Pedagogical Agents as Team Members: Impact ofProactive and Pedagogical Behavior on the User

Mukesh BarangeINSA de Rouen

Francemukesh.barange@insa-

rouen.fr

Julien SaunierINSA de Rouen

Francejulien.saunier@insa-

rouen.fr

Alexandre PauchetINSA de Rouen

Francealexandre.pauchet@insa-

rouen.fr

ABSTRACTIn a virtual environment for the learning of cooperativetasks, learners have to coordinate with one or more au-tonomous agents in order to perform a task. This coor-dination requires that the human and agent team membersreason and dialogue about their resources, action plans, andshared actions. This article proposes a new agent architec-ture called PC2BDI designed to generate reactive, proactive,and pedagogical behaviors of the agents based on three agen-das: the task, the dialogues, and the pedagogical scenario.An experimental study, in the context of learning a pro-cedural activity in a virtual environment involving 3 teammembers (1 human and 2 agents), is presented to evaluatethe effect of the agent behavior on a learner. The resultsshow that using proactive pedagogical agents improves thelearner’s engagement and task learning.

KeywordsHuman-Agent interaction, pedagogical agent, virtual envi-ronment

1. INTRODUCTIONIn the context of a collaborative virtual environment (VE)

for training and learning, coordination requires that teammembers, both humans and virtual agents, reason and di-alogue about their actions, resources and shared plans [5].However, coordination in such a context of mixed human-agent teamwork faces a number of issues. Firstly, one ofthe important characteristics to work in a team is that theteam members proactively provide information by anticipat-ing information needs of the other members [21] to main-tain a state of mutual awareness. Furthermore, in a mixedhuman-agent team, agents provide pedagogical assistance sothat the learners can reach their learning objectives. Thesepedagogical agents enable proactivity [27], learner’s engage-ment in the VE for learning [14], and interaction amongteam members [19]. Secondly, the natural language commu-nication among team members requires not only to take intoaccount the current dialogue, but also the shared task andthe beliefs of other team members [31]. Multiparty dialoguesencourage group interaction and coordination among team

Appears in: Proceedings of the 16th International Confer-ence on Autonomous Agents and Multiagent Systems (AA-MAS 2017), S. Das, E. Durfee, K. Larson, M. Winikoff(eds.), May 8–12, 2017, Sao Paulo, Brazil.Copyright c© 2017, International Foundation for Autonomous Agents andMultiagent Systems (www.ifaamas.org). All rights reserved.

members. This advantage can be exploited to promote typ-ical human activities such as (a) the learning in more socialcontexts and (b) the construction and maintenance of socialrelations between the members of a team.

During the last decade, several research projects have fo-cused on designing agents in augmented VEs, their usage,their efficiency and limitations [4, 26, 33]. Some agents aredeveloped and incorporated into VEs so that they appearmore alive. Others possess limited but sufficient rational-ity, in order to increase the learners’ involvement in VEs.However, these agents never possess both collaborative andpedagogical behavior to offer effective assistance to learnersin the context of learning procedural activities in a task-oriented, mixed human-agent teamwork.

This article focuses on the task-oriented, collaborative andpedagogical behavior of agents in a VE designed to learna procedural task. Multiparty dialogues between the userand pedagogical agents are exploited to improve the en-gagement in interactions and to proactively provide informa-tion needed by the user. Other aspects of embodied virtualagents, such as emotions and non-verbal communication areout of the scope of this article. In this context, we proposethe (PC2BDI) architecture of a pedagogical agent, that ex-tends the (C2BDI) agent architecture [3]. On the one hand,it allows the interleaving between deliberative and conversa-tional (reactive and proactive) behavior for the realizationof collective activities and information sharing among teammembers. On the other hand, it offers pedagogical behaviorto provide appropriate assistance to the learner. An experi-mental study in the context of learning a procedural activityin a VE involving 3 team members (1 human and 2 agents)is performed to examine the effects of the behaviorial capa-bilities of an agent on a learner.

In section 2, we present related work concerning exist-ing agent architectures and pedagogical behavior of agents.Section 3 describes the architecture of the agents and theircomponents. The evaluation of the effects of agent behaviorson the learner is presented in section 4 and the discussionon the results is described in section 5. Finally, section 6summarizes the contribution and concludes the article.

2. STATE OF ARTCollaborative VEs for training allow learners to acquire

new skills or knowledge that can be applied in real situ-ations [14]. Some of the applications of these VEs includeprocedural task learning [26], decision-making during criticalsituations [38] and risk management training [4]. In VEs, thelearners can perform activities accompanied by other users

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or by virtual agents. These autonomous virtual agents, usu-ally having some or all of the classic cognitive abilities suchas decision-making, memory and planning are called Intelli-gent Virtual Agents (IVAs) [13]. They can also be EmbodiedConversational Agents (ECAs), with a physical representa-tion and able to demonstrate some human-like skills, suchas face-to-face interaction, gestures, facial expressions, emo-tions and personality [9, 29]. Agents which can act as tutorsor motivation vectors in a VE for learning are known aspedagogical agents [36]. Pedagogical agents can be linked tovarious cognitive theories [23], can have a natural interactioncapability [9] and improve social engagement [16].

