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Technology, Knowledge and Learning

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20-12-2019

Multiple Agent Designs in Conversational Intelligent Tutoring Systems

Authors: Anne Lippert, Keith Shubeck, Brent Morgan, Andrew Hampton, Arthur Graesser

Published in: Technology, Knowledge and Learning | Issue 3/2020

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Abstract

This article describes designs that use multiple conversational agents within the framework of intelligent tutoring systems. Agents in this case are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with them in natural language. The earliest conversational intelligent tutoring systems were limited to a single agent that interacted with a student in the role of a teacher or expert. Technological advances have since made possible systems in which multiple agents interact with the learner and each other to model ideal behavior, strategies, reflections, and social interactions. Though still an emerging technology, multi-agent intelligent tutoring systems afford pedagogical benefits that go beyond the capabilities of the single-agent system and have facilitated learning gains on a variety of subject matters and skills, including science, technology, engineering, mathematics, research methods, metacognition, and language comprehension. The present work describes some common multi-agent designs that may be used to achieve a variety of pedagogical goals. We provide examples of how these designs have been implemented in educational or experimental settings and anticipate future use within the field of artificial intelligence.

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Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2016). Help helps, but only so much: Research on help seeking with intelligent tutoring systems. International Journal of Artificial Intelligence in Education,26, 205–223. https://doi.org/10.1007/s40593-015-0089-1.CrossRef

Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. (2003). Help seeking and help design in interactive learning environments. Review of Educational Research,73(3), 277–320.CrossRef

Alison, L., van den Heuvel, C., Waring, S., Power, N., Long, A., O’Hara, T., et al. (2013). Immersive simulated learning environments for researching critical incidents: A knowledge synthesis of the literature and experiences of studying high-risk strategic decision making. Journal of Cognitive Engineering and Decision Making,7(3), 255–272. https://doi.org/10.1177/1555343412468113.CrossRef

Alport, F. H. (1920). The influence of the group upon association and thought. Journal of Experimental Psychology,3, 159–182.CrossRef

Azevedo, R., Landis, R. S., Feyzi-Behnagh, R., Duffy, M., Trevors, G., Harley, J. M., et al. (2012). The effectiveness of pedagogical agents’ prompting and feedback in facilitating co-adapted learning with MetaTutor. In International conference on intelligent tutoring systems (pp. 212–221). Berlin: Springer.

Azevedo, R., Moos, D., Johnson, A., & Chauncey, A. (2010). Measuring cognitive and metacognitive regulatory processes used during hypermedia learning: Issues and challenges. Educational Psychologist,45(4), 210–223. https://doi.org/10.1080/00461520.2010.515934.CrossRef

Baker, L. (1985). Differences in standards used by college students to evaluate their comprehension of expository prose. Reading Research Quarterly,20(3), 298–313. https://doi.org/10.2307/748020.CrossRef

Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248–287. https://doi.org/10.1016/0749-5978(91)90022-L.CrossRef

Becker-Beck, U., Wintermantel, M., & Borg, A. (2005). Principles of regulating interaction in teams practicing face-to-face communication versus teams practicing computer-mediated communication. Small Group Research,36(4), 499–536.CrossRef

Biswas, G., Jeong, H., Kinnebrew, J., Sulcer, B., & Roscoe, R. (2010). Measuring self-regulated learning skills through social interactions in a teachable agent environment. Research and Practice in Technology-Enhanced Learning,5(2), 123–152. https://doi.org/10.1142/s1793206810000839.CrossRef

Care, E., Scoular, C., & Griffin, P. (2016). Assessment of collaborative problem solving in education environments. Applied Measurement in Education,29(4), 250–264. https://doi.org/10.1080/08957347.2016.1209204.CrossRef

Chase, C. C., Chin, D. B., Oppezzo, M. A., & Schwartz, D. L. (2009). Teachable agents and the protege effect: Increasing the effort towards learning. Journal of Science Education and Technology,18(4), 334–352.CrossRef

Chi, M. T. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science,1(1), 73–105. https://doi.org/10.1111/j.1756-8765.2008.01005.x.CrossRef

Chi, M. T., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science,25, 471–533. https://doi.org/10.1207/s15516709cog2504_1.CrossRef

Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist,49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823.CrossRef

Cohen, P. A., Kulik, J. A., & Kulik, C. C. (1982). Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal,19(2), 237. https://doi.org/10.2307/1162567.CrossRef

Conradi, E., Kavia, S., Burden, D., Rice, A., Woodham, L., Beaumont, C., et al. (2009). Virtual patients in a virtual world: Training paramedic students for practice. Medical Teacher,31(8), 713–720. https://doi.org/10.1080/01421590903134160f.CrossRef

Craig, S. D. (Ed.). (2018). Tutoring and intelligent tutoring systems. New York: Nova Science Publishers.

