The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Effects of Artificial Intelligence-Powered Virtual Agents on Learning Outcomes in Computer-Based Simulations: A Meta-Analysis
Open accessOverview
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?
Empirical evidence indicates positive impacts of student engagement with GenAI-assisted educational tools on learning outcomes.
“Empirical evidence indicates positive impacts of student engagement with GenAIassisted educational tools on learning outcomes (Dai et al., 2024 ; Gu & Yan, 2024 ).”
From Socratic Mind: Impact of a Novel GenAI-Powered Formative Assessment Tool on Student Learning and Higher-Order Thinking (2026) · cites this paper as Dai et al., 2024
Citations
Cites
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
THE ROLE OF TUTORING IN PROBLEM SOLVING *
Wood, D., Bruner, J. S., & Ross, G. (1976) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.1976.tb00381.x · Cited by 8,851 · cited 1×
“Learning can occur when learners are actively engaged in iterative problem-solving processes for knowledge construction and skills application (e.g., Kolodner, 1992; Wood et al., 1976).”
Distribution Theory for Glass's Estimator of Effect Size and Related Estimators
Hedges, L. V. (1981) · Journal of Educational Statistics · doi:10.2307/1164588 · Cited by 5,695
Cognitive load theory, learning difficulty, and instructional design
Sweller, J. (1994) · Learning and Instruction · doi:10.1016/0959-4752(94)90003-5 · Cited by 3,801 · cited 1×
“For example, in multimedia design for learning, using text can be redundant in explaining self-explanatory graphics that contain learning materials (Sweller, 1994).”
Publication bias in clinical research
Easterbrook, P. J., Gopalan, R., Berlin, J. A., & Matthews, D. R. (1991) · The Lancet · doi:10.1016/0140-6736(91)90201-y · Cited by 3,109 · cited 2×
“Publication type is a general moderator in a meta-analysis research, since studies with significant effects are more likely to be published as peer-reviewed papers (Easterbrook et al., 1991).”
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
A Nonparametric “Trim and Fill” Method of Accounting for Publication Bias in Meta-Analysis
Duval, S., & Tweedie, R. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10473905 · Cited by 2,955
Analysing data and undertaking meta‐analyses
Deeks, J. J., Higgins, J. P., Altman, D. G., & on behalf of the Cochrane Statistical Methods Group (2019) · Cochrane Handbook for Systematic Reviews of Interventions · doi:10.1002/9781119536604.ch10 · Cited by 2,672
Cognitive Architecture and Instructional Design: 20 Years Later
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019) · Educational Psychology Review · doi:10.1007/s10648-019-09465-5 · Cited by 2,337 · cited 2×
“The multimodal forms of computer-based simulations for learning can also increase learners’ cognitive load (Sweller et al., 2019).”
VanLEHN, K. (2011) · Educational Psychologist · doi:10.1080/00461520.2011.611369 · Cited by 2,323
Scientific discovery learning with computer simulations of conceptual domains
De Jong, T., & Van Joolingen, W. R. (1998) · Review of Educational Research · doi:10.3102/00346543068002179 · Cited by 1,593
Choosing effect measures and computing estimates of effect
Higgins, J. P., Li, T., & Deeks, J. J. (2019) · Cochrane Handbook for Systematic Reviews of Interventions · doi:10.1002/9781119536604.ch6 · Cited by 977 · cited 1×
“If the studies reported standard errors, we calculated the standard deviations (Higgins et al., 2019).”
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 · cited 4×
“Implemented in computer-based simulations for learning, virtual agents have generally demonstrated benefits for learners (Kulik & Fletcher, 2016).”
InfluentialEffects of game‐based learning on students' mathematics achievement: A meta‐analysis
Tokac, U., Novak, E., & Thompson, C. G. (2019) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12347 · Cited by 366
Face-to-Face Interaction with Pedagogical Agents, Twenty Years Later
Johnson, W. L., & Lester, J. C. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0065-9 · Cited by 255
Multimedia Design for Learning: An Overview of Reviews With Meta-Meta-Analysis
Noetel, M., Griffith, S., Delaney, O., Harris, N. R., Sanders, T., Parker, P., del Pozo Cruz, B., & Lonsdale, C. (2022) · Review of Educational Research · doi:10.3102/00346543211052329 · Cited by 148 · cited 2×
“…perspective argued that information exhibition with audio-visual dual channels (voice and graphic presentations) can be easier to understand than the ones presented with illustrated text (i.e., text and graphic presentations) (Castro-Alonso & Sweller, 2022; Noetel et al., 2022; Reinwein, 2012).”
