Cited by 119Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning MethodsArtificial Intelligence in Education

The DOI matches its Crossref record; 43 of the 45 references it cites were found in a registry or an index.

Preparing students for future learning with Teachable Agents

Chin, Doris B., Dohmen, Ilsa M., Cheng, Britte H., Oppezzo, Marily A., Chase, Catherine C., Schwartz, Daniel L.·2010·Educational Technology Research and Development

DOI 10.1007/s11423-010-9154-5

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/s11423-010-9154-5

Cites

43 of 43 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Democracy and Education
(2015) · Routledge eBooks · doi:10.4324/9781315712383-22 · Cited by 10,894
What Video Games Have to Teach Us about Learning and Literacy
(2004) · Education + Training · doi:10.1108/et.2004.00446dae.002 · Cited by 6,674
What video games have to teach us about learning and literacy
Gee, J. P. (2003) · Computers in Entertainment · doi:10.1145/950566.950595 · Cited by 5,735
Learning How to Learn
Novak, J. D., Gowin, D. B., & Kahle, J. B. (1984) · doi:10.1017/cbo9781139173469 · Cited by 5,199
Reciprocal Teaching of Comprehension-Fostering and Comprehension-Monitoring Activities
Palinscar, A. S., & Brown, A. L. (1984) · Cognition and Instruction · doi:10.1207/s1532690xci0102_1 · Cited by 5,186
Qualitative Process Theory
Forbus, K. D. (1984) · Artificial Intelligence · doi:10.1016/0004-3702(84)90038-9 · Cited by 2,491
COGNITIVE APPRENTICESHIP: MAKING THINKING VISIBLE
Collins, A., Brown, J. S., & Holum, A. (1991) · Cited by 1,516
Handbook of Metacognition in Education
Hacker, D. J., Dunlosky, J., & Graesser, A. C. (2009) · doi:10.4324/9780203876428 · Cited by 1,509
Foreign-language experience in infancy: effects of short-term exposure and social interaction on phonetic learning
Kuhl, P. K., Tsao, F.-M., & Liu, H.-M. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1532872100 · Cited by 1,237 · cited 1×
“For example, one outstanding question involves the value of videogames and other interactive media frequently found outside of school (e.g., Barron et al. 2009; Gee 2003; Kuhl et al. 2003; Ito 2009; Stevens et al. 2008).”
Background
Learning With Concept and Knowledge Maps: A Meta-Analysis
Nesbit, J. C., & Adesope, O. O. (2006) · Review of Educational Research · doi:10.3102/00346543076003413 · Cited by 932 · cited 1×
“Concept maps have proven to be a useful paper-and-pencil technology for improving knowledge retention and integration (for reviews, see Hilbert and Renkl 2008; Horton et al. 1993; Nesbit and Adesope 2006; O’Donnell et al. 2002).”
Background

Show all 45 references

Research works printed but not linked: 2

Gopnik, A., & Schulz, L. (Eds.). (2007). Causal learning: Psychology, philosophy, computation. New York: Oxford University Press.
Ito, M. (2009). Hanging out, messing around, geeking out: Kids living and learning with new media. Cambridge, MA: MIT Press. · DOI

