310
FACULTY, STAFF AND STUDENTS' PERSPECTIVES ON THE APPLICATION
OF ARTIFICIAL INTELLIGENCE IN VOCATIONAL EDUCATION: AN
EMPIRICAL STUDY AT NAM SAI GON POLYTECHNIC COLLEGE
Bui Thi Mai Chau, MA.
Vice Principal, NamSaiGon Polytechnic College
Le Van Khanh, MA.
Deputy Manager of Science – Technology and Cooperation Deparment
Le Dinh Dat
Specialist, Department of Science Technology and Cooperation
Email: dinhdat@namsaigon.edu.vn ; Phone: 0347771234
ABSTRACT
This study surveys the perspectives of 250 participants (30 faculty members, 20 staff
members, and 200 students) at Nam Sai Gon Polytechnic College regarding the application
of artificial intelligence (AI) in vocational education. The results show that 76% of students
strongly support the use of AI, while only 50% of faculty and 60% of staff have positive
attitudes. The study identifies key factors influencing perspectives including: level of
technological understanding, concerns about data security, and perception of AI benefits.
Based on these findings, the research proposes solutions to promote effective AI application
in the vocational education environment.
Keywords: Artificial intelligence, vocational education, perspectives, faculty, students,
Nam Sai Gon Polytechnic College
1. INTRODUCTION
The Fourth Industrial Revolution is profoundly transforming all aspects of society, with
vocational education facing unprecedented opportunities and challenges. Artificial
intelligence (AI), with its ability to learn, analyze and make decisions like humans, is
gradually becoming an indispensable tool in enhancing training quality and meeting the
increasingly high demands of the labor market. However, successful application of AI in
education depends not only on technology but also on the attitudes, perceptions and
readiness of people - those who directly use and are impacted by this technology.
In the context of Vietnamese vocational education, where practical skills training accounts
for 60-70% of learning time, the question of how to effectively integrate AI becomes
increasingly urgent. Can AI support the cultivation of manual skills - the core element of
vocational education? Do faculty members, the craft instructors, view AI as a supporting
tool or a threat? What do students, the future workforce generation, expect from AI in their
learning process? And how ready are administrative staff, who ensure the smooth operation
of the system, for this change?
Nam Sai Gon Polytechnic College, one of the prestigious vocational training institutions in
the Southern region, stands at an important crossroads in its digital transformation journey.
With diverse training fields from mechanics and electronics to information technology, the
college faces the challenge of how to make AI effectively serve each different professional
311
field. This requires not only investment in technology but more importantly, consensus and
preparation from the entire college community.
From this reality, the research is conducted with the objective of comprehensively
evaluating the perspectives of three key groups in the vocational education system: faculty -
those who directly teach and impart skills; staff - those who manage and support the training
process; and students - those who directly benefit from AI technology. The differences in
roles, experiences and expectations of each group will create a multidimensional picture of
the current state and potential of AI application in vocational education.
The research seeks answers to three core questions:
First, what are the current perspectives of faculty, staff and students on using AI in
teaching, learning and working activities at the college? This is the foundational question to
help understand the current state of awareness and attitudes of stakeholders.
Second, what factors are influencing their perspectives? Identifying impact factors will help
managers develop appropriate intervention strategies to promote effective AI acceptance
and use.
Third, are there significant differences between the perspectives of the three groups?
Understanding these differences will help build AI deployment strategies suitable for each
group, avoiding a "one size fits all" approach.
With a sample size of 250 participants, including 30 faculty members, 20 staff members and
200 students from both intermediate and college levels, the research expects to provide a
comprehensive and representative view of the Nam Sai Gon Polytechnic College
community. The research results are significant not only for the college but also contribute
to the knowledge base on AI application in Vietnamese vocational education - a field that
has been little studied systematically.
Theoretically, the research contributes to testing and expanding technology acceptance
models in the specific context of Vietnamese vocational education. Practically, the research
results provide a scientific basis for developing appropriate AI application strategies and
policies, not only for Nam Sai Gon Polytechnic College but also for other vocational
education institutions nationwide.
The article is structured as follows: Section 2 presents the theoretical overview of AI in
vocational education and related research. Section 3 describes the research methodology.
Section 4 presents the survey results. Section 5 discusses the significance of the findings.
Finally, Section 6 provides conclusions and recommendations for stakeholders.
2. THEORETICAL OVERVIEW
2.1. Concepts and development of AI in education
2.1.1. Definition of AI in Education (AIED)
Artificial Intelligence in Education (AIED) is the application of AI techniques to support,
enhance and automate teaching and learning activities. According to Zawacki-Richter et al.
(2019), AIED includes four main application areas: (1) profiling and prediction, (2)
assessment and testing, (3) adaptive systems and personalization, and (4) intelligent tutoring
systems.
