Pedagogical Agent Design: The Impact of Agent
Realism, Gender, Ethnicity, and Instructional Role
Amy L. Baylor, Ph.D., Yanghee Kim
Pedagogical Agent Learning Systems (PALS) Research Laboratory
Department of Educational Psychology and Learning Systems
307 Stone Building
Florida State University
Abstract. In the first of two experimental studies, 312 students were randomly
assigned to one of 8 conditions, where agents differed by ethnicity (Black,
White), gender (male, female), and image (realistic, cartoon), yet had identical
messages and computer-generated voice. In the second study, 229 students
were randomly assigned to one of 12 conditions where agents represented dif-
ferent instructional roles (expert, motivator, and mentor), also differing by eth-
nicity (Black, White), and gender (male, female). Overall, it was found that
students had greater transfer of learning when the agents had more realistic im-
ages and when agents in the “expert” role were represented non-traditionally
(as Black versus White). Results also generally confirmed prior research where
agents perceived as less intelligent lead to significantly improved self-efficacy.
The presence of motivational messages, as employed through the motivator and
mentor agent roles, led to enhanced learner self-regulation and self-efficacy.
Results are discussed with respect to social cognitive theory.
Pedagogical agent design has recently been placing greater emphasis on the impor-
tance of the agent as an actor rather than as a tool (Persson, Laaksolahti, & Lonnqvist,
2002), thus focusing on the agent’s implicit social relationship with the learner. The
social cognitive perspective in teaching and learning emphasizes the importance that
social interaction (e.g., Lave & Wenger, 2001; Vygotsky, Cole, John-Steiner, Scrib-
ner, & Souberman, 1978) plays in contributing to motivational outcomes such as
learner self-efficacy (Bandura, 2000) and self-regulation (Zimmerman, 2000).
According to Bandura (1997), attribute similarities between a social model and a
learner, such as gender, ethnicity, and competency, often have predictive significance
for the learner’s efficacy beliefs and achievements. Similarly, pedagogical agents of
the same gender or ethnicity or similar competency as learners’ might be viewed as
more affable and could instill strong efficacy beliefs and behavioral intentions to
learners. Learners may draw positive judgments about their capabilities when they
observe agents who demonstrate successful performance.
Even so, while college students were not more likely to choose to work with an agent
of the same gender (Baylor, Shen, & Huang, 2003), in a between-subjects study they
were more satisfied with their performance and reported that the agent better facili-
tated self-regulation if it was male (Baylor & Kim, 2003). Similarly, Moreno and
colleagues (2002) revealed that learners applied gender stereotypes to animated
agents, and this stereotypic expectation affected their learning. With respect to the
ethnicity of pedagogical agents, empirical results do not provide consistent results. In
both a computer-mediated communication and an agent environment, participants
who had similar-ethnicity partners than those with different-ethnicity partners pre-
sented more persuasive and better arguments; elicited more conformity to the part-
ners’ opinions; and perceived their partners as more attractive and trustworthy (Lee &
Nass, 1998). In a more recent study, Baylor and Kim (2003b) examined the impact of
pedagogical agents’ ethnicity on learners’ perception of the agents. Undergraduate
participants who worked with pedagogical agents of the same ethnicity rated the
agents as more credible, engaging, and affable than those who worked with agents of
different ethnicity. However, Moreno and colleagues (2002) indicated that the ethnic-
ity of pedagogical agents did not influence students’ stereotypic expectations or
Given their function for supporting learning, pedagogical agents must also
represent different instructional roles, such as expert, instructor, mentor, or learning
companion. These roles also may interact with the agent’s gender and ethnicity given
that human social relationships influence their perceptions and understanding in gen-
eral (Dunn, 2000). In a similar fashion, the instructional roles of the pedagogical
agents may influence the perceptions or expectations of and the social bonds with
learners. Along this line, Baylor and Kim (2003c, in press) showed that distinct roles
for pedagogical agents—as expert, motivator, and mentor—significantly influenced
the learners’ perceptions of the agent persona, self-efficacy, and learning.