Several agent architectures exist that are potential can-didates for designing collaborative and pedagogical agentsin VE for learning1. For example, STEVE agent [33] devel-oped on SOAR architecture, can explain and demonstratethe task to students and can work as the missing team mem-ber. However, there is only question/answer, but no dia-logue management. Similarly, Paco agent [34] is built uponthe collaborative discourse theory [32] and STEVE. It sup-ports both collaboration and tutorial dialogues, however, itlacks of planning and deliberation capabilities. Other po-tential architectures for pedagogical agents can be IVAs orECAs. Greta agent [29] is based on SAIBA framework, sup-ports multimodal interactions between an agent and theuser, and can improve the user’s engagement. However,Greta also does not endow dialogue management and ca-pability to collaborate. Finally, the C2BDI [3] agent archi-tecture is based on BDI model [30] that contains mentalattitudes such as belief, desire and intention. The conversa-tional behavior integrated in C2BDI is based on the Infor-mation State (IS) [40] that contains contextual informationabout the current conversation. Furthermore, this archi-tecture also supports multiparty interaction between a userand many agents. Similarly, Kopp and Pfeiffer-Lessmannalso proposed an IS based interaction model for Max agent[24]. They considered coordination as an implicit character-istic of team members. These different agent architecturesexhibit different capabilities and limitations. For example,the decision centric architectures mainly concerned with theplanning and decision-making, where as dialogue manage-ment remains a major deadlock in IVAs [38]. Most of theexisting embodied only integrates basic dialogue manage-ment processes. Thus, in the context of a collaborative VEfor learning, neither decision centric not embodied architec-tures can be directly applied. Furthermore, these architec-tures do not allow to easily integrate a pedagogical behaviorto the agent. Therefore, these agents cannot be directlyused as pedagogical agents in the context of the learning ofa task-oriented shared activity.

Pedagogical agents exhibit different characteristics: (a)they are adaptive [6], (b) they provide realistic simulations[33], (c) they address socio-cultural needs of learners [23],(d) they encourage learner’s engagement and motivation [16]and (e) they improve learning and performance [14]. How-ever, various studies conducted by Doering et al. [11], Choiand Clark [8], Schroeder et al. [36], and Veletsianos andRussell [42] concluded that these arguments are not alwaysjustifiable. For example, although Doering et al. [11] statedthat the pedagogical agents are adaptive, they also noticedthat firstly, in most cases these agents sometimes fail to

1A number of pedagogical agents are discussed in Johnsonet al. [20].

provide certain information in response to questions of thelearner and secondly, they sometimes provide informationinappropriately. Moreover, Choi and Clark demonstratedthat simply adding pedagogical agents in a VE does not pro-vide better results [8]. Indeed, the observed gain in user’slearning is generally attributed to the pedagogy used by theagent rather than to the agent itself.

In addition, agent profiles based on reactive or proactivebehavior also affect the overall performance of a learner inVEs [2]. Kim et al. [22] found that the pedagogical agentswith visual appearance and communication capabilities givepositive effects on learners. Although different aspects ofan agent such as reactive, proactive and pedagogical be-havior have been explicitly evaluated, the combined effectsof reactive and proactive behaviors of an agent along withpedagogical behavior have not been evaluated. Schroeder etal., have concluded in their meta-analysis for the effective-ness of pedagogical agents for learning, that evaluation andcomparison of pedagogical agents remain difficult because oftheir different roles, the complexity of interactions, differentagent modalities, and the way experiments are designed forevaluation [36]. Thus, the use of pedagogical agents in vir-tual learning situations requires a thorough evaluation of thecapabilities of the agents and of the opinions of the users.

To summarize, many different applications of collabora-tive VE for learning share common aspects in terms of aware-ness (consciousness and attention) of the human and agentsteam members [28], ability to share information [25], and ef-fective team coordination [5]. Thus, it is important to takeinto account first, how team members (human and virtualagents) coordinate with each other, second, how they sharetheir knowledge in order to establish common grounding andmutual awareness among them and third, how agents pro-vide effective assistance to the learner. We propose an agentarchitecture that provides the deliberative and natural lan-guage interaction, as well as the pedagogical behavior toassist the learner in a collaborate VE for training. Similarlyto Rich and Sidner [31], we are convinced that the dialoguescan be managed in a deliberative way considering the collab-orative task resolution in a human-agent teamwork. BDI-like architecture is a prominent choice, because it is basedon the mental attitudes, and its procedural representation isclose to the implementation. Concerning the evaluation, weaim to evaluate the effects of agent behaviors on the learnerbased on different aspects such as the engagement of thelearner, learning gain, and the learner’s opinions about themotivation of using virtual agents in a VE for learning.

