. 2014), homework assessment, tutoring programs (see Cole and Todd 2003; Ma et al. 2014), and online discussion forums (see Mazzolini and Maddison 2003; Thomas 2002) highlight how the use of technology is impacting our society today (Department of…”
BackgroundSelf-citation
Reconsidering the voice effect when learning from a virtual human
Craig, S. D., & Schroeder, N. L. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.07.003 · Cited by 151 · see citing works
Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective
Wang, H., Tlili, A., Huang, R., Cai, Z., Li, M., Cheng, Z., Yang, D., Li, M., Zhu, X., & Fei, C. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11555-x · Cited by 147 · see citing works
“…student diagnose module which collects and updates the information about students’ knowledge, skills, behaviors, responses, learning styles, etc. (Ma et al., 2014); (3) instructional module which focuses on the strategies and methods of teaching and delivering customized learning content…”
BackgroundMethodResultInfluential
The effectiveness of intelligent tutoring systems on K‐12 students' reading comprehension: A meta‐analysis
Xu, Z., Wijekumar, K. K., Ramı́rez, G., Hu, X., & Irey, R. C. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12758 · Cited by 145 · see citing works
Integration of educational technology during the Covid-19 pandemic: An analysis of teacher and student receptions
Christopoulos, A., & Sprangers, P. (2021) · Cogent Education · doi:10.1080/2331186x.2021.1964690 · Cited by 145 · see citing works
“Computer-based instruction has yielded positive results in areas such as student motivation (Turk & Akyuz, 2016), satisfaction (Özyurt et al., 2014), performance (Ma et al., 2014), and learning outcomes (Van der Kleij et al., 2015).”
Background
The Normalized Programming State Model
Carter, A. S., Hundhausen, C. D., & Adesope, O. (2015) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2787622.2787710 · Cited by 144 · see citing works
Self-citation
An investigation of barriers to Hong Kong K-12 schools incorporating Artificial Intelligence in education
Wang, T., & Cheng, E. C. K. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100031 · Cited by 141 · see citing works
Looking Beyond the Hype: Understanding the Effects of AI on Learning
Bauer, E., Greiff, S., Graesser, A. C., Scheiter, K., & Sailer, M. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10020-8 · Cited by 153 · see citing works
“These examples illustrate how LLMs can be integrated with ITS components and pedagogical principles, but also highlight challenges in balancing flexibility, content quality, pedagogical quality, and personalized instruction.”
Influential
OnTask: Delivering Data-Informed, Personalized Learning Support Actions
Pardo, A., Bartimote-Aufflick, K., Shum, S. B., Dawson, S., Gao, J. P., Gašević, D., Leichtweis, S., Liu, D., Martínez‐Maldonado, R., Mirriahi, N., et al. (2018) · Journal of Learning Analytics · doi:10.18608/jla.2018.53.15 · Cited by 140 · see citing works
Learning fractions with and without educational technology: What matters for high-achieving and low-achieving students?
Reinhold, F., Hoch, S., Werner, B., Richter-Gebert, J., & Reiss, K. M. (2019) · Learning and Instruction · doi:10.1016/j.learninstruc.2019.101264 · Cited by 156 · see citing works
“In addition, these differences between low-achieving and highachieving students lead to new questions that need further investigation and cannot be answered based on our studies: as appropriate feedback as well as adaptive task difficulty have shown to be beneficial for the learning process in different studies (Arroy…”
BackgroundResultInfluential
Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view
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
Modelling Human Teaching Tactics and Strategies for Tutoring Systems: 14 Years On
du Boulay, B., & Luckin, R. (2015) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0053-0 · Cited by 124 · see citing works
“In terms of understanding recent progress, largely in the cognitive rather than the motivational aspects of modelling teaching, we note a meta-analysis of the learning outcomes of using Intelligent Tutoring Systems (Ma et al. 2014).”