Craig, S. D., Gholson, B., Brittingham, J. K., Williams, J., & Shubeck, K. T. (2012). Promoting vicarious learning of physics using deep questions with explanations. Computers and Education,58, 1042–1048. https://doi.org/10.1016/j.compedu.2011.11.018.CrossRef

Craig, S. D., Sullins, J., Witherspoon, A., & Gholson, B. (2006). The deep-level-reasoning-question effect: The role of dialogue and deep-level-reasoning questions during vicarious learning. Cognition and Instruction,24(4), 565–591. https://doi.org/10.1207/s1532690xci2404_4.CrossRef

D’Mello, S. K., Lehman, B., Pekrun, R., & Graesser, A. C. (2014). Confusion can be beneficial for learning. Learning and Instruction,29(3), 153–170. https://doi.org/10.1016/j.learninstruc.2012.05.003.CrossRef

D’Mello, S. K., & Graesser, A. C. (2010). Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features. User Modeling and User-Adapted Interaction,20(2), 147–187. https://doi.org/10.1007/s11257-010-9074-4.CrossRef

Douglas, K. M., & Albro, E. R. (2014). The progress and promise of the reading for understanding research initiative. Educational Psychology Review,26(3), 341–355. https://doi.org/10.1007/s10648-014-9278-y.CrossRef

Fiore, S. M., Carter, D. R., & Asencio, R. (2015). Conflict, trust, and cohesion: Examining affective and attitudinal factors in science teams. In E. Salas, W. B. Vessey, & A. X. Estrada (Eds.), Team cohesion: Advances in psychological theory, methods and practice (Vol. 17, pp. 271–301). Bingley: Emerald Group Publishing Limited. ISBN 978-1-78560-283-2.CrossRef

Fiore, S. M., Graesser, A. C., & Greiff, S. (2018). Collaborative problem-solving education for the twenty-first-century workforce. Nature Human Behaviour,2(6), 367–369. https://doi.org/10.1038/s41562-018-0363-y.CrossRef

Foronda, C. L., Shubeck, K., Swoboda, S. M., Hudson, K. W., Budhathoki, C., Sullivan, N., et al. (2016). Impact of virtual simulation to teach concepts of disaster triage. Clinical Simulation in Nursing,12(4), 137–144. https://doi.org/10.1016/j.ecns.2016.02.004.CrossRef

Forsyth, C. M., Pavlik, P., Graesser, A. C., Cai, Z., Germany, M., Millis, K., et al. (2012). Learning gains for core concepts in a serious game on scientific reasoning. In K. Yacef, O. Zaïane, H. Hershkovitz, M. Yudelson, & J. Stamper (Eds.), Proceedings of the 5th international conference on educational data mining (pp. 172–175). Chania, Greece.

Gholson, B., Witherspoon, A., Morgan, B., Brittingham, J. K., Coles, R., Graesser, A. C., et al. (2009). Exploring the deep-level reasoning questions effect during vicarious learning among eighth to eleventh graders in the domains of computer literacy and Newtonian physics. Instructional Science,37, 487–493.CrossRef

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.CrossRef

Graesser, A. C., Conley, M. W., & Olney, A. M. (2012a). Intelligent tutoring systems. In S. Graham & K. Harris (Eds.), APA educational psychology handbook: Vol 3 applications to learning and teaching (pp. 451–473). Washington, D.C.: American Psychological Association.CrossRef

Graesser, A. C., & D’Mello, S. (2012). Emotions during the learning of difficult material. In B. Ross (Ed.), The psychology of learning and motivation (Vol. 57, pp. 183–225). Amsterdam: Elsevier. ISBN 978-0-12-394293-7.CrossRef

Graesser, A. C., D’Mello, S. K., Hu. X., Cai, Z., Olney, A. & Morgan, B. (2012b). AutoTutor. In P. McCarthy & C. Boonthum-Denecke (Eds.), Applied natural language processing: Identification, investigation, and resolution (pp. 169–187). Hershey, PA: IGI Global. ISBN 9781609607418.

Graesser, A. C., D’Mello, S. & Person, N. K. (2009). Metaknowledge in tutoring. In D. J. Hacker, J. Donlosky, A. C. Graesser (Eds.), Handbook of metacognition in education (1st ed., pp. 361–382). New York: Taylor & Francis. ISBN 0805863540.

