International Journal of Engineering Insights: (2025) Vol. 3, Nro.1, Regular Paper

https://doi.org/10.61961/injei.v3i1.29

Augmented Reality Systems for Enhancing Robotic

Manipulator Learning

Cristian P. Chuchico · Diego Bustillos

Received: 05 Feb 2025 / Accepted:16 May 2025 / Published: 15 Nov 2025

Abstract: Augmented Reality (AR) has emerged as

a transformative technology in education by integrat-

ing digital information into physical environments. This

study presented the development of an AR platform de-

signed to teach robotic manipulator concepts to engi-

neering students. The system provided interactive 3D

visualizations of joint conﬁgurations, degrees of free-

dom (DoF), kinematic models, and safety workspaces.

A quasi-experimental study with a pretest–posttest de-

sign (n = 60) was conducted at Universidad de las

Fuerzas Armadas ESPE. The results showed a signif-

icant improvement in student performance, with an av-

erage increase of approximately 40% in test scores. A

paired t-test conﬁrmed that the diﬀerence between pre-

test and post-test results was statistically signiﬁcant

(p < 0.05). Additionally, user perception results indi-

cated high acceptance levels, with an overall satisfac-

tion rate of 88%. These ﬁndings suggest that AR-based

learning environments can eﬀectively enhance the un-

derstanding of complex robotic concepts in engineering

education.

Keywords Augmented Reality · Robotics Education ·

Interactive Learning Environments · Industry 4.0 ·

Kinematics Visualization

1 Introduction

Augmented Reality (AR) has emerged as a transforma-

tive technology that integrates digital information into

physical environments through multimodal interaction.

Unlike Virtual Reality (VR), which creates fully syn-

Cristian P. Chuchico

Escuela de Doctorado, Programa de Doctorado en Ingenier´ıa

Electr´onica, Universidad de Zaragoza, Zaragoza, Espa˜na.

E-mail: 980122@unizar.es

Diego Bustillos

Universidad de las Fuerzas Armadas - ESPE, Latacunga,

Ecuador.

E-mail: dibustillos@espe.edu.ec

thetic environments [1], AR enhances real-world per-

ception by overlaying interactive elements such as vi-

sual, auditory, and haptic feedback, while preserving

the user’s contextual immersion [2,3]. Over the past

decade, AR has expanded beyond entertainment and

has been increasingly adopted in strategic domains in-

cluding Industry 4.0, education, smart manufacturing,

healthcare, and engineering training. [4,5,6,7,8,9]

In educational contexts, AR has demonstrated sig-

niﬁcant potential to improve the understanding of com-

plex and abstract concepts by enabling interactive and

spatial visualization [10,11]. By transforming abstract

models such as molecular structures or kinematic sys-

tems into manipulable representations, AR reduces cog-

nitive load and optimizes the transfer of knowledge [12,

13]. In engineering education, particularly in robotics,

the comprehension of concepts such as kinematics, de-

grees of freedom (DoF), and safety workspaces often

represents a challenge due to their abstract nature. AR-

based systems address this by providing intuitive and

immersive representations that facilitate cognitive pro-

cessing and knowledge retention [14,15].

However, despite the growing adoption of AR in ed-

ucation, there remains a lack of integrated platforms

speciﬁcally designed for teaching industrial robotic ma-

nipulators that combine kinematic visualization, safety

analysis, and interactive exploration within a uniﬁed

environment [16,17]. Additionally, limited empirical ev-

idence exists regarding the quantitative impact of such

systems on student learning outcomes in real laboratory

settings.

To address this gap, this study developed and im-

plemented an AR-based learning platform focused on

the Mitsubishi RV-2SDB robotic manipulator [18]. The

system, which utilizes a smartphone and a Bluetooth-

connected pointer, allows students to interact with vir-

tual representations of the robot’s structure, kinemat-

ics, and safety zones without requiring direct physical

manipulation. A quasi-experimental pretest–posttest de-

sign was employed to evaluate its eﬀectiveness in im-

proving student learning metrics.

7 International Journal of Engineering Insights, (2025) 3:1

The remainder of this paper is organized as follows:

Section 2 describes the system development and archi-

tecture. Section 3 presents the methodology and eval-

uation instruments. Section 4 discusses the statistical

results and ﬁndings. Finally, Section 5 presents the con-

clusions and future work.

2 Project Development

The development of the proposed Augmented Reality

(AR) system was carried out using the Scrum method-

ology as an agile framework to support iterative design,

continuous testing, and incremental system integration

[19,20]. This approach facilitated eﬃcient coordination

of development tasks and ensured adaptability to evolv-

ing technical requirements.

During each development cycle, the core modules of

the AR platform were implemented and validated, in-

cluding the integration of AR visualization, user inter-

action mechanisms, and mobile interface components.

This process enabled the progressive construction of a

functional and scalable system.