Lastly, Norman (1994; 1997) expressed concerns about human-like inter-
faces. If an interface is anthropomorphized too realistically, people tend to form unre-
alistic expectations. That is, a too realistic human-like appearance and interaction can
be deceptive and misleading by implying promises of functionality that can be never
reached. On the other hand, socially intelligent agents are of “no virtual difference”
from humans (Vassileva, 1998) and can provoke “illusion of life” (Hays-Roth &
Doyle, 1998), thus impressing the learners interacting with a “living” virtual being
(Rizzo, 2000). So, we may inquire how realistic agent images should be to establish
social relations to learners. Norman argues that people will be more accepting of an
intelligent interface when their expectation matches with its real functionality. What
extent of agent realism will match learners’ expectations with agent functionality is
an open question, however.
Consequently, the relationships among pedagogical agent gender, ethnicity,
instructional role, and realism seem to play a role to enhance learner motivation (e.g.,
self-efficacy), self-regulation, and learning. The purpose of this research was to ex-
amine these relationships through two controlled experiments. Experiment I exam-
ined the impact of agent gender, ethnicity, and realism; Experiment II examined the
impact of agent gender, ethnicity, and instructional role.
2 Experiment I: Agent Realism, Gender, Ethnicity
2.1 Agent Design
Eight agent images were designed by a graphic artist based on the same basic face,
but differing by gender, ethnicity, and realism. The animated agents were then
developed using a 3D character design tool, Poser 5, and Microsoft Agent Character
Builder. Next, the agents were incorporated into the web-based research application,
MIMIC (Multiple Intelligent Mentors Instructing Collaboratively) (Baylor, 2002). To
control confounding effects, we used consistent parameters and matrices to delineate
facial expression, mouth movement, and overall silhouettes across the agents. Also,
except for image, the agents had identical scripts, voice, animation, and emotion. For
voice, we used computer-generated male and female voices. For animation, blinking
and mouth movements were included. Emotion was expressed using the scripts
together with facial expression, such as smiling. Figure 1 presents the images of the
eight agents used in the study.
Figure 1: Images of eight agents in Experiment I
Validation. In a controlled between-subjects study with 83 undergraduates, we vali-
dated that each agent effectively represented the intended gender, ethnicity, and de-
gree of realism.
Dependent Variables. Dependent variables included self-regulation, self-efficacy,
and learning and were identical for both Experiment I and II.
Self-regulation. Learners’ self-regulation was assessed through three Likert-scale
items: 1) I stopped to think over what I was learning and doing; 2) I kept track of my
progress; and 3) I evaluated the quality of my lesson plan. The students rated their
self-regulation on a five-point scale ranging from 1 (Strongly disagree) to 5 (Strongly
agree). Item reliability was evaluated as
Self-efficacy. Learners’ self-efficacy beliefs about the learning tasks were measured
with a one-item question developed according to the guidelines of Bandura and
Schunk (1981) for specificity. The guidelines emphasize that self-efficacy is the
degree to which one feels capable of performing a particular task at certain designated
levels (Bandura, 1986). The participants answered the question, "How sure are you
that you can write a lesson plan?" on a scale ranging from 1 (Not at all sure) to 5
(Extremely sure) after the intervention.
Learning. Learning was assessed by an open-ended question where the participants
had to transfer their knowledge to a new situation. The participants were asked to
write a brief instructional plan with the following prompt:
Applying what you’ve learned, develop an instruc-
tional plan for the following scenario: Imagine that
you are a sixth grade teacher of a mathematics class.
Your principal informs you that a member of the
president’s advisory committee will be visiting next
week and wants to see an example of your instruction
about multiplication of fractions.
The overall quality of a the answers were evaluated by two instructional designers,
who scored the students’ answers with a detailed scoring rubric on a scale ranging
from 1 (very poor) to 5 (excellent). Inter-rater reliability was evaluated as Cohen’s
Kappa = 0.95.
Sample. Participants included 312 pre-service teachers enrolled in an introductory
educational technology class in two large southeast universities in the United States.
Approximately 30% of the participants were male and 70% were female; 53% of the
participants were Caucasian, 33% were African-American, and 14% were others. The
average age of the participants was 20.54 years (SD=2.63).
Procedure. The experiment was conducted during a regular session of an introduc-
tory educational technology course. The participants were randomly assigned to one
of the eight agent conditions. They logged on the web site loading MIMIC (Multiple
Intelligent Mentors Instructing Collaboratively), which was designed to help the
students develop instructional planning. The participants were given as much time as
they needed to finish each phase of the tasks. The entire session took about an hour
with individual variations.
