Advanced Journal of STEM Education, Vol. 3 No. 1 (2025)

https://doi.org/10.31098/ajosed.v3i1.3302
Research Paper

Teachers’ Awareness in Artificial Intelligence and Digital
Competence in the Workplace
Karen A. Manaig*1

, Alberto D. Yazon1, Chester Alexus C. Buama1

, Ruel T. Bonganciso2

1 Laguna State Polytechnic University, Philippines
2 Blenheim Middle School, South Carolina, United States of America

Received : February 15, 2025

Revised : May 20, 2025

Accepted : May 28, 2025

Online : May 31, 2025

Abstract
This study examines the impact of artificial intelligence (AI) on education, specifically in the areas of teaching,
learning, and assessment. While AI offers benefits such as personalized learning, automation, and data-driven
feedback, it also brings challenges such as data privacy, teacher adaptation, and equity concerns. This research
examines the link between teachers’ awareness of AI and their digital competence in the workplace. Using a
descriptive correlational design, data were collected via an online survey from 144 public basic education
teachers in Laguna, Philippines. The study employed validated tools: the AI Awareness Scale and the Digital
Competence Questionnaire. Results showed that only one factor—attendance at AI or ICT-related training—
significantly influenced AI awareness (p = .044). Thus, the hypothesis was partially accepted, as the other
demographic attributes showed no significant differences. However, a notable finding is the rejection of the
hypothesis that no significant relationship exists between AI awareness and digital competence, suggesting a
meaningful connection between the two. This research provides new insights into a relatively unexplored area:
how teachers’ understanding of AI correlates with their ability to effectively use digital tools. Although AI’s role
in fields such as health care and technology is well studied, its educational impact, particularly on teachers’
preparedness, remains underrepresented.
Keywords: Teachers, Artificial Intelligence, Digital Competence, Awareness, Correlations

INTRODUCTION
Technology nearly permeates every aspect of modern life, transforming how we function
and interact. This shift is especially evident in education, where innovations like the Internet of
Things (IoT) have changed traditional learning methods. Simultaneously, artificial intelligence (AI)
is gaining significant relevance in educational settings, offering innovative opportunities to improve
teaching and learning experiences. Al-Darayseh (2023) highlighted the importance of AI in
education, noting its ability to personalize learning experiences to meet individual student needs,
offer detailed feedback for ongoing improvement, engage and motivate learners to achieve better
outcomes, and reduce teachers’ administrative workload, allowing for more effective time
management.
The adoption of AI tools to improve educational outcomes has accelerated in recent years,
as demonstrated by Holmes et al. (2019). The COVID-19 pandemic accelerated this integration, as
noted by Zawacki-Richter et al. (2019). With schools closing unexpectedly, AI-driven platforms and
tools were rapidly implemented to maintain educational continuity. This period also saw a surge in
user registrations for commercial AI education platforms, illustrating an increased dependence on
AI to support remote learning, as documented by Miao et al. (2021).
Although AI offers considerable promise for advancing education, several challenges must
be addressed. AI must be used equitably and without bias to avoid worsening existing inequalities
among student groups. Moreover, thorough training programs for educators are crucial to enable
teachers to successfully integrate AI into their classrooms. Although AI integration holds significant
Copyright Holder:
This Article is Licensed Under:
© Karen, Alberto, Chester & Ruel. (2025)
Corresponding author’s email: karen.manaig@lspu.edu.ph Corresponding author’s
Corresponding author’s email: xxx@xxx.com

Adv. J. STEM. Ed.

