
















































Microsoft Word - 2126-JOTLT FINAL.docx


Journal of Teaching and Learning with Technology, Vol. 1, No. 2, December 2012, pp. 43 – 47.  

A Case for the use of Pedagogical Agents in Online Learning 
Environments 

 
Noah L Schroeder & Olusola O. Adesope 

 
Keywords: Pedagogical Agent, Cost-effectiveness, Multimedia, Learning 
 
Framework 
 
Progressive multimedia learning tools have been extensively researched over the past twenty 
years. Two of these tools include intelligent tutoring systems (Graesser et al., 2004; Ma, 
Adesope, & Nesbit, 2011; VanLehn, 2011) and pedagogical agents (Mayer & DaPra, 2012; 
Moreno, Mayer, Spires, & Lester, 2001).  In this paper we discuss pedagogical agents, which are 
visible characters in multimedia learning environments designed to facilitate learning (Moreno, 
2005; Schroeder, Adesope, & Barouch Gilbert, 2012). Some researchers have expressed 
reservations that pedagogical agents may not be cost-effective (Choi & Clark, 2006; Clark & 
Choi, 2005; 2007). However, while it previously may have taken a considerable amount of time 
and resources to design and implement a pedagogical agent within a learning environment, 
recent advances in technology make pedagogical agent-based systems more accessible and 
affordable to educators. 
Pedagogical agent research is typically grounded in social agency theory.  Social agency theory 
is based on previous research which indicates that people treat computers as fellow humans 
(Reeves & Nass, 1996), and posits that “social cues in a multimedia message can prime the 
social conversation schema in learners” (Mayer, Sabko, & Mautone, 2003, p. 419).  Thus, Mayer 
et al. (2003) suggest that learners may perceive computer interaction as a social exchange of 
information. In sum, it is hypothesized that if the learner perceives the computer interaction as 
social communication, it may cause increased performance on transfer tests due to the student 
engaging in the “sense-making process” (Mayer et al., 2003, p. 420). This process describes 
active learning, which is delineated into three stages: selecting information, organizing it, and 
integrating it with prior knowledge (Mayer et al., 2003; Mayer, 2005).  Alternatively, Mayer et 
al. (2003) posit that a lack of social cues in a multimedia message will not cause a social 
response in the learner, and thus foster rote learning, or memorization.  As such, it is the process 
of deeper understanding (Atkinson, Mayer, & Merrill, 2005) that pedagogical agent researchers 
hope to foster to promote meaningful learning (Mayer et al., 2003) in pedagogical agent-based 
learning environments. 
  
 Are pedagogical agents useful in multimedia environments? 
 Research suggests that pedagogical agents have the ability to play many roles in the 
multimedia learning environment, such as demonstrating, scaffolding, coaching, modeling and 
testing (Clarebout, Elen, Johnson, & Shaw, 2002). However, throughout research, pedagogical 
agents often take the role of an instructor or a coach (Clarebout et al., 2002).  Recent research 
has started to investigate the use of peer-agents (e.g., Holmes, 2007), however this area is under-
represented compared to studies which utilized the agent as an instructor.   

Pedagogical agents are not necessarily artificially intelligent, although in the past 
researchers have paired them with intelligent tutoring systems (e.g., Moreno, Mayer, Spires, & 



Schroeder, N.L., & Adesope, O.O. 

Journal of Teaching and Learning with Technology, Vol. 1, No. 2, December 2012. 
jotlt.indiana.edu 

44 

Lester, 2001). To some this may seem a major limitation. However, an alternative viewpoint 
suggests that constructing artificially intelligent agents generally requires computing and 
programming knowledge that many educators may lack.  Thus, the ability to incorporate a non-
intelligent agent into a multimedia learning environment with relative ease may increase the 
effectiveness of the environment for minimal cost.  Cost-effectiveness should be an important 
consideration for educational researchers, as it is well known that budget cuts continue to affect 
many higher education programs (Potter, 2003). 
 
