Intelligent Tutoring Systems in Virtual Worlds

There is a shift in education toward a more student-centered approach (Glowa & Goodell, 2016). At the same time, the 2014 Horizon Report in both K-12 and Higher Education predict a increased adoption of hybrid and online learning models within the next 1 to 2 years (Johnson, Adams, Becker, Estrada, Freeman, 2014a, 2014b). Both of these trends create a push toward self-regulated learning. Since students will have access to the learning material at any time, the likelihood that their instructor will be present is greatly reduced. In some cases, the instructor is never directly involved in the learning process. Instead, instructors contribute to a repository of learning content, set learning objectives, and develop pedagogical frameworks to help meet those objectives. The students themselves are then responsible for accessing the content and managing their own time to achieve the goals.

In the United States, education standards in English, mathematics, and science are changing to incorporate more so-called “21st century skills.” In English, the emphasis is now on “critical-thinking, problem-solving, and analytical skills” and in mathematics, there is an emphasis on “procedural skill and fluency, and the ability to apply the math they know to solve problems inside and outside the classroom” (Common Core State Standards Initiative, 2016). The Framework for K-12 Science Education puts an emphasis on investigation and states that “ in all inquiry-based approaches to science teaching, our expectation is that students will themselves engage in the practices and not merely learn about them secondhand” (National Research Council, 2012, p. 30). All of these standards lead toward a more exploratory model of learning across all disciplines, in which students experience content and learn in an authentic, organic way.

Given these converging forces of student-centered learning, online learning, and the drive for inquiry-based authentic learning experiences, two emerging trends in educational technology can play an important role in the new educational model. Intelligent tutoring systems (ITS) and open virtual worlds (OVW) have the potential to satisfy the needs of instructors, instructional designers, schools, and most importantly, students. Intelligent tutoring systems can provide students with feedback that is equivalent to a human tutor (VanLehn, 2011). The growing field of learning analytics and natural language assessment tools provide unobtrusive methods for building a profile of an individual student’s cognitive abilities and progress in mastering content (Lintean, Rus, & Azevedo, 2008; Rus & Stefanescu, 2016). Open virtual worlds have existed in computer and video games for nearly half a century (Nelson & Erlandson, 2012), and both desktop and immersive virtual worlds offer learning environments that provide a safe and authentic spaces for students to explore (Padiotis & Mikropoulos, 2012; Passig, 2015). Learning in virtual environments has been shown to be as effective as learning in real environments in a variety of contexts (Basur & Dormus, 2009; Finkelstein et al, 2005).

Education is a complex system, as defined by Ni and Branch (2008). The incorporation of intelligent learning objects to the environment only adds to the complexity. Given the sheer volume of content and learning paths available, and the fact that learning in an online environment may take place in the absence of an instructor, students could easily face cognitive overload (Kinshuk, 2016). The combination of ITS and OVW could address a number of challenges that will be faced in the coming years. While there is an existing body of research on these two technologies, both are still relatively untested in the field of educational technology.

There is much research still needed to integrate the existing information with the emerging opportunities to create virtual learning environments with intelligent tutoring systems.


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Finkelstein, N. D., Adams, W. K., Keller, C. J., Kohl, P. B., Perkins, K. K., Podolefsky, N. S., & Reid, S. (2005). When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment. Physics Education Research 1, 1(1), 1-8.


Glowa, L. & Goodell, J. (2016). Student-centered learning: Functional requirements for integrated systems to optimize learning. Vienna, VA: International Association for K-12 Online Learning.



Johnson, L., Adams Becker, S., Estrada, V., Freeman, A. (2014a). NMC Horizon Report: 2014 Higher Education Edition. Austin, Texas: The New Media Consortium.

Johnson, L., Adams Becker, S., Estrada, V., and Freeman, A. (2014b). NMC Horizon Report: 2014 K-12 Edition. Austin, Texas: The New Media Consortium.

Kinshuk (2016). Designing adaptive and personalized learning environments. New York: Routledge.


Lintean, M., Rus, V., & Azevedo, R. (2011). Automatic detection of student mental models based on natural language student input during metacognitive skill training. International Journal of Artificial Intelligence in Education, 21, 169-190.


National Research Council. (2012). A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. Washington, DC: The National Academies Press.

Nelson, B. C., & Erlandson, B. E. (2012). Design for learning in virtual worlds. New York: Routledge.

Ni, X., & Branch, R M. (2008). Complexity theory. In J. M. Spector, M. D. Merrill, J. van Merrienboer, & M. P. Driscoll (Eds.), Handbook of Research on Educational Communications and Technology (3rd ed.). New York: Taylor & Francis.

Padiotis, I., & Mikropoulos, T. A. (2010). Using SOLO to evaluate virtual environment in a technology education setting. Educational Technology & Society, 13(3), 233-245.

Passig, D. (2015). Revisiting the Flynn Effect through 3D immersive virtual reality (IVR). Computers & Education, 88, 327-342.

Rus, V., & Stefanescu, D. (2016). Non-intrusive assessment of learner’s prior knowledge in dialogue-based intelligent tutoring systems. Smart Learning Environments, 3(2). Retrieved from


Van Lehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.


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