Background
Learner modelling: systematic review of the literature from the last 5 years
Abyaa, A., Idrissi, M. K., & Bennani, S. (2019) · Educational technology research and development · doi:10.1007/s11423-018-09644-1 · Cited by 108 · see citing works
A Systematic Literature Review of Intelligent Tutoring Systems With Dialogue in Natural Language
Paladines, J., & Ramírez, J. (2020) · IEEE Access · doi:10.1109/access.2020.3021383 · Cited by 123 · see citing works
“As a result, we have considered the ITS types mentioned in [4]: model-tracing tutors (MTTs), constraint-based modeling (CBM), Bayesian network modeling (BNM), and expectation and misconceptions tailoring (EMT).”
Artificial Intelligence in Education: Mathematics Teachers’ Perspectives, Practices and Challenges
Tashtoush, M. A., Wardat, Y., AlAli, R. M., & Saleh, S. G. (2024) · Iraqi Journal for Computer Science and Mathematics · doi:10.52866/ijcsm.2024.05.01.004 · Cited by 161 · see citing works
“A literature review on AI in education reveals that it has been extensively implemented in various sectors, including higher education [7], K-12 education [1], student assessment [15], robotics [16], data mining [17], and intelligent tutoring systems (ITS) [18].”
Background
Authoring Tools for Designing Intelligent Tutoring Systems: a Systematic Review of the Literature
Dermeval, D., Paiva, R., Bittencourt, I. I., Vassileva, J., & Borges, D. C. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0157-9 · Cited by 126 · see citing works
“As reported by du Boulay (2016), there have been a number of recent positive reviews in support of the effectiveness of ITSs (Kulik and Fletcher 2016; Ma et al. 2014; Steenbergen-Hu and Cooper 2013, 2014; VanLehn 2011).”
BackgroundMethodResult
The Science of Workplace Instruction: Learning and Development Applied to Work
Kraiger, K., & Ford, J. K. (2020) · Annual Review of Organizational Psychology and Organizational Behavior · doi:10.1146/annurev-orgpsych-012420-060109 · Cited by 137 · see citing works
Enhancing problem-solving skills with smart personal assistant technology
Winkler, R., Söllner, M., & Leimeister, J. M. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104148 · Cited by 134 · see citing works
“3 the last five to ten years, a lot of researchers were able to show that step-based computer tutoring systems were nearly as effective as average human tutors and more effective compared to no tutoring condition and teacher-led classroom instruction (Ma et al. 2014; Nesbit et al. 2014; Vanlehn 2011).”
Background
Critically Reviewing GraphoGame Across the World: Recommendations and Cautions for Research and Implementation of Computer‐Assisted Instruction for Word‐Reading Acquisition
McTigue, E. M., Solheim, O. J., Zimmer, W. K., & Uppstad, P. H. (2020) · Reading Research Quarterly · doi:10.1002/rrq.256 · Cited by 113 · see citing works
Scaffolding Computational Thinking With ChatGPT
Liao, J., Zhong, L., Zhe, L., Xu, H., Liu, M., & Xie, T. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3392896 · Cited by 113 · see citing works
“Melero et al. [38] and Ma et al. [39] proposed the concept of systematic instruction scaffolding as the systematic use of technological tools or environmental designs, such as learning management systems and intelligent tutoring systems including computers or AI.”
Background
Artificial intelligence as a double-edged sword: Wielding the POWER principles to maximize its positive effects and minimize its negative effects
Chen, J. J., & Lin, J. C. (2023) · Contemporary Issues in Early Childhood · doi:10.1177/14639491231169813 · Cited by 106 · see citing works
Development and Adoption of an Adaptive Learning System: Reflections and Lessons Learned
Khosravi, H., Sadiq, S., & Gašević, D. (2020) · Technical Symposium on Computer Science Education · doi:10.1145/3328778.3366900 · Cited by 96 · see citing works
“using an ITS or individualised human tutoring [33].”
Method
Incorporation of Artificial Intelligence Tutoring Techniques in Mathematics
Pappas, M. A., & Drigas, A. S. (2016) · International Journal of Engineering Pedagogy (iJEP) · doi:10.3991/ijep.v6i4.6063 · Cited by 103 · see citing works
“Each constraint consists of three components: 1) a relevance condition indicating when the constraint is applicable, 2) a satisfaction condition and 3) a feedback message for student’s errors (Ma et al, 2014)”
Background
Evaluating Artificial Intelligence in Education for Next Generation
Joshi, S., Rambola, R. K., & Churi, P. P. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1714/1/012039 · Cited by 139 · see citing works
“Some focus on subjects with mathematical rules, some teach reading or writing, and still, others try to teach general topic skills, such as self-paced reading strategies [24] [25] [29].”