Graesser, A. C., Forsyth, C. M., & Foltz, P. (2016). Assessing conversation quality, reasoning, and problem solving performance with computer agents. In B. Csapo, J. Funke, & A. Schleicher (Eds.), On the nature of problem solving: A look behind PISA 2012 problem solving assessment (pp. 275–297). Heidelberg: OECD Series.

Graesser, A. C., Forsyth, C. M., & Lehman, B. A. (2017a). Two heads may be better than one: Learning from computer agents in conversational trialogues. Teachers College Record,119(3), 1–20.

Graesser, A. C., Greenberg, D., Olney, A. M., & Lovett, M. W. (2019). Educational technologies that support reading comprehension for adults who have low literacy skills. In D. Perin (Ed.), Wiley adult literacy handbook (pp. 471–493). New York: Wiley.CrossRef

Graesser, A. C., Hu, X., Nye, B. D., VanLehn, K., Kumar, R., Heffernan, C., et al. (2018a). ElectronixTutor: An intelligent tutoring system with multiple learning resources for electronics. International Journal of Stem Education: Innovations and Research,5(1), 15. https://doi.org/10.1186/s40594-018-0110-y.CrossRef

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.CrossRef

Graesser, A., Kuo, B. C., & Liao, C. H. (2017b). Complex problem solving in assessments of collaborative problem solving. Journal of Intelligence,5(2), 10. https://doi.org/10.3390/jintelligence5020010.CrossRef

Graesser, A. C., Li, H., & Forsyth, C. (2014). Learning by communicating in natural language with conversational agents. Current Directions in Psychological Science,23(5), 374–380. https://doi.org/10.1177/0963721414540680.CrossRef

Graesser, A. C., Lippert, A., & Hampton, A. J. (2017c). Successes and failures in building learning environments to promote deep learning: The value of conversational agents. In J. Buder & F. W. Hesse (Eds.), Informational environments: Effects of use, effective design (1st ed., pp. 273–298). New York: Springer. ISBN 978-3-319-64274-1.CrossRef

Graesser, A. C., Lippert, A. M. & Shi, G. (2018b). Performance of adults is higher for material tapping deep rather than shallow comprehension when using AutoTutor. In M. Lovett, D. Greenberg, Chairs, Adult literacy learners: Evaluating reading and reading-related skills and intervening with blended instructional programs, symposium conducted at the meeting of the scientific study of reading, Brighton, United Kingdom.

Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H. H., Ventura, M., Olney, A. M., et al. (2004). AutoTutor: A tutor with dialogue in natural language. Behavior Research Methods, Instruments, and Computers,36, 180–193. https://doi.org/10.3758/bf03195563.CrossRef

Graesser, A. C., & McNamara, D. S. (2011). Computational analyses of multilevel discourse comprehension. Topics in Cognitive Science,3(2), 371–398. https://doi.org/10.1111/j.1756-8765.2010.01081.x.CrossRef

Graesser, A. C., Person, N. K., & Magliano, J. P. (1995). Collaborative dialogue patterns in naturalistic one-to-one tutoring. Applied Cognitive Psychology,9(6), 495–522. https://doi.org/10.1002/acp.2350090604.CrossRef

Graesser, A. C., Rus, V., & Hu, X. (2017d). Instruction based on tutoring. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (2nd ed., pp. 460–482). New York: Routledge. ISBN 978-1-315-73641-9.

Greenberg, D. (2008). The challenges facing adult literacy programs. Community Literacy Journal,3(1), 39–54.

Griffin, P., & Care, E. (2015). The ATC21S method. In P. Griffin & E. Care (Eds.), Assessment and teaching of 21st century skills: Methods and approach (Vol. 2, pp. 3–33). Dordrecht: Springer. ISBN 978-94-017-9395-7.CrossRef

Halpern, D. F., Millis, K., Graesser, A. C., Butler, H., Forsyth, C., & Cai, Z. (2012). Operation ARA: A computerized learning game that teaches critical thinking and scientific reasoning. Thinking Skills and Creativity,7, 93–100. https://doi.org/10.1016/j.tsc.2012.03.006.CrossRef

Hesse, F., Care, E., Buder, J., Sassenberg, K., & Griffin, P. (2015). A framework for teachable collaborative problem solving skills. In P. Griffin & E. Care (Eds.), Assessment and teaching of 21st century skills: Methods and approach (Vol. 2, pp. 37–56). Dordrecht: Springer. ISBN 978-94-017-9395-7.CrossRef

Hew, K. F., & Cheung, W. S. (2010). Use of three-dimensional (3-D) immersive virtual worlds in K-12 and higher education settings: A review of the research. British Journal of Educational Technology,41(1), 33–55. https://doi.org/10.1111/j.1467-8535.2008.00900.x.CrossRef