InfluentialTanana, M. J., Soma, C. S., Srikumar, V., Atkins, D. C., & Imel, Z. E. (2019) · Journal of Medical Internet Research · doi:10.2196/12529 · Cited by 100
Flagged first, then by citations
Research works printed but not linked: 100
- Al-Elq, A. H. (2010). Simulation-based medical teaching and learning. Journal of family and Community Medicine, 17(1), 35–40. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3195067/pdf/JFCM-17-35.pdf · DOI
- Alinier, G. (2007). A typology of educationally focused medical simulation tools. Medical Teacher, 29(8), e243–e250. https://doi.org/10.1080/01421590701551185 · DOI
- Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051 · DOI · Paper page
- Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp. 89–195). Academic Press. https://doi.org/10.1016/S0079-7421(08)60422-3 · DOI · Paper page
- Baylor, A. L., & Kim, S. (2009). Designing nonverbal communication for pedagogical agents: When less is more. Computers in Human Behavior, 25(2), 450–457. https://doi.org/10.1016/j.chb.2008.10.008 · DOI
- *Beidel, D. C., Tuerk, P. W., Spitalnick, J., Bowers, C. A., & Morrison, K. (2021). Treating childhood social anxiety disorder with virtual environments and serious games: a randomized trial. Behavior Therapy, 52(6), 1351-1363. https://doi.org/10.1016/j.beth.2021.03.003 · DOI · Paper page
- Bjork, E. L., & Bjork, R. A. (2011). Making things hard on your-self, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher, R. W. Pew, L. M. Hough, & J. R. Pomerantz (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society (pp. 56–64). Worth Publishers.
- Byrne, J., Heavey, C., & Byrne, P. J. (2010). A review of Web-based simulation and supporting tools. Simulation Modelling Practice and Theory, 18(3), 253–276. https://doi.org/10.1016/j.simpat.2009.09.013 · DOI
- *Carlotto, T., & Jaques, P. A. (2016). The effects of animated pedagogical agents in an English-as-a-foreign-language learning environment. International Journal of Human-Computer Studies, 95, 15-26. https://doi.org/10.1016/j.ijhcs.2016.06.001 · DOI
- Carroll, J. B. (1963). A model of school learning. Teachers College Record, 64(8), 723–733. https://doi.org/10.1177/016146816306400801 · DOI · Paper page
- Cassell, J. (2001). Embodied conversational agents: Representation and intelligence in user interfaces. AI Magazine, 22(4), 67–83. https://doi.org/10.1609/aimag.v22i4.1593 · DOI
- Castro-Alonso, J. C., & Sweller, J. (2022). The modality principle in multimedia learning. In R. E. Mayer & L. Fiorella (Eds.), The Cambridge handbook of multimedia learning (3rd ed., pp. 261–267). Cambridge University Press. https://doi.org/10.1017/9781108894333.026 · DOI
- Castro-Alonso, J. C., Wong, R. M., Adesope, O. O., & Paas, F. (2021). Effectiveness of multimedia pedagogical agents predicted by diverse theories: A meta-analysis. Educational Psychology Review, 33(3), 989–1015. https://doi.org/10.1007/s10648-020-09587-1 · DOI
- *Chen, C.-H., & Chou, M.-H. (2015). Enhancing middle school students' scientific learning and motivation through agent‐based learning. Journal of Computer Assisted Learning, 31(5), 481-492. https://doi.org/10.1111/jcal.12094 · DOI
- Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002 · DOI · Paper page
- *Cheng, Y. M., Chen, L. S., Huang, H. C., Weng, S. F., Chen, Y. G., & Lin, C. H. (2009). Building a general purpose pedagogical agent in a web-based multimedia clinical simulation system for medical education. IEEE Transactions on Learning Technologies, 2(3), 216-225. https://doi.org/10.1109/TLT.2009.18 · DOI
- Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer, F. (2020). Simulation-based learning in higher education: A meta-analysis. Review of Educational Research, 90(4), 499–541. https://doi.org/10.3102/0034654320933544 · DOI · Paper page
- *Clarebout, G., & Elen, J. (2006). Open learning environments and the impact of a pedagogical agent. Journal of Educational Computing Research, 35(3), 211-226. https://doi.org/10.2190/3UL1-4756-H837-2704 · DOI
- Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104 · DOI · Paper page
- * D'Mello, S., Olney, A., Williams, C., & Hays, P. (2012). Gaze tutor: A gaze-reactive intelligent tutoring system. International Journal of Human-computer studies, 70(5), 377-398. https://doi.org/10.1016/j.ijhcs.2012.01.004 · DOI
- Dai, C.-P. (2023). Enhancing learning achievements and self-efficacy for preservice teachers using model-based support in simulation-based learning with artificial intelligence-powered virtual agents. Doctoral dissertation. Florida State University.