3. THE PC2BDI AGENT ARCHITECTUREThe proposed Pedagogical Collaborative-Conversational

BDI (PC2BDI) agent architecture is a cognitive architec-ture that is inspired from the Belief, Desire, Intention ar-chitecture (BDI) [30], and in particular with its proceduralimplementation [43]. The collaborative and proactive be-haviors of the PC2BDI agent are built upon the joint in-tention theory [10], shared plan theory [15] and the theoryof collaborative problem solving [44] along with the seman-tics of collective attitudes [12]. This architecture extendsshared plan theory by introducing multiparty informationsharing, anticipating the need of collaboration, and propo-sitions for deriving natural language communication behav-iors. PC2BDI treats both deliberative and conversationalbehaviors uniformly as guided by the goal-directed shared

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activity. It is based on the (C2BDI) agent architecture [3]and adds a pedagogical layer to generate new behaviors.

3.1 Components of the Agent ArchitectureThe architecture is composed of four layers for interaction,

accomplishment of shared activity and communicative goals,knowledge management, and pedagogical behavior (Fig. 1).

The interaction layer is responsible for the interface withthe VE. It allows the agent to interpret the behavior of oth-ers in order to adapt its own behavior based on its decision-making process and on the dialogue manager.

Audio /text

Control Flow Data Flow

Virtual World

Avatar

Audio /text

Dialogue Manager

Behavior Realizer

Perception

Decision Making

Task Planner

Turn Handler

NLU NLG

Pedagogical Module Pedagogical Library

Interaction layer

Cognitive layer

Belief Revision

Semantic Knowledge

Perception Memory

Knowledge Base Information State

(Context model)

Dialogue Semantic

Cognitive Perceptual

Social Task

Knowledge layer

Pedagogical layer

Figure 1: PC2BDI Agent Architecture

The cognitive layer determines how the agent maintainscollaboration with other team members. The task plan-ner determines the appropriate actions by referencing theshared activities and the roles of each member. The decision-making process is motivated by the goals, plans, IS and se-mantic knowledge about the VE and the task. The multi-party Dialogue Manager (DM) [3] is founded on a model-based approach to interpret and generate natural languageutterances relying on the current state of the IS. The DMclassifies the intentions of received utterances and the proac-tive intentions determined by the decision-making, whichcan be associated to pedagogical primitives. It then passesthe control to the pedagogical module. The multiparty turn-taking is derived from the Ymir architecture [39], using theangle and distance between the agent and the other teammembers, and his own intention to speak.

The knowledge layer manages the knowledge base that in-cludes semantic knowledge, perceptual knowledge, and IS.Each agent shares the same semantic knowledge about theVE and shared activities in order to simplify the planningprocess. The IS contains contextual information about thecurrent dialogue and about the current task, and the mutualbeliefs shared among team members. The semantic model-ing of the VE is based on the methodology for designingadaptive VEs for training proposed in [35]. This method-ology provides distinct roles for design actors such as peda-gogues (defining the pedagogical primitives), domain expert(describing the task model), designers (constructing VEs)and trainers (defining pedagogical scenarios).

The pedagogical layer brings the capability to provide as-sistance to learners either according to their demands orproactively by determining their needs. It uses the pedagog-

Technician: can you take the endoscope

Sebastien: Ok, I will take

[Sebastien executes the action of taking endoscope] Technician: what are you doing

Sebastien: I am trying to validate tools

Technician: can you take the spray

Sebastien: Ok, I will take [Sebastien currently executing the action of taking spray]

Request for an action Information seeking Request for an action

[A]

[B]

[C]

Figure 2: Reactive Conversational Behavior

ical actions described by the pedagogue to guide or correctthe learner. It then passes the control to the DM to generateappropriate utterances and feedback when required.

3.2 Conversational Behavior of the AgentCooperation between a learner and accompanying virtual

agents is supported through shared actions [10] and sharedplans [15] that are synchronized through dialogues. Theagent has both reactive and proactive dialogue capabilitiesthat rely on the semantic modeling of the VE and task ac-tivities using the MASCARET meta-model [7]. Reactiveconversational capabilities allow the agent to understandutterances and answer. Thus, the learner requests piecesof information to progress toward the goal. The agent canuse all the knowledge contained in the different models withrespect to the activities, roles, actions, resources and otherobjects in the VE, their properties and operations, to pro-vide appropriate response. For example, in figure 2 [B], thelearner asks about the current action of the technician Se-bastien (information seeking). Moreover, the learner canalso ask agents to perform certain actions during the task.For example, in figure 2 [C] the learner asks the technicianto take the spray bottle.

The agent can also proactively communicate with otherteam members in order to establish or maintain cooperation,to satisfy the anticipated information needs of the learner orhandle the sharing of resources. For example, in Fig. 3 [B],when the learner does not start her next action, the agentexplicitly and proactively asks her to perform this action.