Huang, X., & Mayer, R. E. (2016). Benefits of adding anxiety-reducing features to a computer-based multimedia lesson on statistics. Computers in Human Behavior,63, 293–303.CrossRef

Jackson, G. T., & Graesser, A. C. (2006). Applications of human tutorial dialog in AutoTutor: An intelligent tutoring system. Revista Signos,39(60), 31–48. https://doi.org/10.4067/s0718-09342006000100002.CrossRef

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.CrossRef

Jestice, J. & Kahai, S. (2010). The effectiveness of virtual worlds for education: An empirical study. In Proceedings of the sixteenth Americas conference on information systems (AMCIS) (pp. 512). Lima, Peru.

Johnson, W. L., & Lester, J. C. (2018). Pedagogical agents: Back to the future. AI Magazine,39(2), 33–44. https://doi.org/10.1609/aimag.v39i2.2793.CrossRef

Johnson, W. L., & Valente, A. (2009). Tactical language and culture training systems: Using AI to teach foreign languages and cultures. AI Magazine,30(2), 72. https://doi.org/10.1609/aimag.v30i2.2240.CrossRef

Kim, Y., & Baylor, A. L. (2006). Pedagogical agents as learning companions: The role of agent competency and type of interaction. Educational Technology Research and Development,54(03), 223–243.CrossRef

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.CrossRef

Kopp, K. J., Britt, M. A., Millis, K., & Graesser, A. C. (2012). Improving the efficiency of dialogue in tutoring. Learning and Instruction,22(5), 320–330. https://doi.org/10.1016/j.learninstruc.2011.12.002.CrossRef

Lee, E. J., & Nass, C. (2002). Experimental tests of normative group influence and representation effects in computer-mediated communication when interacting via computers differs from interacting with computers. Human Communication Research,28(3), 349–381.

Lehman, B., D’Mello, S. K., & Graesser, A. C. (2012). Confusion and complex learning during interactions with computer learning environments. Internet and Higher Education,15(3), 184–194. https://doi.org/10.1016/j.iheduc.2012.01.002.CrossRef

Lehman, B., D’Mello, S. K., Strain, A., Mills, C., Gross, M., Dobbins, A., et al. (2013). Inducing and tracking confusion with contradictions during complex learning. International Journal of Artificial Intelligence in Education,22(1–2), 85–105. https://doi.org/10.3233/jai-130025.CrossRef

Li, C., & Liu, Z. (2017). Collaborative problem-solving behavior of 15-year-old Taiwanese students in science education. Eurasia Journal of Mathematics, Science and Technology Education,13(10), 6677–6695. https://doi.org/10.12973/ejmste/78189.CrossRef

Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology,106(4), 901.CrossRef

Martin, S. A., Azevedo, R., Taub, M., Mudrick, N. V., Millar, G. C., & Grafsgaard, J. F. (2016). Are there benefits of using multiple pedagogical agents to support and foster self-regulated learning in an Intelligent Tutoring System? In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Intelligent Tutoring Systems ITS, Lecture Notes in Computer Science (Vol. 9684). Cham: Springer.

McNamara, D. S., O’Reilly, T., Best, R., & Ozuru, Y. (2006). Improving adolescent students’ reading comprehension with iSTART. Journal of Educational Computing Research,34, 147–171. https://doi.org/10.2190/1ru5-hdtj-a5c8-jvwe.CrossRef

Millis, K., Forsyth, C., Butler, H., Wallace, P., Graesser, A., & Halpern, D. (2011). Operation ARIES! A serious game for teaching scientific inquiry. In M. Ma, A. Oikonomou, & J. Lakhmi (Eds.), Serious games and edutainment applications (1st ed., pp. 169–196). London: Springer. ISBN 978-1-4471-2161-9.CrossRef

Millis, K., Forsyth, C., Wallace, P., Graesser, A. C., & Timmins, G. (2017). The impact of game-like features on learning from an intelligent tutoring system. Technology, Knowledge, and Learning,22, 1–22. https://doi.org/10.1007/s10758-016-9289-5.CrossRef

National Research Council. (2011). Assessing 21st century skills. Washington, DC: National Academies Press. ISBN 978-0-309-21793-4.

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.CrossRef

Olney, A. M., D’Mello, S., Person, N., Cade, W., Hays, P., Williams, C., et al. (2012). Guru: A computer tutor that models expert human tutors. In Intelligent tutoring systems. Lecture notes in computer science (pp. 256–261). https://doi.org/10.1007/978-3-642-30950-2_32.