2.1 AR System Implementation

The AR application was developed using Unity [21] as

the primary environment for creating interactive 3D ex-

periences, combined with Vuforia for image recognition

and tracking. This conﬁguration enabled the detection

of visual markers and the real-time visualization of vir-

tual content associated with the Mitsubishi RV-2SDB

robotic manipulator.

The system allows users to access key technical in-

formation, including robot structure, kinematic behav-

ior, and safety zones, through an interactive AR in-

terface. This approach enhances the understanding of

complex robotic concepts by providing intuitive and im-

mersive visualization.

The 3D model of the Mitsubishi RV-2SDB manip-

ulator was implemented as the central element of the

application, as shown in Fig. 1. The initial interface of

the system includes a presentation screen and a struc-

tured navigation menu, which allows users to access dif-

ferent functional modules, as illustrated in Fig. 2. The

main interaction menu is presented in Fig. 3, enabling

intuitive navigation between system functionalities.

2.2 Functional Modules

The AR platform was structured into multiple func-

tional modules designed to support the learning of key

robotics concepts:

Fig. 1 Modeling of the Mitsubishi RV-2SDB Robot

Fig. 2 Application Presentation Interface

2.2.1 Speciﬁcations

This module provides access to the main technical pa-

rameters of the robotic manipulator, including degrees

of freedom, operating range, motion characteristics, and

physical constraints. The information is presented in a

structured format to facilitate rapid understanding of

the system’s capabilities, as shown in Fig. 4.

2.2.2 Links and Joints

This module enables the visualization of the manipula-

tor’s kinematic chain through AR-based overlays. The

system uses visual markers to trigger the display of vir-

tual information associated with each link and joint,

8 International Journal of Engineering Insights, (2025) 3:1

Fig. 3 Main Interaction Menu

Fig. 4 Technical Speciﬁcations of the Mitsubishi RV-2SDB

Manipulator

Fig. 5 Links and Joints Visualization

allowing users to explore the robot structure in real

time. This functionality improves spatial understand-

ing without requiring direct physical interaction. The

visualization is presented in Fig. 5.

2.2.3 Forward Kinematics

The forward kinematics module presents the relation-

ship between joint parameters and the position of the

end-eﬀector using the Denavit–Hartenberg (D–H) for-

mulation. This approach enables the representation of

the manipulator through homogeneous transformation

matrices, facilitating the analysis of its kinematic be-

havior.

The D–H parameters used in the system are summa-

rized in Table 1, while the corresponding visualization

within the AR environment is shown in Fig. 6.

Table 1 Denavit-Hartenberg (D-H) Parameters for the Mit-

subishi RV-2SDB.

Link (i) θ

(mm) a

(mm) α

(rad)

1 θ

300 0 −π/2

2 θ

− π/2 0 250 0

3 θ

0 160 0

2.2.4 Safety Zone

This module illustrates the operational workspace of

the robotic manipulator, including safe and restricted

9 International Journal of Engineering Insights, (2025) 3:1

Fig. 6 Forward Kinematics Representation using D-H Pa-

rameters

Fig. 7 Safety Zones of the Manipulator

areas. It allows users to identify motion limits and un-

derstand safety constraints within the working environ-

ment. This feature contributes to risk reduction and im-

proves awareness of safe operation practices. The safety

zones are visualized in Fig. 7.

3 Methodology

3.1 Study Design and Participants

This study employed a quasi-experimental design with

a single group using a pretest–posttest approach (n =

60). The participants were undergraduate students en-

rolled in the Electromechanical Technology program at

Universidad de las Fuerzas Armadas ESPE. A non-

probabilistic purposive sampling method was used to

ensure that all participants had a similar academic back-

ground in robotics and kinematics.

A control group was not included due to institu-

tional constraints, as all students were required to have

equal access to the AR-based intervention. Therefore,

each participant served as their own control, with pretest

scores used as a baseline for comparison.

3.2 Evaluation Instruments

Two instruments were used to assess the eﬀectiveness

of the proposed system:

Technical Knowledge Assessment: A structured

questionnaire consisting of 10 items was designed to

evaluate three key competencies: (i) identiﬁcation of

degrees of freedom and joint conﬁgurations, (ii) appli-

cation of Denavit–Hartenberg parameters, and (iii) un-

derstanding of safety zones. Each item was scored on a

scale of 0–2, resulting in a maximum score of 20 points.

To ensure comparability, both pretest and posttest

were designed with equivalent levels of diﬃculty and

aligned with the same learning objectives.

User Perception Survey: A Likert-scale survey

(1–5) was administered to evaluate usability, clarity of

information, and perceived learning eﬀectiveness.

Content validity of the instruments was established

through expert evaluation by three faculty members

specializing in robotics and automation.