Sorted by citations

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
“Although AI techniques displayed and predicted smart computation in the education domain, they generally fail to bring “added-value” to large-scale students because of the concern of costs, and the mainstream is still occupied by “basic value” [38].”
BackgroundMethod
AI technologies for education: Recent research & future directions
Zhang, K., & Aslan, A. B. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100025 · Cited by 923
Intelligent tutoring systems and learning outcomes: A meta-analysis
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014) · Journal of Educational Psychology · doi:10.1037/a0037123 · Cited by 840
Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review
Dai, C., & Ke, F. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100087 · Cited by 357
Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-analysis
García‐Martínez, I., Fernández‐Batanero, J. M., Cerero, J. F., & León, S. P. (2023) · Journal of New Approaches in Educational Research · doi:10.7821/naer.2023.1.1240 · Cited by 386
Parents as Learning Partners in the Development of Technological Fluency
Barron, B., Martin, C. K., Takeuchi, L., & Fithian, R. (2009) · International Journal of Learning and Media · doi:10.1162/ijlm.2009.0021 · Cited by 230
Toward a taxonomy of adaptivity for learning
Plass, J. L., & Pawar, S. S. (2020) · Journal of Research on Technology in Education · doi:10.1080/15391523.2020.1719943 · Cited by 225
C2STEM: a System for Synergistic Learning of Physics and Computational Thinking
Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., Blair, K. P., Chin, D., Conlin, L., Basu, S., et al. (2020) · Journal of Science Education and Technology · doi:10.1007/s10956-019-09804-9 · Cited by 122
Self-citation
Learning as a Generative Activity: Eight Learning Strategies that Promote Understanding
Fiorella, L., & Mayer, R. E. (2015) · Cited by 109
A teachable-agent-based game affording collaboration and competition: evaluating math comprehension and motivation
Pareto, L., Haake, M., Lindström, P., Sjödén, B., & Gulz, A. (2012) · Educational Technology Research and Development · doi:10.1007/s11423-012-9246-5 · Cited by 107
Artificial Intelligence and the Disruption of Higher Education: Strategies for Integrations across Disciplines
Hutson, J., Jeevanjee, T., Graaf, V. V., Lively, J., Weber, J., Weir, G., Arnone, K., Carnes, G., Vosevich, K., Plate, D., et al. (2022) · Creative Education · doi:10.4236/ce.2022.1312253 · Cited by 153
Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education
Jin, H., Lee, S., Shin, H., & Kim, J. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642349 · Cited by 99
Assessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students.
Sánchez‐Prieto, J. C., Cruz-Benito, J., Therón, R., & Peñalvo, F. J. G. (2020) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2020.11.009 · Cited by 106
Potential Risks of Artificial Intelligence Integration into School Education: A Systematic Review
Karan, B., & Angadi, G. R. (2023) · Bulletin of Science Technology & Society · doi:10.1177/02704676231224705 · Cited by 122
Assessing Algorithmic and Computational Thinking in K-12: Lessons from a Middle School Classroom
Grover, S. (2017) · Springer eBooks · doi:10.1007/978-3-319-52691-1_17 · Cited by 97
Learning by explaining orally or in written form? Text complexity matters
Jacob, L., Lachner, A., & Scheiter, K. (2020) · Learning and Instruction · doi:10.1016/j.learninstruc.2020.101344 · Cited by 82
Learning by Teaching Human Pupils and Teachable Agents: The Importance of Recursive Feedback
Okita, S. Y., & Schwartz, D. (2013) · Journal of the Learning Sciences · doi:10.1080/10508406.2013.807263 · Cited by 86
Self-citation
Timing matters! Explaining between study phases enhances students’ learning.
Lachner, A., Backfisch, I., Hoogerheide, V., van Gog, T., & Renkl, A. (2019) · Journal of Educational Psychology · doi:10.1037/edu0000396 · Cited by 76
Learning-by-Teaching Without Audience Presence or Interaction: When and Why Does it Work?
Lachner, A., Hoogerheide, V., van Gog, T., & Renkl, A. (2022) · Educational Psychology Review · doi:10.1007/s10648-021-09643-4 · Cited by 77
Automated, adaptive guidance for K-12 education
Gerard, L. F., Matuk, C. F., McElhaney, K. W., & Linn, M. C. (2015) · Educational Research Review · doi:10.1016/j.edurev.2015.04.001 · Cited by 83
Automatically Detecting a Student's Preparation for Future Learning: Help Use is Key
Baker, R. S., Gowda, S. M., & Corbett, A. T. (2011) · Educational Data Mining · Cited by 64
Providing Written or Oral Explanations? Differential Effects of the Modality of Explaining on Students' Conceptual Learning and Transfer
Lachner, A., Ly, K.-T., & Nückles, M. (2017) · The Journal of Experimental Education · doi:10.1080/00220973.2017.1363691 · Cited by 70
Model construction as a learning activity: a design space and review
VanLehn, K. A. (2013) · Interactive Learning Environments · doi:10.1080/10494820.2013.803125 · Cited by 61
Young Children Can Learn Scientific Reasoning with Teachable Agents
Chin, D. B., Dohmen, I. M., & Schwartz, D. L. (2013) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2013.24 · Cited by 54