3.3 Pedagogical Behavior of the AgentThe agent plays different roles during the collective activ-

ity. Each agent can participate as an ”equivalent” memberof the team, and each agent can also provide the necessarypedagogical assistance to the user depending on the currentcontext of the task. Pedagogical actions may be played bythe agent or by the VE player directly. The pedagogicalaction library developed until now contains the followingaction categories: 1. pedagogical actions on the VE: high-light an object, play an animation, 2. pedagogical actions onuser interactions: change the point of view, block a position,3. pedagogical actions on the structure of the system: de-scribe the structure or an element of this structure; displayan entity’s documentation, 4. pedagogical actions on the sys-

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tem dynamics: explain the objectives of a procedure, explainan action, 5. pedagogical actions on the scenario: display apedagogical resource such as a video or text document, ex-plain the objective of the current scenario

The agent can assist the learner in one of the followingconditions. Firstly, when the pedagogical action is explic-itly specified by the teacher in a training scenario [35], theagent can then interpret this action in an appropriate prim-itive pedagogical action and pass control to the DM. Sec-ondly, the agent may determine the information needs ofthe learner, or understand when the learner explicitly asksfor information. For example, if the learner asks the agent”where is the endoscope?”, the agent can highlight the en-doscope and can provide the description of that object (seeFigure 3 [C]). The agent can answer differently to the samequestion depending on the current context of the task and onthe level of the learner (beginner, intermediate or expert).For example, if the learner repeatedly asks the same questionabout the endoscope, the agent can describe its operation,its different elements and in which actions it can be used.Similarly, if the current context of the task or the dialoguehas been changed, the response to this query varies.

S1

S2

S3

[B] Proactive request for an action

[A] Proactively informing about the user’s next action

[C] Pedagogical action (highlighting an object)

Sebastien : you will remove boxes. Sebastien : Florian, can you verify the weather?

Technicien : Where is the endoscope? Sebastien : We can find it there.

[Sebastien turns towards the endoscope and highlight it]

Figure 3: Proactive conversational behavior andpedagogical behavior: Highlight an object

The agent determines the need for pedagogical informa-tion of the learner according to the ongoing dialogue withthe learner or via the actions performed by the learner. Ifthe learner is a beginner, the agent can give information onthe next action to be performed and resources to be used forthis action (see Figure 3 [A] for instance). In addition, theagent identifies the need for assistance according to learners’behavior based on the number and types of errors made byuser during previous actions. The agent classifies events gen-erated by the learner into different categories (erroneous ac-tion and wrong resource as defined in [17]) depending on thecurrent context of an activity. The agent then associates apedagogical primitive appropriate to the type of event. Thisinformation is transmitted to the DM. The DM processes itand generates an utterance corresponding to the pedagogi-cal action. For example, if the user makes “resource errors”and does not start the next action, the agent informs himabout the next expected action and highlights concerned re-sources without being explicitly asked. If the user performs“action errors”, the agent informs him about the next ex-

Figure 4: [A] Industrial scenario: Maintenance Ac-tivity, [B] Experimental scenario: Moving a trunk

pected action and how to perform it. Finally, if the userdoes not make any error, the agent only informs him aboutthe action to be performed.

DiscussionThis architecture has been integrated into a learning VE forthe maintenance of a wind turbine. A PC2BDI agent is as-sociated to each virtual character. Figure 4 shows a screen-shot of the maintenance scenario, where two technicians (anavatar of the learner and a virtual agent also playing the roleof tutor) collaborate to perform a collaborative activity.

The originality of the proposed architecture, compared to”pure” BDI, lies first on the role of dialogues that modify to-gether the believes, desires and intentions of the agent, andsecond on the collaborative nature of the agent’s activity.Compared to the context model of Max agents, PC22BDIagent exhibits both reactive and proactive conversational be-haviors, and explicitly handles cooperative situations throughnatural language interaction between team members takinginto account the user in the loop. Like STEVE, PC2BDIagent exhibits decision-making mechanism that allows theinterleaving between deliberation, conversational and peda-gogical behavior of the agent. Moreover, unlike Paco, thePC2BDI agent also supports IS based task-oriented multi-party dialogue management capability. Furthermore, thisapproach consists in formalizing the conversational behav-ior of the agent related to the coordination of the activityand the support of the learner, which reduces the necessityto explicitly define communicative and pedagogical actionsin the activity scenario.

4. EXPERIMENTAL EVALUATIONThe objective of this experiment is to gain insight into the

role of agent behaviors on the learner (user) in the context ofa task-oriented collaborative activity in a VE for learning.To evaluate the effects of adding both proactive and ped-agogical behaviors to the agents, we have considered twosystems with different experimental conditions. These con-ditions are based on the reactive, proactive and pedagogicalnature of agent behavior, and allow comparing the effectsof reactive and proactive behavior along with the pedagog-ical behavior of an agent. In both conditions, autonomousvirtual agents can work as equivalent team members with alearner in a collective activity. In the first condition, agentsare reactive and can provide pedagogical information to thelearner. However, they are not proactive, which means thatthey can react only on the user’s initiative for the request ofinformation or for the help to perform an action. In contrast,in the second condition, agents also exhibit proactive be-

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havior and thus, they can determine the user’s informationneed during the realization of a task and can provide help.Thus, agents can establish effective cooperation proactivelyduring the shared task. In both conditions, these agentsare endowed with the pedagogical behavior along with theirdedicated reactive or proactive behavior in order to help theuser to achieve their learning objectives.