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Designing Adaptive Instruction for Teams: a Meta-Analysis
Sottilare, R. A., Burke, C. S., Eduardo Salas, A., Sinatra, A. M., Johnston, J. H., & Gilbert, S. B. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0146-z · Cited by 101 · see citing works
“A recent review of artificial intelligence in education (AIED) meta-analyses by du Boulay (2016) noted investigations by VanLehn (2011), Ma et al. (2014), Kulik and Fletcher (2015), Steenbergen-Hu and Cooper (2013, 2014), and Pane et al. (2014).”
Background
ElectronixTutor: an intelligent tutoring system with multiple learning resources for electronics
Graesser, A. C., Hu, X., Nye, B. D., VanLehn, K. A., Kumar, R. V., Heffernan, C., Heffernan, N. T., Woolf, B. P., Olney, A. M., Rus, V., et al. (2018) · International Journal of STEM Education · doi:10.1186/s40594-018-0110-y · Cited by 97 · see citing works
“…meta-analyses show positive effect sizes that vary from d = 0.05 (Dynarsky et al. 2007; Steenbergen-Hu and Cooper 2014) to d = 1.08 (Dodds and Fletcher 2004), but most hover between d = 0.40 and d = 0.80 (Kulik and Fletcher 2015; Ma et al. 2014; Steenbergen-Hu and Cooper 2013; VanLehn 2011).”
BackgroundInfluential
The Efficacy of Artificial Intelligence-Enabled Adaptive Learning Systems From 2010 to 2022 on Learner Outcomes: A Meta-Analysis
Wang, X., Huang, R., Sommer, M., Pei, B., Shidfar, P., Rehman, M. S., Ritzhaupt, A. D., & Martin, F. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241240459 · Cited by 144 · see citing works
“Our ﬁ ndings are different from the meta-analysis conducted Ma et al. (2014) that found large effect size in learning humanities.”
Influential
On the promise of personalized learning for educational equity
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“Personalized learning does ﬁ nd support in the recent literature, with some studies reporting that such approaches, especially those supported by adaptive learning technologies, are more effective in raising student achievement than traditional non-adaptive instruction 34,37,39,42,43 .”
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Challenges and opportunities for classroom-based formative assessment and AI: a perspective article
Hopfenbeck, T. N., Zhang, Z., Sun, S. Z., Robertson, P., & McGrane, J. A. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1270700 · Cited by 125 · see citing works
“…and intelligent tutoring systems enables every student’s attainment to be individually and more precisely assessed, which facilitates more appropriate and targeted feedback based on their individual learning stage and trajectory (Ma et al., 2014; Tomasik et al., 2018; Mousavinasab et al., 2021).”
Background
Scaffolding for Optimal Challenge in K–12 Problem-Based Learning
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An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning
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“can be as effective as human tutoring [63], and that use of an ITS in a study was associated with greater achievement in comparison with teacher-led large-group instruction, non-ITS computer-based instruction, and textbooks or workbooks [41].”
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A feedback model for data-rich learning experiences
Pardo, A. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2017.1356905 · Cited by 82 · see citing works
“Recent studies have shown the positive effect of these systems with respect to learning outcomes (Ma et al. 2014) for which feedback is an essential element.”
Background
RiPPLE: A Crowdsourced Adaptive Platform for Recommendation of Learning Activities
Khosravi, H., Kitto, K., & Williams, J. J. (2019) · Journal of Learning Analytics · doi:10.18608/jla.2019.63.12 · Cited by 81 · see citing works
“44 and d = 54 which gives support to the claim that using RiPPLE does indeed provide measurable learning gains; however, the effect is considered to be at a medium level (Ma et al., 2014) and less effective compared to many of the studies that were discussed in Section 2.”