Self-citation
Systematically reviewing the potential of concept mapping technologies to promote self-regulated learning in primary and secondary science education
Stevenson, M. P., Hartmeyer, R., & Bentsen, P. (2017) · Educational Research Review · doi:10.1016/j.edurev.2017.02.002 · Cited by 68
Reinforcing Math Knowledge by Immersing Students in a Simulated Learning-By-Teaching Experience
Lenat, D. B., & Durlach, P. J. (2014) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-014-0016-x · Cited by 53
Measuring Students’ Acceptance to AI-Driven Assessment in eLearning: Proposing a First TAM-Based Research Model
Cruz-Benito, J., Sánchez‐Prieto, J. C., Therón, R., & García‐Peñalvo, F. J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21814-0_2 · Cited by 53
A systematic review of concept mapping-based formative assessment processes in primary and secondary science education
Hartmeyer, R., Stevenson, M. P., & Bentsen, P. (2017) · Assessment in Education Principles Policy and Practice · doi:10.1080/0969594x.2017.1377685 · Cited by 44
Learning by teaching with ChatGPT : The effect of teachable ChatGPT agent on programming education
Chen, A., Wei, Y., Le, H., & Zhang, Y. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.70001 · Cited by 65
A pragmatic perspective on visual representation and creative thinking
Martin, L., & Schwartz, D. L. (2014) · Visual Studies · doi:10.1080/1472586x.2014.862997 · Cited by 35
Self-citation
The Effect of Highly Scaffolded Versus General Instruction on Students’ Exploratory Behavior and Arousal
Blikstein, P., Gomes, J. S., Akiba, H. T., & Schneider, B. (2016) · Technology Knowledge and Learning · doi:10.1007/s10758-016-9291-y · Cited by 33
AI in Education: Bridging the Divide or Widening the Gap? Exploring Equity, Opportunities, and Challenges in the Digital Age
Li, H. (2023) · Advances in Education Humanities and Social Science Research · doi:10.56028/aehssr.8.1.355.2023 · Cited by 32
Studying the Effect of Tutor Learning Using a Teachable Agent that Asks the Student Tutor for Explanations
Matsuda, N., Cohen, W. W., Koedinger, K. R., Keiser, V., Raizada, R., Yarzebinski, E., Watson, S., & Stylianides, G. J. (2012) · IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning (DIGITEL) · doi:10.1109/digitel.2012.12 · Cited by 32
Epistemic gameplay and discovery in computational model-based inquiry activities
Wilkerson, M. H., Shareff, R. L., Laina, V., & Gravel, B. (2017) · Instructional Science · doi:10.1007/s11251-017-9430-4 · Cited by 35
How to Measure Teachers' Acceptance of AI-driven Assessment in eLearning
Sánchez‐Prieto, J. C., Cruz-Benito, J., Therón, R., & García‐Peñalvo, F. J. (2019) · Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21) · doi:10.1145/3362789.3362918 · Cited by 31
How teachable agents influence students’ responses to critical constructive feedback
Silvervarg, A., Wolf, R. C., Blair, K. P., Haake, M., & Gulz, A. (2020) · Journal of Research on Technology in Education · doi:10.1080/15391523.2020.1784812 · Cited by 29
Towards Automatically Detecting Whether Student Learning Is Shallow
Baker, R. S., Gowda, S. M., Corbett, A. T., & Ocumpaugh, J. L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-30950-2_57 · Cited by 29
Learning from the folly of others: Learning to self-correct by monitoring the reasoning of virtual characters in a computer-supported mathematics learning environment
Okita, S. Y. (2013) · Computers & Education · doi:10.1016/j.compedu.2013.09.018 · Cited by 32
Science Education
Linn, M. C., Gerard, L. F., Matuk, C. F., & McElhaney, K. W. (2016) · Review of Research in Education · doi:10.3102/0091732x16680788 · Cited by 36
Evaluation of a meta-tutor for constructing models of dynamic systems
Zhang, L., VanLehn, K. A., Girard, S., Burleson, W., Chavez-Echeagaray, M. E., Gonzalez-Sanchez, J., & Hidalgo-Pontet, Y. (2014) · Computers & Education · doi:10.1016/j.compedu.2014.02.015 · Cited by 27
Curiosity Notebook: The Design of a Research Platform for Learning by Teaching
Lee, K. J., Chauhan, A., Goh, J., Nilsen, E. S., & Law, E. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479538 · Cited by 25
Examining the effects of a virtual character on learning and engagement in serious games
Gamage, V., & Ennis, C. (2018) · Motion in Games · doi:10.1145/3274247.3274499 · Cited by 25
Peer-mentors Reflect on the Benefits of Mentoring: An Autoethography
Booth, S., Merga, M. K., & Roni, S. M. (2016) · International journal of doctoral studies · doi:10.28945/3603 · Cited by 22
Integrating Digital and Mathematical Practices across Contexts: A Manifesto for Mobile Learning
White, T., Booker, A., Ching, C. C., & Martin, L. (2011) · International Journal of Learning and Media · doi:10.1162/ijlm_a_00076 · Cited by 22
Embodied mixed reality with passive haptics in STEM education: randomized control study with chemistry titration
Johnson‐Glenberg, M. C., Yu, C. S. P., Liu, F., Amador, C., Bao, Y., Yu, S., & LiKamWa, R. (2023) · Frontiers in Virtual Reality · doi:10.3389/frvir.2023.1047833 · Cited by 21
Effects of Playing an Educational Math Game That Incorporates Learning by Teaching
Fiorella, L., Kuhlmann, S. L., & Vogel-Walcutt, J. J. (2018) · Journal of Educational Computing Research · doi:10.1177/0735633118797133 · Cited by 23