We consider three aspects of evaluation which include (1)engagement and motivation of the learner, (2) task learningand (3) learner’s opinion on the profiles of the agent. Inorder to evaluate these aspects, we have defined followingthree hypotheses:

• Hypothesis 1: By providing the pedagogical infor-mation during the procedural activity, the proactiveagent improves the user’s learning experience.

• Hypothesis 2: Subjects who begin the experimentwith the scenario having proactive behavior of the agenthave better results in terms of learning than those whobegins with the scenario having only reactive agents.

• Hypothesis 3: The proactive pedagogical profile re-duces the overall learning time.

4.1 Method

4.1.1 ParticipantsSince the VE’s goal is to train students to a procedural

task, a call was made through which 16 students from anengineering school in France were recruited. To ensure theconsistency of the study panel, we imposed a controlled con-dition that the participants must be native French speaker.There were 11 men and 5 women between the ages of 19 and23 years (mean 20.41 years, SD = 1.37).

4.1.2 Data CollectionIn order to evaluate the impact of the behavior of the

agent, several subjective and behavioral measures were used.Subjective measures are carried out using questionnaires intwo parts (adapted from [37]). The first part is based onthe user’s engagement and commitment towards the sharedtasks (Table 1) and the second part focuses on opinions ofthe user on the motivational behavior (Table 2).

Table 1: Questionnaire on the engagement and mo-tivation towards the shared task

Questionnaire1 I was more attentive thanks to virtual agents.2 Agents have been useful to me to learn the task.3 I felt that agents encourage me to interact more with the

VE.4 I felt that agents encourage me to interact more with them.5 Overall, agents helped me in my learning.

These subjective measures are complemented by behav-ioral measures to evaluate the learning process. These mea-sures include the execution duration of actions, the totaltime required by the user to perform a task, the numberof consultations for actions, the number of errors during anaction, and the dialogue interaction between team members.

4.1.3 ProcedureThe evaluation process involves three steps. In the first

step before the experiment, participants were informed on

Table 2: Questionnaire on the user’s opinionQuestionnaire

6 Thanks to information provided by the agents, I was moremotivated in my task.

7 I think that the interaction in natural language supportsthe learning activity.

8 The vocal capacity of the agents is important.9 Collaborative VEs for learning are more interesting when

accompanied by collaborative and pedagogical agents.

the general context of the activity to interact with virtualagents who are members of the team and about the generalcourse of interactions, but no description of the procedurewas given to participants. Figure 4 [B] shows a screenshot ofthe evaluation scenario where three technicians (an avatarof the learner and two virtual agents also playing the roleof tutor) are collaborating to perform a collaborative activ-ity. The scenario takes place in the workshop of a windturbine company, where technicians present themselves toeach other, discover their maintenance task, check if theweather is acceptable, remove obstacles, choose appropriatetools and reach the wind turbine (six sub-activities).

In the second step, participants are invited to perform ex-periments. In each experiment, there are three members ofthe team (an avatar controlled by the learner and two au-tonomous agents Sebastien and Pierre located in the work-shop of a wind turbine company. Each participant is askedto perform experiments in one of the two sequences:

Sequence 1: (Exp1.1) Reactive + Pedagogical –> (Exp1.2)Proactive + Pedagogical

Sequence 2: (Exp2.1) Proactive + Pedagogical –> (Exp2.2)Reactive + Pedagogical

Subjects recruited to sequence 1 (Seq1) had to performExp1.1 followed by Exp1.2. Similarly, subjects enrolled insequence 2 (Seq2) had to perform Exp2.1 followed by Exp2.2.Characteristics of agent’s profiles are summarised in table 3.

Table 3: Characteristics of agent’s profilesAgent Profile Characteristics of scenarioReactive +Pedagogical

Virtual agents will have both reactive andpedagogical behavior

Proactive +Pedagogical

Only Sebastien will have both proactive andpedagogical behavior. Pierre will have peda-gogical behavior. In addition, both Sebastienand Pierre exhibit reactive behavior.

After executing the scenario, participants fill the question-naire on their experience (Table 1). The questions assess-ment is made on a Likert scale of 5 categories (1- stronglyagree, 2- agree, 3- neutral, 4- disagree and 5- strongly dis-agree). After the end of the experimental sequence, the ques-tionnaire on their opinions regarding motivation to the taskand interaction with agents is filled (Table 2).

4.1.4 Design AnalysisIn this study, since we have 16 participants (therefore 8

participants in each sequence), in order to obtain cumulativeresults, we decided to split the Likert scale in two groups (thefirst group with values of strongly agree + agree, and thesecond group with the last three scales). Given the multiple

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dependent measures, we choose to use Fisher’s exact test tocalculate two-tailed value p. In addition, the result is alsoverified using the Chi-Square with Yates correction due tothe small sample size (2 × 2). We also used the Student’stest (t-test) to verify two-tailed hypothesis regarding theexecution time of actions required by users. The significantlevel for all of the analysis was set to 0.05.