Empirical Results 
 Clarebout et al.’s (2002) seminal review concluded that “pedagogical agents do have 
possibilities for supporting learners when working with complex tasks…The potential of these 
pedagogical agents offer opportunities that should be grasped” (p. 281). These claims were 
reiterated by Kim and Ryu’s (2003) meta-analysis, which indicated that pedagogical agents 
presence in multimedia learning environments increased both learners’ retention (d=.30, p<.05) 
and transfer (d=.64, p<.05) scores.  
 Mayer’s (2005b) review revealed a median effect size of d=.22 for studies in which an agent 
was present. Similarly, Moreno’s review (2005) investigated pedagogical agent research in 
relation to Mayer’s (2005) cognitive theory of multimedia learning. Moreno found support for 
the redundancy principle, in that learners were able to learn more when the learning material did 
not provide redundant text and narration.  Additionally, Moreno found support for the modality 
principle, in that learners were able to perform better on post-tests if the pedagogical agent 
provided narration as the modality of communication rather than text. Moreover, Moreno’s 
review did not find support for the deleterious effects of the split-attention principle (Ayers & 
Sweller, 2005).  In other words, while learner’s split their attention between the agent and the 
learning material, it did not produce negative learning effects.  Finally, and perhaps most 
importantly, Moreno found that pedagogical agents can foster the active learning process. 
Recently, Heidig and Clarebout (2011) reviewed pedagogical agent research; however their 
results were not promising. They summarize that “the majority of studies (9 out of 15) yielded no 
difference on learning” (Heidig & Clarebout, 2011, p. 51). However, Schroeder, Adesope, and 
Barouch Gilbert’s (2012) recent meta-analysis indicates that pedagogical agents produce a small, 
positive effect on learning. 
 
Making it Work 
 
As mentioned, researchers have suggested that pedagogical agents may not be cost-effective 
(Choi & Clark, 2006; Clark & Choi, 2005; 2007).  In the past, pedagogical agent learning 
environments needed to either be created from scratch, or through the use of complex computer 
programs. Recently, inexpensive and easy to operate software options are becoming available to 
educators who want to include an agent in their instruction.  For example, Xtranormal (2012) can 
be used to create presentations which include pedagogical agents (see Figure 1). 
Xtranormal (2012) allows the user to create videos using animated characters in virtual 
environments. The characters range from cartoons characters and stick figures to fully 
anthropomorphized humanoids dressed in business attire.  The program is very simple to operate: 
you choose whether you want one or two agents, select the setting in which they will appear, 
select which the characters you will like to use, choose background sounds and type in the text 
which the text-to-speech engine will generate as narration. Alternatively, one could record 



Schroeder, N.L., & Adesope, O.O. 

Journal of Teaching and Learning with Technology, Vol. 1, No. 2, December 2012. 
jotlt.indiana.edu 

45 

human voices and upload the recording to provide the narration. The program also allows the 
user to customize the agents’ gestures and movements to make them more realistic. 

 
Figure 1. A screenshot which shows the user-interface of Xtranormal. From Xtranormal (2012). 

 

Future Implications 

It is plausible that creating a short presentation in Xtranormal (2012) may take slightly longer 
than a comparable slideshow or other multimedia presentation. However, the novelty of the 
presentation may facilitate student learning and motivation. While pedagogical agents are not the 
panacea of multimedia learning, in certain situations where something different is needed to 
grasp students attention, the use of pedagogical agents may be beneficial. 

References 

Atkinson, R.K., Mayer, R.E., & Merrill, M.M. (2005). Fostering social agency in multimedia  
learning: Examining the impact of an animated agent’s voice. Contemporary Educational 
Psychology, 30, 117-139. 
 
Ayers, P., & Sweller, J. (2005). The split-attention principle in multimedia learning. In R. Mayer  
(Ed.), The Cambridge Handbook of Multimedia Learning (pp.19-30). New York, NY: 
Cambridge University Press. 
 
Choi, S., & Clark, R.E. (2006). Cognitive and affective benefits of an animated pedagogical  



Schroeder, N.L., & Adesope, O.O. 

Journal of Teaching and Learning with Technology, Vol. 1, No. 2, December 2012. 
jotlt.indiana.edu 

46 

agent for learning English as a second language. Journal of Educational Computing Research, 
34(4), 441-466. 
 