potential for enriching learning experiences, addressing issues such as bias reduction and providing
adequate teacher training are key to its successful implementation in education.
One of the significant challenges confronting researchers in the realm of information
systems and technology is the acceptance or rejection of modern technology by its users. The
incorporation of advanced technologies, including mobile devices, tablets, laptops, simulators, and
virtual laboratories, seeks to revolutionize the educational environment in schools and institutions.
In light of the globalized nature of education, there is a growing imperative to incorporate digital
technologies (Haleem et al., 2022). Consequently, the teaching profession is confronted with rapidly
evolving demands, necessitating the acquisition of a diverse and advanced set of competencies
(European Commission, 2023). The researchers of this study assert that digital competencies (DCs)
are essential in today’s educational system and serve as a linchpin for education's future.
Teachers across all levels must consistently enhance their digital skills and expertise to
remain aligned with the fast-evolving technological landscape and expanding knowledge base.
Digital competence (DC) is one of the eight key competencies necessary for effectively using a wide
range of digital information and communication technologies, as well as for solving problems in
various aspects of life. These competencies involve the skilled and responsible use of digital
technologies in areas such as work, learning, social engagement, and interpersonal communication
to achieve different goals (Caena & Redecker, 2019). Additionally, DCs include a broad
understanding of digital technologies and their implications within the digital landscape (Becker et
al., 2017).
Despite the growing body of literature on AI in education, a notable gap remains in
understanding the intersection between teachers’ digital competencies and their awareness or
preparedness to integrate AI into pedagogical practice, particularly in diverse educational settings
and across varying levels of technological infrastructure. Existing research has largely focused on
AI’s capabilities or its broad implementation, often overlooking the critical role of educators’ digital
literacy in mediating the effectiveness of these technologies.
To address this gap, this study investigated educators’ awareness of AI and evaluated their
digital competence. This dual focus is essential for informing strategic interventions aimed at
enhancing professional development, fostering inclusive education, and ensuring equitable access
to the benefits of AI-enhanced learning environments.
LITERATURE REVIEW
Background of The Study
The COVID-19 pandemic has significantly accelerated the adoption of online and blended
learning models, prompting educators to integrate emerging technologies to enhance student
outcomes. Among these technologies, artificial intelligence (AI) has gained considerable attention,
particularly in virtual learning environments where it has been leveraged to support and enrich
student learning experiences. Despite its transformative potential, many AI tools remain unfamiliar
to educators who often lack the technical expertise required to effectively incorporate AI
applications into their teaching practices or to foster students’ digital competencies in AI.
The expanding significance of AI in education is demonstrated by a survey of international
literature, which also highlights the advantages it offers to both instructors and students. AI
competencies are now recognized as essential technological skills for the 21st century, enabling
individuals to critically evaluate digital technologies, collaborate effectively, and apply AI tools in
various contexts, including the workplace and home. The European digital competence framework
for educators, specifically the DigCompEdu platform, offers a model for integrating AI into teaching
and learning. Studies have indicated that AI can streamline administrative tasks for teachers while
also empowering students to approach educational challenges with creativity and innovation.
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However, to fully harness AI’s potential, educators must enhance their pedagogical and
technological competencies to create effective learning environments that maximize the benefits of
AI (Tkhachenko, 2023).
Despite advancements in educational technology, a notable lack of research has identified
the specific AI competencies that educators need to effectively integrate AI into their teaching
practices. While general digital competency frameworks such as DigCompEdu and P21’s 21stcentury learning framework offer guidance, comprehensive studies addressing the unique
challenges of teaching with AI are scarce. This study aims to address this gap by examining the
opportunities and challenges of AI integration in education, with a focus on enhancing teaching,
learning, and assessment. By adapting existing digital competency models to include AI-specific
skills, this research seeks to provide practical recommendations for educators and researchers,
facilitating AI literacy and the successful incorporation of AI tools into educational settings.
Theoretical Framework
Incorporating Artificial Intelligence (AI) and digital technologies into education requires a
robust theoretical foundation to examine the connection between teachers’ AI awareness and their
digital competence. Various educational, psychological, and technological theories offer valuable
perspectives on how educators learn, utilize, and integrate digital tools and AI into their
professional routines.
The Technological Pedagogical Content Knowledge (TPACK) framework, introduced by
Koh et al. (2015), emphasizes the interplay between three essential domains: Content Knowledge
(CK), Pedagogical Knowledge (PK), and Technological Knowledge (TK). For teachers to effectively
incorporate AI tools into their instruction, a solid foundation in technological knowledge is
essential, as it significantly impacts their pedagogical approaches and content delivery. AI
awareness further empowers educators to utilize technology to enhance both teaching methods
and learning experiences, positioning TPACK as a vital framework for understanding AI’s
integration into education.
The Digital Competence Framework (Ferrari, 2013) underscores the significance of digital
literacy for educators, highlighting key competencies like information and data literacy, digital
content creation, communication, collaboration, and problem-solving. A teacher’s digital
competence is crucial for effectively using AI in the classroom, as it involves not only the technical
application of tools but also understanding their wider impact on learning, ethics, and the ability to
adapt to various educational contexts.
A comprehensive understanding of the connection between teachers’ digital competency
and their awareness of AI can be obtained by combining these notions. Higher levels of digital
competency are anticipated to be displayed by educators who are more knowledgeable about AI,
which will boost their self-assurance and proficiency with AI tools. The theoretical framework also
emphasizes the crucial role of peer support, training, and professional development in improving
teachers’ digital literacy and facilitating the effective incorporation of AI into teaching methods.
This study investigates the elements that influence teachers’ ability to integrate AI tools into their
instructional settings and how their awareness of AI affects their digital competency, all based on
this integrated framework.