4.2 ResultsThe first experiment of each sequence is used to evaluate

the effects of agent’s reactive/proactive behavior on engage-ments of the learner as well as on the shared task.

4.2.1 Engagement of the LearnerWe want to assess the engagement of the user from three

points of views of user (attention, utility and encourage-ment for interactions). Figure 5 shows the reaction of usersregarding their engagement with virtual agents in a VE.

The first question represents the learner’s willingness inthe group activity. By analysing the questionnaire, we foundthat only 13% participants in Exp1.1 have noted that theywere more attentive because of virtual agents, while in Exp2.1,88% participants felt that they were more attentive (Fig. 5).We have verified this result using statistical methods. Thevalue of Fisher’s exact test is p = 0.0101. We also confirmedthe result by applying the Chi-Square approach (Chi-Squarestatistic is 9 and p = 0.0027), this result is significant at p<0.05. As the participants had no information about ac-tions to be performed in the shared activity, informationproactively provided by the agent absolutely sustained theirlearning. Consequently, the agent led learners to perceivetheir learning goals and thus complete their task.

Figure 5: Engagement of the learner

The second question is related to the focus of the learnerabout the task. Only 13% participants in Exp1.1 foundagents useful for learning, while 88% users in Exp2.1 foundagents useful (Fig. 5). This result is significant at p <0.05 as in the Fisher’s exact test p = 0.0101 and Chi-Squarestatistic is 9 with p = 0.0027. The reason is that in Exp1.1both agents are reactive, so they do not cooperate with theuser and do not provide information without being asked.This explains that the users do not find them very useful.In the case of Exp2.1, one of the agents is proactive andtherefore provide the necessary information.

The third issue concern the encouragement of learners toparticipate in the shared task. Only 25% of users in Exp1.1found the agents encourage them to interact with the VE,while 88% of users agreed upon this point 5). This resultis significant at p < 0.05 as in the Fisher’s exact test p =0.006993, and Chi-square statistic is 9.6 and p = 0.01946.The reason is that in Exp1.1 both agents are reactive, so theydo not provide any information about actions and resourceswithout being asked. However, in the case of Exp2.1, one

of the agent is proactive and can thus provide the necessaryinformation to perform actions which henceforth motivatesthem to interact with VE.

These findings support hypothesis 1: by providing peda-gogical information during the procedural activity, the proac-tive agent enhances the user’s learning experience.

The fourth question concerns the encouragement of learn-ers to interact with agents. 75% of participants in Exp1.1agreed that the reactive agents encouraged more to inter-act with them, while only 50% of participants agreed in thesecond case (Fig. 5), in the Fisher test p = 0.363636 andChi-Square statistics 1.0667 and p = 0.3017. This result isnot significant at p < 0.05. This result is counter-intuitive:the user does not have information about the task, and inorder to acquire this knowledge, she must interact with otherteam members to know about next actions to perform. How-ever, in the proactive scenario (Exp2.1), the proactive agentcan anticipate the user’s information needs and provide in-formation, even without request from the user. This resultdoes not support the hypothesis 1.

Question five evaluates the overall contribution of peda-gogical behavior of an agent on the learner’s interaction inthe VE (interaction with objects, other team members, andperforming actions). 75% of users thought that the proac-tive pedagogical agents helped them in learning during thecollective activity, while only 25% of users approved thisfact in the reactive scenario (Fig. 5). The statistical valueof Fisher’s exact test is p = 0.040559. The statistical valueof the Chi-Square is 6.3492 and p = 0.011743. This result issignificant at p <0.05 and support the hypothesis 1.

The results show that hypothesis 1 is supported only in thefirst Exp2.1 because firstly, the agent can proactively pro-vide the information required by the user to advance towardthe goal and secondly, both agents can provide pedagogicalassistance depending on the level of the learner. These char-acteristics motivate the learner to actively participate in theshared activity.

The statistical analysis on the execution time of actionsrequired by users in the first experiment is done for the twosequences (Fig. 6 [A] [B]). Applying the Student’s t-test forindependent means, we get t = 1.18902 and p = 0.246064.The result is not significant at p <0.05. Although there wasno significant difference between the execution time of thesetwo activities, the total time required by the user to per-form actions was higher in the case of reactive pedagogicalscenario (in Exp1.1, mean execution time = 26.57 min, andin Exp2.1, mean execution time = 13.05 min). One of thereasons is that in the case of proactive pedagogical scenario,the communication cost (time) is higher than the reactivepedagogical case. Since, in the first experiment of the twoexperimental sequences, the user does not know the sequenceof actions in advance, while in the case of proactive peda-gogical scenario the agent provides pedagogical informationto the user to advance the collective activity.