Learning-by-Teaching: Designing Teachable Agents with Intrinsic Motivation
Zhao, G., Ailiya, & Shen, Z. (2012) · Cited by 22
Scaffolding mentalizing via a play-&-learn game for preschoolers
Haake, M., Axelsson, A., Clausen-Bruun, M., & Gulz, A. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.09.003 · Cited by 19
Learning Science by Constructing Models: Can Dragoon Increase Learning without Increasing the Time Required?
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
Predicting Future Learning Better Using Quantitative Analysis of Moment-by-Moment Learning.
Hershkovitz, A., Baker, R. S., Gowda, S. M., & Corbett, A. T. (2013) · Cited by 18
AI-based feedback tools in education: A comprehensive bibliometric analysis study
Donmez, M. (2024) · International Journal of Assessment Tools in Education · doi:10.21449/ijate.1467476 · Cited by 33
Towards Automatically Detecting Whether Student Learning is Shallow
Gowda, S. M., Baker, R. S., Corbett, A. T., & Rossi, L. M. (2013) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-013-0006-4 · Cited by 17
Supporting Early Math : Rationales and Requirements for High Quality Software
Husain, L., Gulz, A., & Haake, M. (2015) · DiVA (Linkoping University) · Cited by 15
The Future of Interaction: Augmented Reality, Holography and Artificial Intelligence in Early Childhood Science Education
Li, X., & Taber, K. S. (2022) · Lecture notes in educational technology · doi:10.1007/978-981-19-0568-1_18 · Cited by 15
Conversational Intelligent Tutoring Systems for Online Learning: What do Students and Tutors Say?
Ji, S., & Yuan, T. (2022) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon52537.2022.9766567 · Cited by 14
Transferring Teaching to Testing – An Unexplored Aspect of Teachable Agents
Sjödén, B., Tärning, B., Pareto, L., & Gulz, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21869-9_44 · Cited by 14
A Behavior Change Perspective on Self-Regulated Learning with Teachable Agents
Oppezzo, M., & Schwartz, D. L. (2013) · Springer international handbooks of education · doi:10.1007/978-1-4419-5546-3_31 · Cited by 14
Self-citation
Student Modeling in Intelligent Tutoring Systems
Gong, Y. (2014) · Digital WPI · Cited by 13
Assessment of Robust Learning with Educational Data Mining.
Baker, R. S., & Corbett, A. T. (2014) · Research & Practice in Assessment · Cited by 12
Modeling Learner’s Cognitive and Metacognitive Strategies in an Open-Ended Learning Environment
Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2011) · National Conference on Artificial Intelligence · Cited by 12
Steps towards a Challenging Teachable Agent
Silvervarg, A., Kirkegaard, C., Nirme, J., Haake, M., & Gulz, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09767-1_52 · Cited by 11
Introducing a Challenging Teachable Agent
Kirkegaard, C., Gulz, A., & Silvervarg, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-07482-5_6 · Cited by 10
An Overview of Artificial Intelligence in Education
Zhou, M. Y., & Lawless, W. F. (2014) · Advances in information quality and management · doi:10.4018/978-1-4666-5888-2.ch237 · Cited by 10
Demystifying Robots in the Co-Design of a Tutee Robot with Primary School Children
Barendregt, W., Ekström, S., Kiesewetter, S., Pareto, L., & Serholt, S. (2020) · Interaction design & architecture(s)/ID&A Interaction design & architecture(s) · doi:10.55612/s-5002-044-006 · Cited by 10
Designing a Teachable Agent System for Mathematics Learning
Song, D. (2017) · Contemporary Educational Technology · doi:10.30935/cedtech/6194 · Cited by 10
Affective Teachable Agent in VLE: A Goal Oriented Approach
Ailiya, Shen, Z., & Miao, C. (2011) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2011.38 · Cited by 10
Teaching a Conversational Agent using Natural Language: Effect on Learning and Engagement
Love, R., Law, E., Cohen, P. R., & Kulić, D. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00461-1 · Cited by 16
Deciphering the Role of Artificial Intelligence in Health Care, Learning and Development
Varghese, R., Deshpande, A., Digholkar, G., & Kumar, D. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80455-662-720230010 · Cited by 13
Generalizing automated detection of the robustness of student learning in an intelligent tutor for genetics.
Baker, R. S., Corbett, A. T., & Gowda, S. M. (2013) · Journal of Educational Psychology · doi:10.1037/a0033216 · Cited by 9
Efektivitas Penggunaan Video dengan Pengayaan Tokoh dan Animasi terhadap Pemahaman Konseptual Siswa
Fauziyah, E. F., Praherdhiono, H., & Ulfa, S. U. (2020) · JKTP Jurnal Kajian Teknologi Pendidikan · doi:10.17977/um038v3i42020p448 · Cited by 9
Jessy: A Conversational Assistant for Tutoring Digital Board Games
Allameh, M., & Zaman, L. (2021) · Annual Symposium on Computer-Human Interaction in Play · doi:10.1145/3450337.3483496 · Cited by 8
Utilization of Artificial Intelligence in Education: A Perspective on Learning Strategies
Yalçınalp, S., Türkoğlu, H., Koç, S. E., & Ersoy, H. (2024) · Artificial intelligence · doi:10.5772/intechopen.1005086 · Cited by 12
Inter-Identity Technologies for Learning.
Lindgren, R., & Pea, R. D. (2012) · Proceedings. · Cited by 7
Connection, Translation, Off-Loading, and Monitoring: A Framework for Characterizing the Pedagogical Functions of Educational Technologies