- Dai, C.-P. (2024). Applying machine learning to augment the design and assessment of immersive learning experience. In M. S. Khine (Ed.), Machine learning in educational sciences: Approaches, applications and advances. Springer. https://doi.org/10.1007/978-981-99-9379-6_12 · DOI
- Dai, C.-P., & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers & Education: Artificial Intelligence, 3, 100087. https://doi.org/10.1016/j.caeai.2022.100087 · DOI
- Dai, Z., Ke, F., Dai, C.-P., Pachman, M., & Yuan, X. (2021). Role-play in virtual reality: A teaching training design case using OpenSimulator. In G. Akcayir & C. D. Epp (Eds.), Designing, deploying, and evaluating virtual and augmented reality in education (pp. 143–163). IGI Global. https://doi.org/10.4018/978-1-7998-5043-4.ch007 · DOI
- Dai, C.-P., Ke, F., Dai, Z., & Pachman, M. (2023). Improving teaching practices via virtual reality-supported simulation-based learning: Scenario design and the duration of implementation. British Journal of Educational Technology, 54(4), 836–856. https://doi.org/10.1111/bjet.13296 · DOI
- Dai, C-P., Ke, F., Zhang, N., Barrett, A., West, L., Bhowmik, S., Southerland, S. A., & Yuan, X. (2024). Designing conversational agents to support student teacher learning in virtual reality simulation: A case study. In Proceedings of The ACM CHI conference on Human Factors in Computing Systems Extended Abstracts (ACM CHI EA ‘24), Honolulu. https://doi.org/10.1145/3613905.3637145 · DOI
- Daradoumis, T., & Arguedas, M. (2020). Cultivating students’ reflective learning in metacognitive activities through an affective pedagogical agent. Educational Technology & Society, 23(2), 19–31. https://www.jstor.org/stable/26921131
- Davis, R. O. (2018). The impact of pedagogical agent gesturing in multimedia learning environments: A meta-analysis. Educational Research Review, 24, 193–209. https://doi.org/10.1016/j.edurev.2018.05.002 · DOI
- Decker, S., Sportsman, S., Puetz, I., & Billings, I. (2008). The evolution of simulation and its contribution to competency. The Journal of Continuing Education in Nursing, 39(2), 74–80. https://doi.org/10.3928/00220124-20080201-06 · DOI
- *Dinçer, S., & Doğanay, A. (2017). The effects of multiple-pedagogical agents on learners’ academic success, motivation, and cognitive load. Computers & Education, 111, 74-100. https://doi.org/10.1016/j.compedu.2017.04.005 · DOI
- Domagk, S. (2010). Do pedagogical agents facilitate learner motivation and learning outcomes?: The role of the appeal of agent’s appearance and voice. Journal of Media Psychology: Theories, Methods, and Applications, 22(2), 84–97. https://doi.org/10.1027/1864-1105/a000011 · DOI
- Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315(7109), 629–634. https://doi.org/10.1136/bmj.315.7109.629 · DOI · Paper page
- Elkot, M. A. (2019). Embedding adaptation levels within intelligent tutoring systems for developing programming skills and improving learning efficiency. International Journal of Advanced Computer Science and Applications, 10(12), 82–87. https://doi.org/10.14569/IJACSA.2019.0101211 · DOI
- *Frechette, M. C. (2008). Animated pedagogical agents: How the presence and nonverbal communication of a virtual instructor affect perceptions and learning outcomes in a computer-based environment about basic physics concepts [Unpublished doctoral dissertation]. The University of New Mexico.