Design and Analysis. The study employed a 2 × 2 × 2 design, including agent gender
(Male vs. Female), agent ethnicity (Caucasian vs. African-American), and agent real-
ism (realistic vs. cartoon-like) as the factors. For self-regulation, a MANOVA (multi-
variate analysis of variance) was conducted. For self-efficacy and learning, analysis
of variance (ANOVA) was conducted. The significance level was set as
Self-regulation. MANOVA revealed a significant main effect for agent gender,
Wilks’ Lambda = .97, F (3, 287) = 3.45, p = .01, where the presence of a male agent
led to significantly more reported self-regulatory behavior than the presence of a
female agent. Follow-up post-hoc univariate analyses (ANOVA) revealed significant
main effects for each of the three sub-measures (all p<.05).
Self-efficacy. ANOVA indicated a significant main effect for agent gender where the
presence of the male agent led to increased self-efficacy, F(1, 289)=4.20, p<.05.
Analysis of additional Likert items revealed that students perceived the male agents
as significantly more interesting, intelligent, useful, and leading to greater satisfaction
than the female agents.
Learning. For all students (male and female) ANOVA revealed a marginally signifi-
cant main effect for agent realism on learning, F (1, 289) = 4.2, p =.09. Overall, stu-
dents who worked with the realistic agents (M = 3.13, SD = 1.05) performed margin-
ally better than students who worked with the cartoon-like agents (M = 2.94, SD =
1.1). Interestingly, a post-hoc ANOVA indicated a significant main effect for agent
realism where males working with realistic agents (M=3.50) learned more than males
working with cartoon agents (M=2.51, F(1,84) =6.50, p=.01. For female students, the
main effect for agent realism was not significant.
3 Experiment II: Agent Role, Ethnicity and Gender
3.1 Agent Design
For the second study, a different set of twelve agents, differing by gender, ethnicity,
and role, were designed using a 3D character design tool, Poser 5 and Mimic Pro 2.
These agents were richer than those in Experiment I, where the focus was on the
agent image. Consequently, to establish distinct instructional roles, it was important
to consider a set of media features that influence agent “persona,” including image,
animation, affect, and voice. Image is a key factor in affecting learners’ perception of
the computer-based agent as credible (Baylor & Ryu, 2003b) and motivating (Baylor
& Kim, 2003a; Baylor, Shen, & Huang, 2003; Kim, Baylor, & Reed, 2003). Anima-
tion includes body movements such as hand gestures, facial expression, and head
nods, which can convey information and draw students’ attention (Cassell, 1998;
Johnson, Rickel, & Lester, 2000; McNeill, 1992; Roth, 2001). Affect, or emotion, is
also an integral part of human intellectual and cognitive functioning (Kort, Reilly, &
Picard, 2001; Picard, 1997) and thus was deemed as critical for facilitating the social
relationship with learners and affecting their emotional development (Saarni, 2001).
Finally, voice is a powerful indicator of social presence (Nass & Steuer, 1993), and
so the human voices were recorded to match the voices with the gender, ethnicity,
and roles of each agent and with their behaviors, attitudes, and language. Figure 2
shows the images of the twelve agents.
Figure 2: Images of twelve agents in Experiment II
Experts Motivators Mentors
The agent-student dialogue was pre-defined to control for agent functionality across
students. Given that people tend to apply the same social rules and expectations from
human-human interaction to computer-human interaction (Reeves & Nass, 1996), we
referred to research on human instructors for implications for the agent role design.
Agent as Expert. The design of the Expert was based on research that shows that the
development of expertise in humans requires years of deliberate practice in a domain
(Ericsson, Krampe, & Tesch-Romer, 1993) and that experts exhibit mastery or exten-
sive knowledge and perform better than the average within a domain (Ericsson, 1996;
Gonzales, Burdenski, Stough, & Palmer, 2001). Also, experts will be confident and
stable in performance and not swayed emotionally by instant internal or external
stimulation. Based on this, we operationalized the expert agent through the image of a
professor in forties. His animation was limited to deictic gestures, and he spoke in a
formal and professional manner, with authoritative speech. Being emotionally de-
tached from the learners, his function was to provide accurate information in a suc-
cinct way (see sample script in Table 2).