3.3 Experimental Procedure

The experimental process was conducted in four stages:

1. Pretest: Participants completed an initial assess-

ment to determine baseline knowledge.

2. System Introduction: A brief orientation session

was conducted to familiarize participants with the

AR interface and interaction mechanisms.

3. Intervention: Participants engaged in a 45-minute

hands-on session using the AR platform, interacting

with virtual representations of the robotic manipu-

lator.

4. Posttest and Survey: After the intervention, par-

ticipants completed the posttest and the perception

survey.

3.4 Data Analysis

To evaluate the eﬀectiveness of the intervention, a paired

t-test was conducted to compare pretest and posttest

scores. The level of statistical signiﬁcance was set at

α = 0.05.

10 International Journal of Engineering Insights, (2025) 3:1

Additionally, descriptive statistics, including mean

and standard deviation, were calculated to analyze per-

formance trends. The percentage improvement between

pretest and posttest scores was also computed to quan-

tify learning gains.

This analytical approach ensures that the observed

diﬀerences in performance are statistically validated and

not attributable to random variation.

4 Results and Discussion

This section presents the quantitative results obtained

from the experimental evaluation of the proposed Aug-

mented Reality (AR) system, focusing on both learning

performance and user perception.

4.1 Learning Performance Analysis

The results of the technical knowledge assessment are

summarized in Table 2. A clear improvement can be

observed between pretest and posttest scores across all

participants.

The mean score increased from 3.20 to 4.50, repre-

senting an improvement of approximately 40.6%. Ad-

ditionally, the standard deviation decreased from 0.65

to 0.38, indicating a more consistent performance after

the intervention.

Table 2 Consolidated results of the technical knowledge as-

sessment (n = 60).

Metric Pretest Posttest Improvement

Mean Score

(out of 5)

3.20 4.50 40.6%

Standard Devi-

ation

0.65 0.38 -

Performance

(%)

64% 90% +26%

To determine whether the observed improvement

was statistically signiﬁcant, a paired t-test was con-

ducted comparing pretest and posttest scores. The re-

sults indicated a statistically signiﬁcant diﬀerence be-

tween both conditions (p < 0.05), conﬁrming that the

observed improvement is unlikely to be due to random

variation.

Furthermore, the fact that all participants showed

higher posttest scores suggests a consistent positive ef-

fect of the intervention across the entire sample, rein-

forcing the robustness of the results.

4.2 User Perception Analysis

The results of the user perception survey are summa-

rized in Table 3. The evaluated dimensions, including

usability, clarity of information, pedagogical value, and

interactivity, show high levels of acceptance among par-

ticipants.

Table 3 Consolidated User Perception Survey (Likert Scale:

1–5).

Dimension Mean Acceptance (%)

Application Design 4.65 93%

Ease of Use (Usability) 4.70 94%

Information Clarity 4.58 91.6%

Pedagogical Value 4.75 95%

Interactivity 4.40 88%

4.3 Discussion

The observed improvement in learning outcomes can

be attributed to the immersive and interactive nature of

the AR platform. By enabling real-time visualization of

abstract concepts such as kinematics and safety zones,

the system facilitates cognitive processing and enhances

conceptual understanding.

The reduction in score variability after the inter-

vention suggests that the platform not only improves

average performance but also contributes to more uni-

form learning across participants. Additionally, the high

levels of user acceptance indicate that the system is

both usable and engaging, which are critical factors

for successful integration of emerging technologies in

educational environments. Overall, the combination of

statistically signiﬁcant learning gains and positive user

perception supports the eﬀectiveness of AR as a tool

for enhancing robotics education.

5 Conclusions

This study presented the development and evaluation

of an Augmented Reality (AR)-based learning platform

for teaching robotic manipulator concepts. The system

integrates interactive visualization of kinematic struc-

tures, degrees of freedom, and safety zones, providing

an intuitive and immersive learning environment.

The experimental results demonstrated a signiﬁcant

improvement in student performance, with an average

increase of approximately 40% between pretest and post-

test scores. Statistical analysis using a paired t-test

conﬁrmed that this improvement was signiﬁcant (p <

11 International Journal of Engineering Insights, (2025) 3:1

0.05), indicating that the observed learning gains are

not attributable to random variation.

In addition to the quantitative results, user per-

ception analysis revealed high levels of acceptance in

terms of usability, clarity of information, and pedagogi-

cal value. These ﬁndings suggest that the proposed AR

platform eﬀectively supports the understanding of com-

plex robotic concepts and enhances student engagement

in laboratory environments.

Overall, this work highlights the potential of AR

technologies as eﬀective tools for engineering education,

particularly in domains that require spatial reasoning

and interaction with complex systems.

Future work will focus on extending the platform

to multi-robot environments, integrating advanced AR

devices such as head-mounted displays, and incorpo-

rating additional features such as haptic feedback and

remote collaborative capabilities to further enhance the

learning experience.

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