- Gibbons, A. S. (2020). What is instructional strategy? Seeking hidden dimensions. Educational Technology Research and Development, 68(6), 2799–2815. https://doi.org/10.1007/s11423-020-09820-2 · DOI
- *Goldberg, B., & Cannon-Bowers, J. (2015). Feedback source modality effects on training outcomes in a serious game: Pedagogical agents make a difference. Computers in Human Behavior, 52, 1-11. https://doi.org/10.1016/j.chb.2015.05.008 · DOI
- Gorbunova, A., van Merrienboer, J.J.G. & Costley, J. (2023). Are inductive teaching methods compatible with cognitive load theory?. Educational Psychology Review, 35, Article 111. https://doi.org/10.1007/s10648-023-09828-z · DOI
- Graesser, A. C. (2016). Conversations with AutoTutor help students learn. International Journal of Artificial Intelligence in Education, 26(1), 124–132. https://doi.org/10.1007/s40593-015-0086-4 · DOI
- Graesser, A. C., Jeon, M., & Dufty, D. (2008). Agent technologies designed to facilitate interactive knowledge construction. Discourse Processes, 45(4–5), 298–322. https://doi.org/10.1080/01638530802145395 · DOI
- *Gulz, A., Haake, M., & Silvervarg, A. (2011). Extending a teachable agent with a social conversation module–effects on student experiences and learning. In International conference on artificial intelligence in education (pp. 106–114). Springer, Berlin, Heidelberg. https://dl.acm.org/doi/10.5555/2026506.2026524 · DOI
- Guo, Y. R., & Goh, D. H. L. (2015). Affect in embodied pedagogical agents: Meta-analytic review. Journal of Educational Computing Research, 53(1), 124–149. https://doi.org/10.1177/0735633115588774 · DOI
- Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta-regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39–65. https://doi.org/10.1002/jrsm.5 · DOI · Paper page
- Heidig, S., & Clarebout, G. (2011). Do pedagogical agents make a difference to student motivation and learning? Educational Research Review, 6(1), 27–54. https://doi.org/10.1016/j.edurev.2010.07.004 · DOI
- Higgins, J. P., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. British Medical Journal, 327(7414), 557–560. https://doi.org/10.1136/bmj.327.7414.557 · DOI · Paper page
- Hwang, G. J., Xie, H., Wah, B. W., & Gašević. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001 · DOI · Paper page
- Jackson, G. T., & McNamara, D. S. (2013). Motivation and performance in a game-based intelligent tutoring system. Journal of Educational Psychology, 105(4), 1036–1049. https://doi.org/10.1037/a0032580 · DOI
- Johnson, W. L. (2003). Interaction tactics for socially intelligent pedagogical agents. In Proceedings of the 8th International Conference on Intelligent User Interfaces (pp. 251–253). https://doi.org/10.1145/604045.604090 · DOI
- *Johnson, E. (2021). An intelligent tutoring system’s approach for negotiation training. [Unpublished doctoral dissertation]. University of Southern California.
- Kane, M. J., & Engle, R. W. (2000). Working-memory capacity, proactive interference, and divided attention: Limits on long-term memory retrieval. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26(2), 336–358. https://doi.org/10.1037/0278-7393.26.2.336 · DOI
- Ke, F., Dai, Z., Dai, C.-P., Pachman, M., Chaulagain, R., & Yuan, X. (2020). Designing virtual agents for simulation-based learning in virtual reality. In R. Zheng (Ed.), Cognitive and affective perspectives on immersive technology in education (pp. 151–170). IGI Global. https://doi.org/10.4018/978-1-7998-3250-8.ch008 · DOI
- Kim, S. (2005). The effect of multimedia design elements on learning outcomes in pedagogical agent research: a meta-analysis. In M. Grandbastien (Ed.), Young Research Track Proceedings of 12th International Conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (pp. 69–76).