Agent as Motivator. The design of the Motivator was based on social modeling
research dealing with learners’ efficacy beliefs, a critical component of learner moti-
vation. According to Bandura (1997), attribute similarity between the learner and
social model significantly affects the learners’ self-efficacy beliefs. In other words,
learning and motivation are enhanced when learners observed a social model of the
same age (Schunk, 1989). Further, verbal encouragement in support of the learner
performing a task facilitates learners’ self-efficacy beliefs. Thus, we operationalized a
motivator agent with a peer-like image of a casually-dressed student in his twenties,
considering that our target population was college students. Given that expressive
gestures of pedagogical agents may have a strong motivating effects (Johnson et al.,
2000), the agent gestures were expressive and highly-animated. He spoke enthusiasti-
cally and energetically, while sometimes using colloquial expressions, e.g., ‘What’s
your gut feeling?’ He was not presented as particularly knowledgeable but as an ea-
ger participant who suggested his own ideas, verbally encouraged the learner to sus-
tain at the tasks, and, by asking questions, stimulated the learners to reflect on their
thinking (see sample script in Table 2). He expressed emotion that commonly occurs
in learning, such as frustration, confusion, and enjoyment (Kort et al., 2001).
Agent as Mentor. An ideal human mentor does not simply give out information;
rather, a mentor provides guidance for the learner to bridge the gap between the cur-
rent and desired skill levels (Driscoll, 2000). Thus, a mentor should not be an authori-
tarian figure, but instead should be a guide or coach with advanced experience and
knowledge who can work collaboratively with the learners to achieve goals. Thus,
the agent as mentor should demonstrate competence to the learner while simultane-
ously developing a social relationship to motivate the learner (Baylor, 2000). Conse-
quently, the design of the Mentor included an image that was less formal than the
Expert, yet older than the peer-like Motivator. The Mentor’s gestures were designed
to be identical to the Motivator, incorporating both deictic and emotional expressions.
His voice was friendly and approachable, yet more professional and confident than
the Motivator. We operationalized the Mentor’s functionality to incorporate the char-
acteristics of both the Expert and Motivator, (i.e., to provide information and motiva-
tion); thus, his script was a concatenation of the content of the Expert and Motivator
Validation. We initially validated that each agent was effectively representing the
intended gender, ethnicity, and roles with 174 undergraduates in a between-subjects
design. The results indicated successful instantiations of the twelve agents.
Dependent variables were identical to those employed in Experiment I and included
self-regulation, self-efficacy, and learning.
Sample. Participants included 229 undergraduates enrolled in a computer literacy
course in a large university in the Southeastern United States. Approximately 39% of
the participants were male and 61% were female; 70% of the participants were Cau-
casian, 10% were African-American, and 20% were others. Approximately 39% of
the participants were male and 61% were female. The average age of the participants
was 19.39 (SD=1.64).
Procedure. The experiment was conducted during a regular session of a computer
literacy class. The participants were randomly assigned to one of the twelve agent
conditions. They logged on the web site loading a modified version of MIMIC (Mul-
tiple Intelligent Mentors Instructing Collaboratively), which was designed to help the
students develop instructional planning for e-Learning. The participants were given
as much time as they needed to finish each phase of the tasks. The entire session took
about an hour with individual variations.
Design and Analysis. The study employed a 2 × 2 × 3 design, including agent gender
(Male vs. Female), agent ethnicity (White vs. Black), and agent role (expert vs. moti-
vator vs. mentor) as the factors. For self-regulation, a MANOVA (multivariate analy-
sis of variance) was conducted. For self-efficacy and learning, analysis of variance
(ANOVA) was conducted. The significance level was set as
Self-regulation. MANOVA revealed a significant main effect for agent role on self-
regulation, Wilks’ Lambda = .94, F (6, 430) = 2.22, p < .05. Overall, students who
worked with the mentor or motivator agents rated their self-regulation significantly
higher than students who worked with the expert agent. MANOVA also revealed a
main effect for agent ethnicity on self-regulation where Black agents led to increased
self-regulation as compared to White agents, Wilks’ Lambda =.96, F(3, 205) =2.90,
Self-efficacy. There was a significant main effect for agent gender on self-efficacy, F
(1, 217) = 6.90, p <.05. Students who worked with the female agents (M = 2.36, SD =
1.16) showed higher self-efficacy beliefs than students who worked with the male
agents (M = 2.01, SD = 1.12). Analysis of additional Likert items revealed that stu-
dents perceived the female agents as significantly less knowledgeable and intelligent
than the male agents. There was also a significant main effect for agent role on self-
efficacy, F (2, 217) = 4.37, p =.01. Students who worked with the motivator (M =
2.37, SD = 1.2) and mentor agents (M = 2.32, SD = 1.2) showed higher self-efficacy
beliefs than students who worked with the expert agent (M = 1.86, SD = 0.94).