Clarebout, G., Elen, J., Johnson, W.L., & Shaw, E. (2002). Animated pedagogical agents: An  
opportunity to be grasped? Journal of Educational Multimedia and Hypermedia, 11(3), 267-286. 
 
Clark, R.E., & Choi, S. (2005).  Five design principles for experiments on the effects of  
animated pedagogical agents. Journal of Educational Computing Research, 32(3), 209-225. 
 
Clark, R.E., & Choi, S. (2007). The questionable benefits of pedagogical agents: Response to 
Veletsianos. Journal of Educational Computing Research, 36(4), 379-381. 
 
Graesser, A.C., Lu, S., Jackson, G.T., Mitchell, H.H., Ventura, M., Olney, A., & Louwerse, 
M.M. (2004). AutoTutor: A tutor with dialogue in natural language. Behavioral Research 
Methods, Instruments and Computers, 36, 180-193. 
 
Heidig, S., & Clarebout, G. (2011). Do pedagogical agents make a difference to student 
motivation and learning? Educational Research Review, 6, 27-54. 
 
Holmes, J. (2007). Designing agents to support learning by explaining. Computer & Education, 
48, 523–547. 
 
Kim, M,. & Ryu, J. (2003). Meta-analysis of the effectiveness of pedagogical agent. In D.  
Lassner & C. McNaught (Eds.), Proceedings of World Conference on Educational Multimedia, 
Hypermedia and Telecommunications 2003 (pp. 479-486). Chesapeake, VA: AACE. 
 
Ma, W., Adesope, O.O., & Nesbit, J.C. (2011). Intelligent tutoring systems: A meta- 
analysis. American Educational Research Association Meeting, New Orleans, LA. 
 
Mayer, R.E., (2005). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The 
Cambridge Handbook of Multimedia Learning (pp.19-30). New York, NY: Cambridge 
University Press. 
 
Mayer, R.E., (2005b). Principles of multimedia learning based on social cues: Personalization,  
voice, and image principles. In R. Mayer (Ed.), The Cambridge Handbook of Multimedia 
Learning (pp.201-212). New York, NY: Cambridge University Press. 
 
Mayer, R.E., & DaPra, S.C. (2012). An embodiment effect in computer-based learning with 
animated pedagogical agents. Journal of Experimental Psychology: Applied, 3, 239-252. 
 
Mayer, R.E., Sabko, K., & Mautone, P. (2003). Social cues in multimedia learning: Role of 
speaker’s voice. Journal of Educational Psychology, 95(2), 419-425. 
 
Moreno, R. (2005). Multimedia learning with animated pedagogical agents. In R. Mayer (Ed.),  
The Cambridge Handbook of Multimedia Learning (pp. 507-523). New York, NY: Cambridge 
University Press. 



Schroeder, N.L., & Adesope, O.O. 

Journal of Teaching and Learning with Technology, Vol. 1, No. 2, December 2012. 
jotlt.indiana.edu 

47 

Moreno, R., Mayer, R.E., Spires, H.A., & Lester, J.C. (2001) The case for social agency  
in computer-based teaching: Do students learn more deeply when they interact with animated 
pedagogical agents? Cognition & Instruction, 19(2), 177-213. 
 
Potter, W. (2003, August 8). State lawmakers again cut higher-education spending. The 
Chronicle of Higher Education, p. A22. 
 
Reeves, B., & Nass, C. (1996). The Media Equation: How People Treat Computers, Television, 
and New Media like Real People and Places. Stanford, CA: CSLI Publications. 
 
Schroeder, N. L., Adesope, O. O., & Barouch Gilbert, R. (2012). A meta-analysis of the  
effects of pedagogical agents on learning.  Paper presented at the American Educational 
Research Association Annual Meeting. Vancouver, British Columbia. 
 
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, 
and other tutoring systems. Educational Psychologist, 46(4), 197-221. 
 
Xtranormal. (2012). Xtranormal [computer software]. http://www.xtranormal.com  