- Kim, Y., & Baylor, A. L. (2016). Research-based design of pedagogical agent roles: A review, progress, and recommendations. International Journal of Artificial Intelligence in Education, 26(1), 160–169. https://doi.org/10.1007/s40593-015-0055-y · DOI
- Kolb, D. A. (1984). Experiential learning experience as a source of learning and development. Prentice Hall.
- Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6(1), 3–34. https://doi.org/10.1007/BF00155578 · DOI
- *Kron, F. W., Fetters, M. D., Scerbo, M. W., White, C. B., Lypson, M. L., Padilla, M. A., ... & Becker, D. M. (2017). Using a computer simulation for teaching communication skills: A blinded multisite mixed methods randomized controlled trial. Patient education and counseling, 100(4), 748-759. https://doi.org/10.1016/j.pec.2016.10.024 · DOI
- Lane, H. C., Cahill, C., Foutz, S., Auerbach, D., Noren, D., Lussenhop, C., & Swartout, W. (2013). The effects of a pedagogical agent for informal science education on learner behaviors and self-efficacy. In Proceedings of International conference on artificial intelligence in education (pp. 309–318). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_32 · DOI
- *Le, N. T., & Wartschinski, L. (2018). A cognitive assistant for improving human reasoning skills. International Journal of Human-Computer Studies, 117, 45-54. https://doi.org/10.1016/j.ijhcs.2018.02.005 · DOI
- *Lin, L., Atkinson, R. K., Christopherson, R. M., Joseph, S. S., & Harrison, C. J. (2013). Animated agents and learning: Does the type of verbal feedback they provide matter? Computers & Education, 67, 239–249. https://doi.org/10.1016/j.compedu.2013.04.017 · DOI
- Lippert, A., Shubeck, K., Morgan, B., Hampton, A., & Graesser, A. (2020). Multiple agent designs in conversational intelligent tutoring systems. Technology, Knowledge and Learning, 25, 443–463. https://doi.org/10.1007/s10758-019-09431-8 · DOI
- Luck, M., & Aylett, R. (2000). Applying artificial intelligence to virtual reality: Intelligent virtual environments. Applied Artificial Intelligence, 14(1), 3–32. https://doi.org/10.1080/088395100117142 · DOI
- Maroengsit, W., Piyakulpinyo, T., Phonyiam, K., Pongnumkul, S., Chaovalit, P., & Theeramunkong, T. (2019). A survey on evaluation methods for chatbots. In Proceedings of the 2019 7th International Conference on Information and Education Technology (pp. 111–119). https://doi.org/10.1145/3323771.3323824 · DOI
- Mascarenhas, S., Guimarães, M., Prada, R., Dias, J., Santos, P. A., Star, K., Hirsh, B., Spice, E., & Kommeren, R. (2018). A virtual agent toolkit for serious games developers. In 2018 IEEE Conference on Computational Intelligence and Games (CIG) (pp. 1–7). IEEE. https://doi.org/10.1109/CIG.2018.8490399 · DOI
- Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? American Psychologist, 59(1), 14–19. https://doi.org/10.1037/0003-066X.59.1.14 · DOI · Paper page
- McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12–14. https://doi.org/10.1609/aimag.v27i4.1904 · DOI
- Merchant, Z., Goetz, E. T., Cifuentes, L., Keeney-Kennicutt, W., & Davis, T. J. (2014). Effectiveness of virtual reality-based instruction on students’ learning outcomes in K-12 and higher education: A meta-analysis. Computers & Education, 70, 29–40. https://doi.org/10.1016/j.compedu.2013.07.033 · DOI · Paper page
- Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97. https://doi.org/10.1037/h0043158 · DOI · Paper page
- *Moundridou, M., & Virvou, M. (2002). Evaluating the persona effect of an interface agent in a tutoring system. Journal of Computer Assisted Learning, 18(3), 253–261. https://doi.org/10.1046/j.0266-4909.2001.00237.x · DOI
- Nye, B. D., Graesser, A. C., & Hu, X. (2014). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469. https://doi.org/10.1007/s40593-014-0029-5 · DOI
- Pavlik Jr., P. I., Brawner, K., Olney, A., & Mitrovic, A. (2013). A review of student models used in intelligent tutoring systems. In R. A. Sottilare, A. Graesser, X. Hu, & H. Holden (Eds.), Design recommendations for intelligent tutoring systems: Learner modeling (Vol. 1, pp. 39–67). U.S. Army Research Laboratory. Retrieved from: https://gifttutoring.org/documents/42
- Peng, T. H., & Wang, T. H. (2022). Developing an analysis framework for studies on pedagogical agent in an e-Learning environment. Journal of Educational Computing Research, 6(3). https://doi.org/10.1177/07356331211041701 · DOI
- Peterson, M. (2010). Computerized games and simulations in computer-assisted language learning: A meta-analysis of research. Simulation & Gaming, 41(1), 72–93. https://doi.org/10.1177/1046878109355684 · DOI
- Reinwein, J. (2012). Does the modality effect exist? And if so, which modality effect? Journal of Psycholinguistic Research, 41(1), 1–32. https://doi.org/10.1007/s10936-011-9180-4 · DOI
- Richey, R. C., Klein, J. D., & Tracey, M. W. (2011). The instructional design knowledge base: Theory, research, and practice. Routledge.
