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Abstract Deadline 1 June 2019 | Full Paper Deadline 15 October 2019
Call for Papers Special Issue
Learning with Virtual Humans
Virtual humans are becoming ubiquitous in today’s society. The term virtual human is used in a broad sense, encompassing all visually-represented human-like entities within software systems. For example, pedagogical agents are a type of virtual human intended to facilitate learning, while a conversational agent is a type of virtual human designed to interact with a learner in natural language. For the purposes of this special issue, we are interested in all applications of virtual humans as long as they are designed to facilitate learning or learning processes. Virtual humans have supported learning in a variety of educational systems such as intelligent tutoring systems (Graesser, 2016; Graesser, Lippert, & Hampton, 2017; Nye, Graesser, & Hu, 2014), educational games (Rowe et al., 2009), instructional videos (Craig & Schroeder, 2017), and demonstrations in virtual reality (Johnson & Lester, 2016). They have also been used in varied instructional contexts, such as learning mathematics in high school (Kim, Thayne, & Wei, 2017), in healthcare training for doctors and nurses (Albright, Bryan, Adam, McMillian, & Shockley, 2018), as well as counselor training (Lowell & Alshammari, 2018).
Research has shown that virtual humans used as pedagogical agents can enhance learning in some situations (Schroeder, Adesope, & Gilbert, 2013), and while there is some guidance as to what design features virtual humans should have to aid learning (Craig & Schroeder, 2018), and a framework for agent design exists (Heidig & Clarebout, 2011), the data-driven design of virtual humans is often not discussed. The limitations of the field may be due to the limited theoretical perspectives as to how and why virtual humans may be effective learning tools, as well as the small number of strong measurement instruments in the area (Schroeder, Romine, & Craig, 2017). While social agency theory (Domagk, 2010; Mayer, Sobko, & Mautone, 2003) provides a broad perspective of why virtual humans may influence learning outcomes, there have been only limited extensions of the theory in relation to virtual humans. Novel approaches to virtual human research are needed, and new theoretical perspectives that can lead to testable hypotheses could help move the field forward.
We invite manuscripts that approach a broad range of issues around how virtual humans may influence learning. Potential topics include, but are not limited to:
- Empirical investigations of virtual human design in laboratory or non-laboratory settings (such as classrooms, informal learning environments, or healthcare settings).
- The development of measurement instruments for use in virtual human research.
- Novel syntheses of virtual human research.
- Novel theoretical approaches to virtual human research.
- Syntheses of the issues that virtual human researchers may encounter and potential ways to alleviate them.
- Ethical issues in virtual human research.
Authors interested in submitting an article for this special issue are asked to submit a ~350-500 word abstract describing the scope of their manuscript on this Google form. Proposed abstracts are due by June 1, 2019. Feedback will be provided by July 1, 2019, and the full papers should be submitted to JRTE’s online system by October 15, 2019. Questions about this special issue can be sent to Noah Schroeder and Scotty Craig.
Timeline for Special Issue
Short proposal (~350-500 words) due to guest editors: June 1, 2019
Feedback provided to authors and full articles invited: July 1, 2019
Full manuscripts of invited papers submitted: October 15, 2019
Feedback provided to authors: February 1, 2020
Manuscript resubmitted: May 1, 2020
Feedback provided to authors: August 1, 2020
Revised manuscripts due: October 1, 2020
Final decisions from editors: December 1, 2020
Publication of issue: Q1, 2021 or Q2, 2021
Helping you Publish your Research
Albright, G., Bryan, C., Adam, C., McMillan, J., & Shockley, K. (2018). Using virtual patient simulations to prepare primary health care professionals to conduct substance use and mental health screening and brief intervention. Journal of the American Psychiatric Nurses Association, 24(3), 247-259.
Craig. S. D., & Schroeder, N. L. (2017). Reconsidering the voice effect when learning from a virtual human. Computers & Education, 114, 193-205.
Craig, S. D. & Schroeder, N. L. (2018). Design principles for virtual humans in educational technology environments. In K. Millis, J. Magliano, D. Long, & K. Wiemer (Eds.) Deep learning: Multi-disciplinary approaches (pp. 128-139). NY, NY: Routledge/Taylor and Francis.
Domagk, S. (2010). Do pedagogical agents facilitate learner motivation and learning outcomes? Journal of Media Psychology, 22(2), 82-95.
Graesser, A. C. (2016). Conversations with AutoTutor help students learn. International Journal of Artificial Intelligence in Education, 26(1), 124-132.
Graesser, A. C., Lippert, A. M., & Hampton, A. J. (2017). Successes and failures in building learning environments to promote deep learning: The value of conversational agents. In Informational Environments (pp. 273-298). Springer, Cham.
Heidig, S., & Clarebout, G. (2011). Do pedagogical agents make a difference to student motivation and learning? Educational Research Review, 6(1), 27-54.
Johnson, W. L., & Lester, J. C. (2016). Face-to-face interaction with pedagogical agents, twenty years later. International Journal of Artificial Intelligence in Education, 26(1), 25-36.
Kim, Y., Thayne, J., & Wei, Q. (2017). An embodied agent helps anxious students in mathematics learning. Educational Technology Research and Development, 65(1), 219-235.
Lowell, V. L., & Alshammari, A. (2018). Experiential learning experiences in an online 3D virtual environment for mental health interviewing and diagnosis role-playing: A comparison of perceived learning across learning activities. Educational Technology Research and Development, 1-30. https://doi.org/10.1007/s11423-018-9632-8
Mayer, R. E., Sobko, K., & Mautone, P. D. (2003). Social cues in multimedia learning: Role of speaker's voice. Journal of Educational Psychology, 95(2), 419.
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.
Rowe, J., Mott, B., McQuiggan, S., Robison, J., Lee, S., & Lester, J. (2009). Crystal island: A narrative-centered learning environment for eighth grade microbiology. In workshop on intelligent educational games at the 14th international conference on artificial intelligence in education, Brighton, UK (pp. 11-20).
Schroeder, N. L., Adesope, O. O., & Gilbert, R. (2013). How effective are pedagogical agents for learning? A meta-analytic review. Journal of Educational Computing Research. 49(1), 1-39.
Schroeder, N. L., Romine, W. L., & Craig, S. D. (2017). Measuring pedagogical agent persona and the influence of agent persona on learning. Computers & Education, 109, 176-186.