Learning. There was a significant interaction of agent role and agent ethnicity on
learning, F (2, 214) = 3.36, p <.05. Post hoc t-tests of the cell means indicated that
there was a significant difference between the Black (M = 2.61, SD =.75) and White
Experts (M = 2.13 , SD =.84, p<.01), indicating that the Black agents were signifi-
cantly more effective in the role of Expert than the White agents. This interaction is
illustrated in Figure 3. Additional analysis of Likert items regarding the level to
which students paid attention during the program revealed that students with the
Black Experts better “focused on the relevant information” ((M = 3.03, SD =1.08 vs.
M = 2.42, SD =1.11) and “concentrated” (M = 2.70, SD = .95 vs. M = 2.23, SD =
Figure 3: Interaction of Role * Ethnicity on Learning
Role * Ethnicity on Learning
White 2.13 2.24 2.44
Black 2.61 2.36 2.21
Expert Motivator Mentor
Results from Experiment I highlight the potential value of more realistic agent images
(particularly for male students) to positively affect transfer of learning. This supports
the value in designing pedagogical agents to best represent the live humans that they
attempt to simulate (e.g., Hays-Roth & Doyle, 1998; Rizzo, 2000). Even so, a variety
of permutations of agents with different levels of realism needs to be examined to
more fully substantiate this finding.
In Experiment II, the Black agents in the role of expert led to significantly
improved learning as compared to the White agents as experts, even though both had
identical messages. Students working with the Black experts also reported enhanced
concentration and focus, which could be explained by the fact that they perceived the
agents as more novel (and thereby more worthy of paying attention to) than the White
experts. Similarly, Black agents overall (in all roles) led to enhanced learner self-
regulation in the same experiment, perhaps because they also warranted greater atten-
tion and focus. In support of this explanation (i.e., that students pay more attention to
agents that represent non-traditional roles), we recently found that a female agent
acting as a non-traditional engineer (e.g., outgoing, highly attractive) significantly
enhanced student interest in engineering as compared to a more stereotypical “nerdy”
version (e.g., introverted, homely) (Baylor, 2004).
The importance of the agent message was demonstrated in Experiment II,
where the presence of motivational messages (as delivered through the motivator and
mentor agent instructional roles) led to greater learner self-regulation and self-
efficacy. This finding is supported by Bandura (1997), who suggests that such verbal
persuasion leads to positive motivational outcomes.
Our prior research has indicated that agents that are perceived as less intelli-
gent lead to greater self-efficacy (Baylor, 2004; Baylor & Kim, in press). This was
replicated in Experiment II since the female agents (who were perceived as signifi-
cantly less intelligent than the males) led to enhanced self-efficacy. Similarly, the
finding that the motivator and mentor agents led to greater self-efficacy could be
attributed to the fact that they were validated to be perceived as significantly less
expert-like (i.e., knowledgeable, intelligent) than the expert agents. While results
from Experiment I initially seem contradictory because the agents rated as most intel-
ligent (males) also led to improved self-efficacy, this can be attributed to an overall
positive student bias toward the male agents in this particular study (e.g., they were
rated as more useful, interesting, and leading to overall more satisfaction and self-
Overall, while the agent message is undoubtedly important, results support
the conclusion that a seemingly superficial interface feature like pedagogical agent
image plays a very important role in impacting learning and motivational outcomes.
The image is key because it directly impacts how the learner perceives it as a human-
like instructor; consequently, pedagogical agent designers must take great care in
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This work was sponsored by National Science Foundation Grant # IIS-0218692