Martin, L. (2012) · Technology Knowledge and Learning · doi:10.1007/s10758-012-9193-6 · Cited by 7
APRENDER ENSINANDO: O QUE DIZEM AS PESQUISAS SOBRE O TEMA
Darroz, L. M., Nicolodi, J. C., & da Rosa, C. T. W. (2021) · Revista da FAEEBA- Educação e Contemporaneidade · doi:10.21879/faeeba2358-0194.2021.v30.n62.p223-242 · Cited by 6
Impact of Prior Knowledge and Teaching Strategies on Learning by Teaching.
Rodrigo, M. M. T., Ong, A., Bringula, R. P., Basa, R. S., Cruz, C. D., & Matsuda, N. (2013) · AIED Workshops · Cited by 6
Yapay Zekanın Eğitimdeki Etkisinin İncelenmesi Üzerine Bir Alan Araştırması
Korkmaz, M. S., & Polat, S. (2023) · International Conference on Recent and Innovative Results in Engineering and Technology (ICRIRET) · doi:10.59287/icriret.1371 · Cited by 5
An Epistemic Shift: A Literacy of Adaptivity as Critical for Twenty-First Century Learning
Hung, D. W. L., Lim, K. Y. T., & Jamaludin, A. B. (2013) · Education innovation series · doi:10.1007/978-981-4560-17-7_1 · Cited by 5
Choice-based Assessment: Can Choices Made in Digital Games Predict 6th-Grade Students' Math Test Scores?
Chi, M., Schwartz, D. L., Blair, K. P., & Chin, D. B. (2014) · Cited by 5
Self-citation
From Learning Companions to Testing Companions
Sjödén, B., & Gulz, A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19773-9_46 · Cited by 5
The Science of Learning: Breaking News
Straumanis, J. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500208 · Cited by 5
Maximizing learning from collaborative activities
Lam, R. (2013) · eScholarship (California Digital Library) · Cited by 4
Artificial intelligence in classrooms
Yoo, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24001-0.00017-8 · Cited by 5
An Exploratory Comparison of a Traditional and an Adaptive Instructional Approach for College Algebra
Kasha, R. (2015) · Journal of International Crisis and Risk Communication Research · Cited by 3
Can a Teachable Agent Influence How Students Respond to Competition in an Educational Game?
Sjödén, B., Lind, M., & Silvervarg, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61425-0_29 · Cited by 3
The interrelationship between concepts about agency and students’ use of teachable-agent learning technology
Jaeger, C. B., Hymel, A. M., Levin, D. T., Biswas, G., Paul, N. A., & Kinnebrew, J. S. (2019) · Cognitive Research Principles and Implications · doi:10.1186/s41235-019-0163-6 · Cited by 5
Design and Evaluation of Micro-class Based on WeChat Public Platform
Zhang, N., Xu, T., Zhou, J., & Xie, M. (2019) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-35095-6_10 · Cited by 3
Computational Thinking in Support of Learning and Transfer
Hutchins, N. M., Biswas, G., Wolf, R. C., Chin, D. B., Grover, S., & Blair, K. P. (2020) · Proceedings. · doi:10.22318/icls2020.1405 · Cited by 3
Self-citation
Revolutionizing Mathematics Learning: Exploring the Potential of Robotics and AI as Interactive Tools for Personalized and Engaging Mathematical Education
Anand, M., Srivastava, V. K., Rayal, A., Pandey, S., Kumar, B. V., & Pachouri, V. (2023) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i59117.2023.10398165 · Cited by 2
The Powerful Teacher: A Power Hypothesis for the Benefits of Learning-by-Teaching
Wong, S. S. H., & Lim, S. W. H. (2026) · Educational Psychology Review · doi:10.1007/s10648-025-10097-1 · Cited by 4
AI in education: Pedagogical and ethical analysis of the implementation of ASSISTments in the school environment
Bazoukis, G. A., Halkidis, S. T., Πεπές, Ε., & Venardos, P. (2024) · European Journal of Science and Mathematics Education · doi:10.30935/scimath/14902 · Cited by 4
Affective teachable agent in virtual learning environment
Borjigin, A. (2014) · doi:10.32657/10356/59271 · Cited by 2
AI in Education
Khan, N., Khalique, F., Khan, M. A., & Saini, K. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4576-5.ch001 · Cited by 1
Fostering Critical Thinking in Intelligent Tutoring Systems Through a Supervised Multi-agent Pedagogical Framework
Chy, M. K. A., Arthi, P. B., Ifty, R. A., Hamim, A. M. A. I., Iqbal, J., & Franklin, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15346-3_47 · Cited by 1
Expanding Bloom's Two-Sigma Tutoring Theory Using Intelligent Agents
Hall, O. P. (2018) · International Journal of Knowledge-Based Organizations · doi:10.4018/ijkbo.2018070102 · Cited by 1
Teaching Without Learning: Is It OK With Weak AI?
Månsson, K., & Haake, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93846-2_44 · Cited by 1
How do you introduce an agent?
Silvervarg, A., & Månsson, K. (2018) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3267851.3267861 · Cited by 1
The Role of AI in Modern Classroom Management
Gayaki, U., & Daronde, S. B. (2025) · International Conference on Machine Learning and Autonomous Systems (ICMLAS) · doi:10.1109/icmlas64557.2025.10968264 · Cited by 1
Learning by Teaching
Fiorella, L., & Mayer, R. E. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781107707085.010 · Cited by 1
Curriculum-Related Trends in K-12 Adventist Education in the North American Division: An Inductive Analysis of the Profile Surveys--1987-2007
Desir, M. (2015) · doi:10.32597/dissertations/1589 · Cited by 1