2.1.2. Development of AIED
312
Research by Zhai et al. (2021) analyzing 109 papers from 2010-2020 shows that AIED has
developed through three stages:
Stage 1 (2010-2015): Focus on Intelligent Tutoring Systems
Stage 2 (2015-2019): Development of machine learning applications and learning
analytics
Stage 3 (2019-present): Explosion with generative AI and large language models
2.2. AI in Technical and Vocational Education and Training (TVET)
2.2.1. Characteristics of vocational education
Technical and Vocational Education and Training (TVET) has distinctive features:
Practice-oriented: 60-70% of learning time focuses on practical skills training
Industry linkage: Training programs need to reflect actual labor market demands
Diverse levels: From intermediate to college with various professions
2.2.2. Opportunities and challenges of AI in TVET
According to research by Windelband and Spöttl (2012), later updated by Faßhauer and
Windelband (2021), AI in TVET poses central challenges:
Working and learning with virtual systems (simulations, process visualization, VR
applications)
Working with smart factories and AI-enabled processes (expert systems, diagnostic
systems)
Hybrid task management and process structure organization (hybrid tasks, mixed
occupations)
Data handling (data compilation, analysis and transfer, data security)
2.3. Common AI applications in vocational education
2.3.1. Intelligent Tutoring Systems (ITS)
ITS uses AI to provide personalized guidance to students. In TVET, ITS can:
Simulate real work environments for student practice
Provide instant feedback on techniques and procedures
Adjust exercise difficulty based on learner progress
2.3.2. Chatbots and virtual assistants
Research shows AI chatbots in education can:
Support students 24/7 with lesson and technical questions
Guide practice procedures step by step
Provide career counseling and learning orientation
2.3.3. Intelligent assessment systems
Santosa et al. (2023) developed a Virtual Concrete Testing Machine using Multilayer
Perceptron for virtual laboratories in vocational education, showing AI can:
Automatically grade practical assignments
313
Assess skills through video analysis
Predict learning outcomes and provide early warnings
2.3.4. Learning Analytics
AI analyzes data from the learning process to:
Identify strengths/weaknesses of each student
Predict dropout risk and intervene timely
Optimize training programs based on actual data
2.4. Foundational theories
2.4.1. Technology Acceptance Model (TAM)
Davis (1989) developed the TAM model with two main factors:
Perceived Usefulness (PU): The degree to which users believe technology will
improve work performance
Perceived Ease of Use (PEOU): The degree to which users believe using technology
requires little effort
This model is extended in the AI context with additional factors such as trust in AI, privacy
concerns, and organizational support.
2.4.2. Diffusion of Innovation Theory (DOI)
Rogers (2003) proposed the innovation adoption process with 5 stages: knowledge,
persuasion, decision, implementation, and confirmation. In the context of AI in TVET, these
stages reflect:
Knowledge: Understanding AI capabilities
Persuasion: Evaluating benefits for teaching/learning
Decision: Accepting or rejecting use
Implementation: Integrating into daily activities
Confirmation: Evaluating effectiveness and adjusting
2.5. Research on AI in education in Vietnam
2.5.1. Vietnamese context
Vietnam has over 242 higher education institutions with over 2 million students (as of
2022). In the TVET field, the vocational education system is undergoing strong digital
transformation.
2.5.2. Notable research
Nguyen Thanh Thuy et al. (2018) emphasize AI is developing rapidly in Vietnam,
requiring proper understanding to seize opportunities and challenges
Ho Dac Loc and Huynh Chau Duy (2020) propose Industry 4.0 transformation
solutions for Vietnamese vocational education
Dinh Thi My Hanh and Tran Van Hung (2021) analyze opportunities and
challenges of AI in higher education, many points applicable to TVET
314
Bui Trong Tai and Nguyen Minh Tuan (2024) research AI's impact on student
learning activities, identifying both positive and negative effects
2.5.3. Policies and directions
Vietnam has issued the National Strategy for AI research, development and application until
2030, with education as a priority area. For vocational education, the Ministry of Labour -
Invalids and Social Affairs is promoting digitalization and technology application in
vocational training.
2.6. Research gaps
Despite numerous studies on AI in education, significant gaps remain:
1. Lack of empirical research on stakeholder perspectives in Vietnamese TVET
2. No comparative studies of perspectives between faculty, staff and students within
the same institution
3. Lack of specific guidance for AI implementation suitable for Vietnamese vocational
education characteristics
This research aims to fill these gaps by comprehensively surveying perspectives of three
main groups at a typical TVET institution.
3. RESEARCH METHODOLOGY
3.1. Research design
The study uses a mixed-method approach combining quantitative research through survey
questionnaires and qualitative research through in-depth interviews.
3.2. Research sample
A total of 250 participants from Nam Sai Gon Polytechnic College:
30 faculty members (12%)
20 administrative staff (8%)
200 students (80%) from intermediate and college programs
The sample was selected using convenience sampling combined with stratification to ensure
representation of different departments in the college.
3.3. Data collection instruments
The survey questionnaire was designed based on a 5-point Likert scale, including 4 main
sections:
1. Demographic information
2. Level of AI understanding (10 questions)
3. Perspectives on AI application (15 questions)
4. Influencing factors (12 questions)
Scale reliability was tested with Cronbach's Alpha = 0.85.
3.4. Analysis methods