4.2.2 Task LearningIn this section, we study the effects of the order of ex-

periments (Seq1:: Exp1.1 –> Exp1.2 or Seq2:: Exp2.1 –>Exp2.2) to evaluate the learning curve (time) between thesetwo sequences. We firstly evaluated the learning gain withthe first experimental sequence (reactive –> proactive) (Fig.6 [A]). By applying the Student t-test for dependent meansof the execution time of actions required by the user, we

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found that t = 2.123776 and p = 0.055163. This means thatthe result is not significant at p <0.05. However, the totaltime required by the user in the scenario Exp2.2 is signifi-cantly lower (mean time = 6.85 min) than that of the firstexperiment in the reactive mode. Furthermore, there was nosignificant difference between proactive experiences (Exp2.1and Exp1.2). Total time required in Exp1.2 (mean time =10.6 min) was lower than that of Exp2.1. This result sup-ports one of the conclusions presented by Ganier, Hoareauand Tisseau [14] that the user learns the procedural actionsin the VE by performing several times the same activity.

We now compare the time taken by the user to performactions in the second experimental sequence (proactive –>reactive) (Fig. 6 [B]). By applying the Student t-test onthese two dependent means we obtain t = 2.206821 and p =0.047551. The result is significant at p <0.05. In addition,the total time required in the Exp2.2 (mean time = 6.85 min)was lower than that of Exp1.2 (mean time = 10.6 min).

With this result, we can conclude that learning is moreefficient in Seq2 compared to Seq1. In other words, the useof proactive pedagogical agents in the initial phase improvesthe procedural tasks learning. We can therefore concludethat these results support the hypothesis 2 which states thatsubjects who begin the experiment with scenario having theproactive behavior of the agent have better results (in termsof learning), than those who begin with reactive agents.

In addition, we also noticed that although the experi-ments Exp1.2 and Exp2.2 are not significantly different (t= 0.77183 and p = 0.447749), the learning gain betweenthe two experiments of each sequences was significant at p<0.05 (t = 1.7314 and p = 0.048109) (Fig. 6 [C]). Theseresults support hypothesis 3 indicating that the proactivepedagogical behavior reduces the overall learning time.

4.2.3 Opinions of UsersWe now evaluate the opinions of users regarding the mo-

tivation of using virtual agents in a VE for learning. Figure7 shows the comparison of opinions of users on their mo-tivation towards the task, the use of natural language andverbal ability and their overall opinion with respect to theutility of pedagogical virtual agents for learning.

We now analyse the results concerning the motivation ofthe learner to complete the collective activity (question 6).In both experiments, participants agreed that they were mo-

Figure 7: Opinions of Users

tivated to progress in the task thanks to the informationprovided by agents (Fig 7). Statistical analysis also sup-ports the fact that there is no significant difference in theiropinions (Chi-Square statistics is 2.2857 and p = 0.13057).

We can also conclude that the agent provides positive mo-tivations and effects on learners through assistance and in-formation. This conclusion may not be true for the eval-uation requirements having only reactive agents such as inExp1.1. These agents do not take initiative to share infor-mation. Nevertheless, if the learner requests, agents canprovide information. Their behavior do not motivate andencourage learners to continue the shared activity. How-ever, this conclusion is valid for the evaluation conditions ofhaving proactive agent behavior such as in Exp2.1. Agentscan engage in proactive way, and may establish and main-tain collaboration within the team. Furthermore, informa-tion provided by proactive agents also encourage learners toactively participate in the shared task. Pedagogical agentsprovide help to reach the learning objectives as well as estab-lishing and maintaining coordination among team members.

Questions 7 to 9 are interested in the personal view of thelearner and to assess the importance of pedagogical behav-ior for learning (Fig. 7). There is no significant differencein the opinion that the interaction in natural language sup-ports the learning activity (Seq1 83%, Seq2 83%). However,users in both experiments accepted that the verbal abilityis important (as in Fisher exact test p = 0.2, the opinionis not significantly different at p <0.05). Users have agreedupon the fact (Seq1 75%, Seq2 100%) that VEs are moreinteresting for learning when accompanied by collaborativeand pedagogical agents (Fisher exact test statistical value ofp = 0.466667).

In addition, we analysed traces of dialogues between usersand agents. The number of conversations initiated by theuser was much higher (maximum 28, average 20) in Exp1.1where both agents are reactive pedagogical, than that inExp2.2 (maximum 12, average 9) where one of the agentsis proactive pedagogical. Information search is faster withproactive pedagogical agents than with only reactive agents.The reason is that proactive pedagogical agents provide in-formation needed by the user and reduces the possibilitythat the user asks for this information. However, in the caseof having only reactive agents, the user must take initiativesto request information. In response, the agent can provideinformation or pedagogical assistance to progress the task.We also examined the traces of dialogue and observed thatthe user was more prone to interact with Sebastian in boththe reactive and the proactive (73% of the user initiated di-alogue interactions with Sebastien). The reason for this be-havior is that in both scenarios, Sebastien introduced othermembers of the team with each others and also, Sebastian is

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proactive in Exp1.2 and Exp2.1, which leaves the impressionof providing information during the learning activity.