Authors: Chin, D. B., Dohmen, I. M., Cheng, B. H., Oppezzo, M. A., Chase, C. C., & Schwartz, D. L.
Affiliations: Stanford University (US)
SRI International (US)
Corresponding author: Chin, Doris B.
ORCID: Chin, Doris B.
Dohmen, Ilsa M.
Cheng, Britte H.
Oppezzo, Marily A.
Chase, Catherine C.
Schwartz, Daniel L.
Venue: Educational Technology Research and Development
Publisher: Springer Science and Business Media LLC
Published: 2010-03-05
Volume: 58
Issue: 6
Pages: 649-669
ISSN: 1042-1629 (ISSN-L)
1556-6501
DOI: 10.1007/s11423-010-9154-5
OpenAlex: W2093782067
Cited by: 119
References: 45
Citations by year: 2026: 10 · 2025: 10 · 2024: 5 · 2023: 7 · 2022: 4 · 2021: 6 · 2020: 7 · 2019: 7 · 2018: 5 · 2017: 7 · 2016: 4 · 2015: 9 · 2014: 12 · 2013: 12 · 2012: 7
FWCI: 7.94
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Educational Technology Research and Development (publishedVersion)
Society for Research on Educational Effectiveness
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 › Education › Artificial Intelligence in Education
Keywords: teachable agents, concept maps, artificial intelligence in education, elementary school science, transfer of learning

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

Preparing students for future learning with Teachable Agents

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks