International Journal of Pedagogics
110
https://theusajournals.com/index.php/ijp
VOLUME
Vol.05 Issue05 2025
PAGE NO.
110-114
10.37547/ijp/Volume05Issue05-27
Models and Mechanisms for Implementing an Inclusive
Approach in Engineering Education Based on Artificial
Intelligence
Atajonova Saidakhon Boratalievna
Head of the Department of Information Technologies, Andijan State Technical Institute, Uzbekistan
Received:
17 March 2025;
Accepted:
13 April 2025;
Published:
15 May 2025
Abstract:
The article analyzes the models and mechanisms for implementing inclusive education in engineering
education using artificial intelligence (AI) technologies. Modern digital tools that facilitate the adaptation of the
educational process for students with different educational needs are analyzed. The principles of personalized
learning, controlled knowledge control and intelligent decision support systems are described. Recommendations
for the implementation of AI in the educational process with increased accessibility of the engineering educational
process are given.
Keywords:
Inclusive approach, engineering education, artificial intelligence, equal opportunities, educational
technologies, adaptive learning, accessibility.
Introduction:
Modern trends in the development of
higher education require the active implementation of
innovative technologies to ensure the accessibility and
quality of education. In particular, inclusive education
in engineering specialties requires the creation of
adaptive mechanisms that take into account the
individual needs of students, including people with
disabilities. Artificial intelligence (AI) opens up new
prospects in this area, providing personalized learning,
intellectual support for teachers and students, as well
as automated knowledge control [1]. The use of AI in
engineering education can not only increase the
accessibility of educational programs, but also improve
the quality of training specialists, adapting the content
of courses to the level of knowledge and needs of each
student. This article is devoted to the study of models
and mechanisms for implementing an inclusive
approach in engineering education using AI, as well as
an analysis of the prospects for their implementation.
The development of artificial intelligence (AI)
technologies allows us to form new approaches to
inclusive engineering education, ensuring the
accessibility of education for students with different
educational needs. Let us consider the main models
and mechanisms that contribute to the effective
implementation of inclusion in engineering education
using AI [2].
METHODS
The educational policy of foreign countries has
contributed to the formation of various approaches to
the education and upbringing of children with
disabilities. The main models include segregation,
mainstreaming, integration and inclusion. Models
based on data analysis and AI can adapt educational
content and tasks to the individual needs of students,
taking into account their abilities and the pace of
learning. Using digital technologies and online
platforms allows you to create accessible educational
materials that can be adapted for students with
different needs, including people with disabilities [3,4].
Models of inclusive education are successfully used in
various European countries, and their implementation
depends on many factors, including the socio-economic
conditions and educational traditions of each country.
In the UK, researchers R. Bond and E. Castagner
emphasize that the successful integration of children
with disabilities into the general education system
requires the use of specialized technologies and
support from teachers. They note that not only tutoring
support from teachers plays an important role, but also
the active participation of peers without disabilities.
This approach is called "class-wide peer tutoring"
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International Journal of Pedagogics (ISSN: 2771-2281)
(CWPT) or "cross-age tutoring" [5].
In South Korea, various strategies for inclusive
education have been developed based on American
research. Their effectiveness depends on a number of
conditions:
• assignment of a specially trained teacher to
support
children with disabilities during the learning process;
• creation of an individual educational space, where
children move from one teacher to another when
studying various disciplines;
• group learning, within the framework of which the
teacher develops a unique work strategy for each
group of students, taking into account their
developmental characteristics;
• organization of classes in small groups, which ensures
more intensive attention from the teacher to each
student;
• rotation of teachers,
when specialists working
individually are periodically replaced by other teachers.
In Sweden, Greece and France, inclusive education is
based on temporary pedagogical interaction. These
countries provide for the participation of highly
qualified specialists who work outside educational
institutions and provide support to teachers interacting
with
children
with
disabilities
in
preschool
organizations [7,8]. We analyzed the mechanisms and
models for the implementation of inclusive education
in technical universities (Table 1):
Т
ABLE 1
MODELS
KEY ASPECTS
EXAMPLES
Adaptive Learning Model -
Using
AI
allows
for
personalization
of
the
educational
process,
adjusting
materials
and
teaching methods to the
individual characteristics of
students
Analyzing
students'
knowledge levels and their
learning needs using intelligent
systems.
Dynamically changing the
difficulty of tasks based on the
student's progress.
Using machine learning
systems
to
predict
the
difficulties a student may
encounter.
AI-based platforms, such as
Smart Learning Systems, that
tailor the course to the student’s
level of knowledge.
Recommender systems that
help the student choose the
most appropriate format for
studying the material (video,
text, interactive tasks).
Model of intelligent
support for students -
AI can
act as a personal assistant
that
accompanies
the
student in the learning
process, helping him to
overcome
educational
barriers
Implementation of chatbots
and voice assistants to answer
students' questions.
Use of intelligent help
systems
integrated
into
educational platforms.
Support for students with
disabilities
through
voice
interfaces
and
machine
translation systems.
Virtual assistants, such as
IBM
Watson
Tutor,
that
analyze students’ questions and
offer
personalized
explanations.
Programs that automatically
translate text into audio or
support sign language for
students
with
hearing
impairments
The
model
of
automated
knowledge
control - AI can significantly
improve the objectivity and
accessibility of the process of
assessing
students'
knowledge,
reducing
the
influence of subjective factors
Automatic
checking
of
assignments using machine
learning algorithms.
Intelligent
systems
for
assessing answers that take into
account
not
only
the
correctness, but also the logic
of the student's reasoning.
Online proctoring with AI
analysis of student behavior to
ensure the fairness of exams
Automated testing systems
such as AI-Graded Exams that
grade
students'
answers
without
the
teacher's
involvement.
Analysis of written and
spoken answers using NLP
(Natural Language Processing)
to identify knowledge gaps
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International Journal of Pedagogics (ISSN: 2771-2281)
RESULTS
In the course of our research, we studied in detail the
mechanisms for implementing inclusive education in
technical universities, analyzing the best global
practices and modern technologies that contribute to
the creation of an accessible educational environment.
An analysis of existing approaches to teaching students
with special educational needs was conducted, digital
tools and methods that ensure their successful
integration
into
engineering
education
were
considered. Based on the data obtained, we developed
a model for implementing the inclusive potential in
technical education based on the use of artificial
intelligence technologies, adaptive learning and
intelligent educational platforms. This model includes
mechanisms for personalizing the educational process,
automated knowledge control, virtual mentoring and
the use of AR / VR technologies to increase the
accessibility of engineering disciplines. The proposed
model is aimed at creating equal opportunities for all
students, regardless of their physical, cognitive or
sensory characteristics, and can significantly increase
the effectiveness of inclusive education in technical
universities (Fig. 1).
Figure 1. Model for the implementation of inclusive potential in technical education
The developed model of inclusive education is aimed at
creating an adaptive educational environment in
technical universities that ensures the availability of
engineering education for students with special
educational needs (SEN). The model is based on
artificial intelligence (AI) technologies, automated
systems for adapting the educational process, digital
student support tools, and innovative knowledge
assessment methods.
1. Model structure
The model includes three key components:
-
Adaptive educational environment - using AI to
personalize learning, adjust materials and formats for
presenting information
-
Digital support tools - integration of voice
assistants, automated tutors, and VR/AR technologies
for students with disabilities.
-
Intelligent
assessment
and
feedback
mechanisms
-
automated
testing
systems,
performance analytics, and forecasting student needs.
2. Key implementation mechanisms
2.1. Adaptive learning using AI
-
AI algorithms analyze the student's knowledge
level and offer personalized learning paths.
-
Functionality:
• Automatic adjustment of the complexity of materials
and tasks.
• Selection of an individual learning pace.
• Use of multimodal content (
audio, video, text,
interactive models).
2.2. Intelligent educational assistants
-
AI bots and voice assistants help students with
disabilities adapt to the educational environment and
receive the necessary support.
-
Functionality:
• Answers to questions
on the curriculum.
• Automated recommendations of educational
materials.
• Text voicing, conversion of lectures into audio format.
2.3. Virtual and augmented laboratories (VR/AR)
The creation of digital labs allows students with
disabilities to participate in hands-on learning without
being physically present in the lab.
-
Functionality:
•
Conducting engineering experiments in a
virtual environment.
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International Journal of Pedagogics (ISSN: 2771-2281)
•
Visualization of complex technical processes.
•
Interactive
simulations
of
engineering
problems.
2.4. Automated knowledge control and online
proctoring
AI systems analyze students' answers and evaluate
them not only for correctness, but also for the logic of
their reasoning.
-
Functionality:
•
Automatic checking of written and oral
answers.
•
Analysis of academic performance and
identification of knowledge gaps.
•
Online proctoring with AI control during exams.
2.5. Digital platforms for inclusive learning
Educational platforms combine adaptive learning, AI
assistant support and virtual labs into a single
ecosystem.
-
Functionality:
•
Flexible curriculum.
•
Availability of educational materials in various
formats.
•
Integration with AR/VR, automated tutors and
analytical systems.
3. Advantages of the model
- Personalization of learning
–
each student receives an
individual development trajectory.
- Accessibility of education
–
learning becomes
inclusive, eliminating physical and cognitive barriers.
- Automation and support
–
digital tools provide
continuous assistance to students with disabilities.
- Interactivity and engagement
–
the use of VR/AR,
simulators and chatbots makes learning interesting.
- Objectivity of assessment
–
AI reduces the influence
of the human factor in knowledge testing.
The developed model of inclusive technical education
based on AI technologies allows creating an accessible,
personalized and effective educational environment
for all students, including those with special
educational needs. The integration of adaptive
platforms, digital assistants, virtual laboratories and
intelligent knowledge assessment systems helps to
remove barriers and improve the quality of engineering
education in technical universities. It includes the
following
main
components:
analysis
and
personalization through AI, adaptive materials, digital
assistants, VR/AR laboratories, automated knowledge
control and a feedback system. All these elements are
combined to create an effective inclusive educational
environment. The conclusion summarizes the results of
the study, noting that the introduction of inclusive
models in engineering education using AI technologies
is a promising direction that helps improve the
accessibility and quality of education.
CONCLUSION
In this research shows that further research and
development in this area can lead to the creation of
innovative solutions that will make engineering
education more inclusive and adaptive. The use of
artificial intelligence technologies in engineering
education opens up new opportunities for increasing
its inclusiveness. The developed models of adaptive
learning, intelligent support and automated knowledge
control allow taking into account the individual needs
of students and creating equal conditions for everyone.
The introduction of intelligent educational platforms,
VR/AR technologies and automated mechanisms for
adapting educational materials contributes to the
formation of an accessible educational environment,
which is especially important in the training of future
engineering personnel. For the further development of
inclusive engineering education, additional research
and pilot projects are needed to test and improve the
proposed models and mechanisms.
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