- Rickel, J. (2001). Intelligent virtual agents for education and training: Opportunities and challenges. In de Antonio, A., Aylett, R., Ballin, D. (Eds). Intelligent Virtual Agents. IVA 2001. Lecture Notes in Computer Science, vol 2190 (pp. 15–22). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44812-8_2 · DOI
- Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3 · DOI · Paper page
- Rosé, C. P., McLaughlin, E. A., Liu, R., & Koedinger, K. R. (2019). Explanatory learner models: Why machine learning (alone) is not the answer. British Journal of Educational Technology, 50(6), 2943–2958. https://doi.org/10.1111/bjet.12858 · DOI
- Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological bulletin, 86(3). · DOI · Paper page
- *Rosenthal-von der Pütten, A. M., Straßmann, C., & Krämer, N. C. (2016, September). Robots or agents–neither helps you more or less during second language acquisition. In Traum, D., Swartout, W., Khooshabeh, P., Kopp, S., Scherer, S., Leuski, A. (Eds). Proceedings of International conference on intelligent virtual agents (pp. 256–268). Springer, Cham. https://doi.org/10.1007/978-3-319-47665-0_23 · DOI
- Roth, P. L. (1994). Missing data: A conceptual review for applied psychologists. Personnel Psychology, 47(3), 537–560. https://doi.org/10.1111/j.1744-6570.1994.tb01736.x · DOI · Paper page
- Russell, S. J., & Norvig, P. (Ed.). (2021). Artificial intelligence: A modern approach (4th edition). Pearson.
- Scheel, A. M., Schijen, M. R., & Lakens, D. (2021). An excess of positive results: Comparing the standard psychology literature with registered reports. Advances in Methods and Practices in Psychological Science, 4(2), 1–12. https://doi.org/10.1177/25152459211007467 · DOI
- Schroeder, N. L., & Gotch, C. M. (2015). Persisting issues in pedagogical agent research. Journal of Educational Computing Research, 53(2), 183–204. https://doi.org/10.1177/0735633115597625 · DOI
- Schroeder, N. L., Adesope, O. O., & Gilbert, R. B. (2013). How effective are pedagogical agents for learning? A meta-analytic review. Journal of Educational Computing Research, 49(1), 1–39. https://doi.org/10.2190/EC.49.1.a · DOI
- Schweppe, J., & Rummer, R. (2014). Attention, working memory, and long-term memory in multimedia learning: An integrated perspective based on process models of working memory. Educational Psychology Review, 26(2), 285–306. https://doi.org/10.1007/s10648-013-9242-2 · DOI
- Seshia, S. A., Sadigh, D., & Sastry, S. S. (2022). Toward verified artificial intelligence. Communications of the ACM, 65(7), 46–55. https://doi.org/10.1145/3503914 · DOI · Paper page
- *Shiban, Y., Schelhorn, I., Jobst, V., Hörnlein, A., Puppe, F., Pauli, P., & Mühlberger, A. (2015). The appearance effect: Influences of virtual agent features on performance and motivation. Computers in Human Behavior, 49, 5–11. https://doi.org/10.1016/j.chb.2015.01.077 · DOI
- Sinatra, A. M., Pollard, K. A., Files, B. T., Oiknine, A. H., Ericson, M., & Khooshabeh, P. (2021). Social fidelity in virtual agents: Impacts on presence and learning. Computers in Human Behavior, 114, 106562. https://doi.org/10.1016/j.chb.2020.106562 · DOI
- Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64(2), 489–528. https://doi.org/10.1111/j.1744-6570.2011.01190.x\ · DOI
- Spronck, P., Ponsen, M., Sprinkhuizen-Kuyper, I., & Postma, E. (2006). Adaptive game AI with dynamic scripting. Machine Learning, 63(3), 217–248. https://doi.org/10.1007/s10994-006-6205-6 · DOI