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Examining the Effects of Artificial Intelligence on Elementary Students’ Mathematics Achievement: A Meta-Analysis
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Effects of a Teacher Dashboard for an Intelligent Tutoring System on Teacher Knowledge, Lesson Planning, Lessons and Student Learning
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“ITSs have been shown to enhance student learning [8, 11, 19].”
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Do published studies yield larger effect sizes than unpublished studies in education and special education? A meta-review
Chow, J. C., & Ekholm, E. (2018) · Educational Psychology Review · doi:10.1007/s10648-018-9437-7 · Cited by 80 · see citing works
A meta-analysis of the effectiveness of ALEKS on learning
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“This is likely the reason, why empirical studies using ALEKS were excluded from recent ITS meta-analyses (Kulik & Fletcher, 2015; Ma, Adesope, Nesbit, & Liu, 2014; Steenbergen-Hu & Cooper, 2013; VanLehn, 2011).”
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Escape from the Skinner Box: The case for contemporary intelligent learning environments
du Boulay, B. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12860 · Cited by 80 · see citing works
“…of formal education, seven meta-reviews have compared the quality of the learning outcomes with ILES as compared to using more traditional methods (Kulik & Fletcher, 2016; Ma, Adesope, Nesbit, & Liu, 2014; Nesbit, Adesope, Liu, & Ma, 2014; Steenbergen-Hu & Cooper, 2013, 2014; VanLehn, 2011).”
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Teachers’ technology use for teaching: Comparing two explanatory mechanisms
Backfisch, I., Scherer, R., Siddiq, F., Lachner, A., & Scheiter, K. (2021) · Teaching and Teacher Education · doi:10.1016/j.tate.2021.103390 · Cited by 83 · see citing works
Evaluating Recent Advances in Affective Intelligent Tutoring Systems: A Scoping Review of Educational Impacts and Future Prospects
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“However, they note that [93] found that studies using ITSs in the humanities and social sciences had a significantly higher weighted mean effect size.”
Factors Influencing Students' Behavioral Intention to Continue Artificial Intelligence Learning
Chai, C. S., Lin, P.-Y., Jong, M. S.-Y., Dai, Y., Chiu, T. K. F., & Huang, B. (2020) · 2020 International Symposium on Educational Technology (ISET) · doi:10.1109/iset49818.2020.00040 · Cited by 106 · see citing works
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“For example, Intelligent Tutoring Systems (ITS) have been shown to positively affect student learning outcomes when compared to conventional educational experiences [33, 34, 51].”
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AI and the Future of Learning: Expert Panel Report
Roschelle, J., Lester, J., & Fusco, J. (2020) · doi:10.51388/20.500.12265/106 · Cited by 76 · see citing works
“More generally, meta-analyses of ITS approaches have found positive impacts on student learning (Van Lehn, 2011; Ma et al., 2014; Kulik & Fletcher, 2016).”
Background
The effect of chatbots on learning: a meta-analysis of empirical research
Alemdağ, E. (2023) · Journal of Research on Technology in Education · doi:10.1080/15391523.2023.2255698 · Cited by 78 · see citing works
“For example, Ma et al. (2014) first defined ITSs as a computer program that provides tutoring functions, creates multidimensional models for each student’s psychological state, and adapts their functions based on these models.”
Influential
A survey of adaptive context-aware learning environments
Hasanov, A., Laine, T. H., & Chung, T. (2019) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-190534 · Cited by 75 · see citing works
“Several studies that were conducted in the field of adaptive and context-aware learning show positive effects of the usage of ACALEs on learning and teaching across domains and levels, including but not limited to effectiveness, efficiency, interaction, support, immersion, and motivation [40,44,56,57,78,79,87].”
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Educational Psychology Is Evolving to Accommodate Technology, Multiple Disciplines, and Twenty-First-Century Skills
Graesser, A. C., Sabatini, J., & Li, H. (2021) · Annual Review of Psychology · doi:10.1146/annurev-psych-020821-113042 · Cited by 88 · see citing works
Twenty‐first century adaptive teaching and individualized learning operationalized as specific blends of student‐centered instructional events: A systematic review and meta‐analysis
Bernard, R. M., Borokhovski, E., Schmid, R. F., Waddington, D. I., & Pickup, D. (2019) · Campbell Systematic Reviews · doi:10.1002/cl2.1017 · Cited by 73 · see citing works
An Interaction Design for Machine Teaching to Develop AI Tutors
Weitekamp, D. P., Harpstead, E., & Koedinger, K. R. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376226 · Cited by 70 · see citing works
“In several studies ITSs have been shown to beneft learners when used alone or in combination with traditional instruction [7, 24, 25, 12].”