Papastergiou, M. (2009). Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation. Computers and Education,52(1), 1–12. https://doi.org/10.1016/j.compedu.2008.06.004.CrossRef

Pashler, H., Bain, P., Bottge, B., Graesser, A. C., Koedinger, K., McDaniel, M., et al. (2007). Organizing instruction and study to improve student learning: A practice guide, NCER 2007-2004. Institute of Education Sciences, Washington, DC, ERIC Number: ED498555.

Patterson, R., Pierce, B., Bell, H. H., Andrews, D., & Winterbottom, M. (2009). Training robust decision making in immersive environments. Journal of Cognitive Engineering and Decision Making,3(4), 331–361. https://doi.org/10.1518/155534309x12599553478836.CrossRef

Puustinen, M. (1998). Help-seeking behavior in a problem-solving situation: Development of self-regulation. European Journal of Psychology of Education,13, 271–282.CrossRef

Renkl, A. (2002). Learning from worked-out examples: Instructional explanations supplement self-explanations. Learning and Instruction,12, 529–556.CrossRef

Rosen, Y. (2017). Assessing students in human-to-agent settings to inform collaborative problem-solving learning. Journal of Educational Measurement,54(1), 36–53. https://doi.org/10.1111/jedm.12131.CrossRef

Rosenshine, B., Meister, C., & Chapman, S. (1996). Teaching students to generate questions: A review of the intervention studies. Review of Educational Research,66(2), 181–221. https://doi.org/10.2307/1170607.CrossRef

Rowe, P. J., Mott, B. W., McQuiggan, S. W., Robison, J. L., Lee, S. & Lester, C. J. (2009). Crystal Island: A narrative centered learning environment for eighth grade microbiology. In Proceedings of the European conference on artificial intelligence (pp. 11–20), Brighton, UK.

Rowe, J. P., Shores, L. R., Mott, B. W., & Lester, J. C. (2011). Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education,21(1–2), 115–133.

Rus, V., D’Mello, S., Hu, X., & Graesser, A. (2013). Recent advances in conversational intelligent tutoring systems. AI Magazine,34(3), 42–54. https://doi.org/10.1609/aimag.v34i3.2485.CrossRef

Salas, E., Cooke, N., & Rosen, M. (2008). On teams, teamwork, and team performance: Discoveries and developments. Human Factors,50(3), 540–547. https://doi.org/10.1518/001872008x288457.CrossRef

Shubeck, K. T., Craig, S. D. & Hu, X. (2016). Live-action mass-casualty training and virtual world training: A comparison. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 60, pp. 2103–2107). Los Angeles, CA: SAGE Publications. https://doi.org/10.1177/1541931213601476.

Shubeck, K. T., Fang, Y. & Hu, X. (2017). Math reading comprehension: Comparing effectiveness of various conversation frameworks in an ITS. In E. André, R. Baker, X. Hu, M. Rodrigo, B. du Boulay (Eds.), Artificial intelligence in education: 18th international conference, AIED 2017, Wuhan, China, June 28–July 1, 2017, Proceedings (1st edn., Vol. 10331, pp. 617–620). Cham, Switzerland: Springer. ISBN 978-3-319-61425-0.

Sottilare, R., Brawner, K. W., Goldberg, B. & Holden, H. (2013). The generalized intelligent framework for tutoring (GIFT). In C. Best, G. Galanis, J. Kerry & R. Sottilare (Eds.), Fundamental issues in defense training and simulation (pp. 223–234). Burlington, VT: Ashgate Publishing Company. ISBN 9781409447214.

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist,46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369.CrossRef

VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A. M., & Rose, C. (2007). When are tutorial dialogues more effective than reading? Cognitive Science,31, 3–62. https://doi.org/10.1080/03640210709336984.CrossRef

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, trans.) Cambridge, MA: Harvard University Press.

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.CrossRef

Wood, H., & Wood, D. (1999). Help seeking, learning and contingent tutoring. Computers and Education,33, 153–169.CrossRef

Woolf, B. P. (2009). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Burlington, MA: MorganKaufmann/Elsevier.

Title: Multiple Agent Designs in Conversational Intelligent Tutoring Systems
Authors: Anne Lippert
Keith Shubeck
Brent Morgan
Andrew Hampton
Arthur Graesser
Publication date: 20-12-2019
Publisher: Springer Netherlands
Published in: Technology, Knowledge and Learning / Issue 3/2020
Print ISSN: 2211-1662
Electronic ISSN: 2211-1670
DOI: https://doi.org/10.1007/s10758-019-09431-8

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