- *Thompson, N., & McGill, T. J. (2017). Genetics with Jean: The design, development and evaluation of an affective tutoring system. Educational Technology Research and Development, 65(2), 279–299. https://doi.org/10.1007/s11423-016-9470-5 · DOI
- Tun, J. K., Alinier, G., Tang, J., & Kneebone, R. L. (2015). Redefining simulation fidelity for healthcare education. Simulation & Gaming, 46(2), 159–174. https://doi.org/10.1177/1046878115576103 · DOI
- *van der Meij, H. (2013). Do pedagogical agents enhance software training?. Human–Computer Interaction, 28(6), 518–547. https://doi.org/10.1080/07370024.2013.789348 · DOI
- Veletsianos, G., & Russell, G. S. (2013). What do learners and pedagogical agents discuss when given opportunities for open-ended dialogue? Journal of Educational Computing Research, 48(3), 381–401. https://doi.org/10.2190/EC.48.3.e · DOI
- Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of statistical software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03 · DOI · Paper page
- Vygotsky, L.S. (1978). Mind in society: Development of higher psychological processes. Harvard university press.
- Wang, N., Johnson, W. L., Mayer, R. E., Rizzo, P., Shaw, E., & Collins, H. (2008). The politeness effect: Pedagogical agents and learning outcomes. International Journal of Human-Computer Studies, 66(2), 98–112. https://doi.org/10.1016/j.ijhcs.2007.09.003 · DOI
- *Wang, F., Li, W., Mayer, R. E., & Liu, H. (2018). Animated pedagogical agents as aids in multimedia learning: Effects on eye-fixations during learning and learning outcomes. Journal of Educational Psychology, 110(2), 250-268. https://doi.org/10.1037/edu0000221 · DOI
- *Ward, W., Cole, R., Bolaños, D., Buchenroth-Martin, C., Svirsky, E., & Weston, T. (2013). My science tutor: A conversational multimedia virtual tutor. Journal of Educational Psychology, 105(4), 1115–1125. https://doi.org/10.1037/a0031589 · DOI
- Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995 · DOI · Paper page
- Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI Magazine, 34(4), 66–84. https://doi.org/10.1609/aimag.v34i4.2490 · DOI
Cited by
Sorted by citations
Han, X., Peng, H., & Liu, M. (2025) · Educational Research Review · doi:10.1016/j.edurev.2025.100714 · Cited by 70
Lee, J., Hung, J.-T., Yilmaz Soylu, M., Popescu, D., Cui, C. Z., Grigoryan, G., Joyner, D. A., & Harmon, S. W. (2026) · Technology, Knowledge and Learning · doi:10.1007/s10758-026-10007-6 · Cited by 23
Metadata
- Authors
- Dai, C.-P., Ke, F., Pan, Y., Moon, J., & Liu, Z.
- Affiliations
- University of Hawaiʻi at Mānoa (US)
- Florida State University (US)
- Southern Methodist University (US)
- University of Alabama (US)
- University of Hong Kong (HK)
- Corresponding author
- Dai, Chih-Pu
- Venue
- Educational Psychology Review
- Published
- 2024-03
- Volume
- 36
- Issue
- 1
- ISSN
- 1040-726X (ISSN-L)
- 1573-336X
- OpenAlex
- W4392356798
- Cited by
- 116
- References
- 17
- Citations by year
- 2026: 68 · 2025: 39 · 2024: 9
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- green
- hub.hku.hk
- Licence
- Locations
- Educational Psychology Review (publishedVersion)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (2110777)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- computer simulation, learning outcomes, adaptive learning, personalized learning, agent representation, intervention duration, AI technologies, educational technology effectiveness
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