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Implementation of technology-supported personalized learning—its impact on instructional quality
Schmid, R., Pauli, C., Стеблер, Р., Reusser, K., & Petko, D. (2022) · The Journal of Educational Research · doi:10.1080/00220671.2022.2089086 · Cited by 91 · see citing works
“Meta-analyses of intelligent tutoring systems (ITS) indicate positive effects on student outcomes when compared to non-individualized forms of teacher-guided instruction or non-ITS educational software (Kulik & Fletcher, 2016; Ma et al., 2014).”
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Measuring the effect of ARS on academic performance: A global meta-analysis
Castillo-Manzano, J. I., Castro‐Nuño, M., Valpuesta, L. L., Díaz, M. T. S., & Ovando, R. Y. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.02.007 · Cited by 75 · see citing works
An intelligent tutoring system architecture based on fuzzy neural network (FNN) for special education of learning disabled learners
Dutt, S., Ahuja, N. J., & Kumar, M. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10713-x · Cited by 68 · see citing works
“ITS application provides comfort to the learner to learn and perform with ability, availability of time and interest, and that too without the human teacher intervention (Ma et al., 2014).”
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Do Innovative Teachers use AI-powered Tools More Interactively? A Study in the Context of Diffusion of Innovation Theory
ÜZÜMCÜ, Ö., & AÇILMIŞ, H. (2023) · Technology Knowledge and Learning · doi:10.1007/s10758-023-09687-1 · Cited by 68 · see citing works
“It is also defined as operating like human intelligence for learning, cognitive problem-solving, and model recognition (Ma et al., 2014).”
A Bayesian Network Meta-Analysis to Synthesize the Influence of Contexts of Scaffolding Use on Cognitive Outcomes in STEM Education
Belland, B., Walker, A., & Kim, N. (2017) · Review of Educational Research · doi:10.3102/0034654317723009 · Cited by 60 · see citing works
“Meta-analyses have also been performed among specific subtypes of scaffolding, such as that in intelligent tutoring systems (Ma et al., 2014; Steenbergen-Hu & Cooper, 2013, 2014; VanLehn, 2011), dynamic assessment (Swanson & Lussier, 2001), and scaffolding for students with learning disabilities (Swanson & Deshler, 20…”
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Leveraging Big Data to Help Each Learner and Accelerate Learning Science
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Providing feedback on computer-based algebra homework in middle-school classrooms
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Intelligent tutoring systems work as a math gap reducer in 6th grade after-school program
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Introducing <scp>AnatomyGPT</scp>: A customized artificial intelligence application for anatomical sciences education
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“An earlier meta-analysis involving 107 effect sizes found that intelligent tutoring systems improve student achievement with effect sizes ranging from 0.35 to 0.57 depending on the control comparison (Ma et al., 2014).”
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The potential of learning with (and not from) artificial intelligence in education
Chichekian, T., & Benteux, B. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.903051 · Cited by 75 · see citing works
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Augmented Reality and Artificial Intelligence in Education: Toward Immersive Intelligent Tutoring Systems
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How Effective are Intelligent Tutoring Systems in Computer Science Education?
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Self-citation

Authors: Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q.
Affiliations: Simon Fraser University (CA)
Washington State University (US)
ORCID: Ma, Wenting
Adesope, Olusola O.
Nesbit, John C.
Venue: Journal of Educational Psychology
Publisher: American Psychological Association (APA)
Published: 2014-11
Volume: 106
Issue: 4
Pages: 901-918
ISSN: 0022-0663 (ISSN-L)
1939-2176
DOI: 10.1037/a0037123
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

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Intelligent tutoring systems and learning outcomes: A meta-analysis

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