5. DISCUSSIONThis study aims to analyse the effect of agent behaviors

on the user to learn procedural activities in VEs. We testedthree hypotheses. The first hypothesis states that by pro-viding the pedagogical information during the proceduralactivity, the proactive agent improves the user’s learningexperience. This hypothesis was supported partly by the re-sults. The results conclude that the users are more attentivewith proactive pedagogical agents and found them helpfulduring the procedural task learning in VE. However, usersfelt that having only reactive agents encouraged them to in-teract with the agents. Furthermore, the results supportedthe second hypothesis stating that the users who begin theexperiment with scenario having the proactive behavior ofthe agent have better results (in terms of learning), thanthose who begins with having only of reactive agents. Fi-nally, the third hypothesis that the proactive pedagogicalprofile reduces the overall learning time is also completelysupported by results.

One of the important findings is that participants weremore engaged with the agent having a proactive behavioreven if in each experimental sequence, both agents had ped-agogical behavior. In addition, agents can provide pedagog-ical assistance according to the level of the learner. Thesecharacteristics motivate the learner to actively participatein the shared activity. The results also showed that proac-tive pedagogical agents have a higher impact on learningwhen participants have to learn the procedural task in col-laborative VE for training. Participants who begin withthe scenario having proactive agents required less numberof consultations and less time to complete the task in thesecond trial than those who begin with the scenario havingonly reactive agents. These results are also consistent withthe results in [14] on the procedural task learning phase ina collaborative VE for training and learning.

Proactive pedagogical behavior of the PC2BDI agent alsoreduces the effort of complex scenario design. The PC2BDIagent can produce pedagogical or communicative actions (atotal of 2 * number of sub goals + number of sub goals wherethe first action is executed by an agent + number of sub goalsnot assigned to any particular agent + 2 * number of actionsperformed by the user) without being explicitly specified inthe scenario. For example, without the PC2BDI agent, ascenario (used in the experiment) with 6 goals where 1 goalis not assigned to any particular agent (by default, the useris supposed to achieve this goal), and 10 user operations, re-quires 33 communicative or pedagogical actions to be addedmanually by the domain expert or by the tutor [35]. Thus,the PC2BDI agent architecture simplifies the design of sce-nario by providing communicative and pedagogical actionsbased on the current state of the activity and by taking intoaccount the current mental state of team members.

Currently we have performed experiments with 16 engi-neering students. Although, the initial results and feedbacksare positive, it would be interesting to conduct evaluationswith more participants in order to improve the precision ofresults. However, there are many improvements possible inthe current system. For example, the pedagogical skills ofthe PC2BDI agent remain fairly limited. This means for ex-ample, the agent can not overcome the negative actions of

users. In other words, the consequences of actions performedby the user can not be restored by the agent. Furthermore,unlike STEVE agents [33], PC2BDI agent does not have thecapability of demonstrating the actions by doing it and thenrestoring all the effects back to the earlier state in the VE,so that the learners can perform the action to achieve theirpedagogical objectives. In addition, the current implemen-tation of the architecture does not support the replanningof the task scenario dynamically in case when the scenariocannot be continued because of the failure of certain actions.

A way to improve the the pedagogical agents is to adapttheir behavior according to the performance of the learner.With this in mind, it is possible to integrate the implemen-tation of the cognitive reliability and error analysis method(CREAM) [17] as well as the one in [41], so that agentsprovide more dynamic pedagogical behaviors.

Concerning the turn-taking capabilities of the agent, theparticipants mentioned that the movement of the head dur-ing the conversion was not realistic. The reason is that theturn-taking model in PC2BDI architecture is not continu-ous and therefore depends on the response threshold valueof each agent. It will be interesting to integrate more dy-namic and continuous turn-taking behavior, such as thatpresented in [18], to provide a more natural interaction.

Another important remark is concerned with the limitednatural language capabilities and the limited vocabulary forthe agent. As we have previously specified, the natural lan-guage processing capacity of the PC2BDI agent depends onthe richness of the semantic description of the model. How-ever, it would be interesting to integrate the modelling ap-proach based on the data with the semantic modelling basedapproach. For example, the statistical approach based on di-alogue corpus proposed in [1] can be used to find the mostfrequent dialogue patterns. These patterns can then be usedto construct dialogue models in combination with the se-mantic modelling of VE [7] to provide more flexible andadaptable dialogue management capabilities.

6. CONCLUSIONThe Behavioral architecture PC2BDI proposed in this ar-

ticle provides to the agents the ability to coordinate their ac-tivities using communication in natural language. We thenpresented an experimental study to evaluate the impact ofreactive/proactive pedagogical behavior of the agent on thelearner in the context of the learning of a collective activ-ity in a virtual environment involving three team members(1 human and 2 agents). The results show that the useof proactive pedagogical agents improves engagement andlearning for the user. Proactive pedagogical agents have thepower to convey information more accessible to other teammembers. The evaluation results indicate the significantdifference between the two experimental sequences whichincludes sequence 1 (reactive pedagogical followed by proac-tive pedagogical) and sequence 2 (proactive pedagogical fol-lowed by reactive pedagogical) enable to perceive the use-fulness of the proactive pedagogical agents. These resultsconfirmed the initial anticipation that proactive pedagogi-cal agents as equivalent team members and as tutors will beuseful for collaborative learning in the VE.

Acknowledgments. This work was supported by the NARECAproject (ANR-13-CORD-0015).

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