Conceptual Framework
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Faculty Members’ Level of
Digital Competence in AI
• Information Processing
• Communication
• Content Creation
• Safety
• Problem Solving

Faculty members’ level of
awareness of AI
• Cognitive Awareness
• Performance
Awareness

Profile of Respondents
• Age
• Sex
• Educational attainment
• Length of service

Figure 1. Research Paradigm
Statement of the Problem
This study assesses the relationship between artificial intelligence awareness and the
digital competence of teachers in the workplace.
Specifically, it answers the following questions:
1. The profile of the respondents in terms of age, sex, educational attainment, length of
service, level taught, ICT-related training, and AI-related training.
2. What is the mean level of respondents’ artificial intelligence awareness?
3. The mean level of respondents’ digital competence
4. Is there a significant difference between the profile and artificial intelligence awareness of
groups?
5. Is there a significant relationship between teachers’ artificial awareness and digital
competence in the workplace?
RESEARCH METHOD
The study utilized a descriptive correlational research design, which is suitable for
describing and measuring the levels of teachers’ awareness of Artificial Intelligence (AI) and their
digital competence in the workplace, while also exploring potential relationships or correlations
between these variables. This approach allows researchers to identify patterns and associations,
offering valuable insights into how teachers’ AI knowledge may be linked to their digital skills in a
professional setting. The correlational nature of the design is particularly beneficial for identifying
potential connections between variables without manipulating them, making it ideal for examining
naturally occurring traits among teachers.
To ensure that the findings of this study can be applied more broadly and to avoid sampling
bias, a simple random sampling method was used. Public Basic Education Teachers from the
Province of Laguna, Philippines, were selected randomly, giving everyone in the target group an
equal chance to participate. Surveys were distributed online using Google Forms, making it easier
to reach various participants. In total, 144 teachers completed the survey. This number is
considered sufficient for studies of this kind, as it allows for meaningful analysis without becoming
too large to manage. It strikes a good balance between being large enough to reflect the views of
the population and small enough to manage with available resources. The sample size also meets

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common recommendations for educational research, thus supporting the credibility of the results.
To assess respondents' awareness of Artificial Intelligence, the AI Awareness Scale was
developed as it provides a validated tool specifically designed to measure AI awareness. This scale
effectively evaluates various aspects of AI knowledge and familiarity, which aligns with the focus of
the study. For evaluating Digital Competence, the Digital Competence Questionnaire was selected
due to its proven reliability and relevance in measuring digital proficiency. This tool effectively
captures the key competencies needed in contemporary educational settings, and it fits well with
the study’s goal of examining digital skills among teachers in their professional environment.
To analyze the demographic profile of the respondents, frequency and percentage
distributions were employed, as these are appropriate for summarizing categorical data and
identifying patterns in respondent characteristics. To determine the level of awareness and digital
competence, the mean and standard deviation were utilized, providing a measure of central
tendency and variability that helps interpret the overall trends and dispersion in the respondents’
responses. Furthermore, to examine the significant relationships between variables, both the chisquared test and Pearson’s product-moment correlation coefficient were applied. The chi-squared
test was suitable for evaluating associations between categorical variables, whereas Pearson’s r
was used to assess the strength and direction of linear relationships between continuous variables.
The selection of these statistical tools was based on the nature of the data and the specific objectives
of the study, ensuring that the analyses were valid and appropriate.
FINDINGS AND DISCUSSION
Table 1. Distribution of the respondents in terms of age
Age
Frequency
24-34 years old
81
35-45 years old
47
46-56 years old
13
57 years old and above
3
Total
144

Percentage
56.25
32.64
9.03
2.08
100.00

The age distribution of respondents is displayed in Table 1. More than half of the
respondents were comparatively young people, as the majority of them (56.25%) were between
the ages of 24 and 34. The second-largest group is the 35-45 years old category, comprising 32.64%
of the respondents. A smaller proportion of respondents were between 46 and 56 years old
(9.03%), and an even smaller segment was 57 years old and above (2.08%). This distribution
suggests that the sample is skewed toward younger age groups.
The sample’s age skew may limit the generalizability of the results to the entire population
of educators, particularly if older age groups are more prevalent in certain schools or regions.
Future studies could benefit from a more balanced age distribution to provide insights that more
accurately reflect the diversity within the teacher population.
Table 2. Distribution of the respondents in terms of sex
Sex
Frequency
Female
115
Male
29
Total
144

Percentage
79.9
20.1
100.00

In a study examining AI awareness among teachers, Table 2 shows that the majority of
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respondents were female, accounting for 79.9% of the sample, while male teachers represented
20.1%. This notable imbalance in gender distribution suggests that the findings and perspectives
from this study are largely reflective of female teachers.
The findings indicate that the AI awareness results mainly reflect the perspectives of female
teachers. This gender imbalance could affect the outcomes because potential differences in
technology adoption or attitudes toward AI based on gender may not be fully represented. Future
studies with a more balanced gender distribution could provide a more comprehensive
understanding of AI awareness among educators.
Table 3. Distribution of the respondents in terms of highest educational attainment
Highest Educational Attainment
Frequency
Percentage
Bachelor’s degree
4
2.8
Bachelor’s degree with MA units
23
16.0
Master's degree
109
75.7
Doctorate degree
6
4.2
Post Doctorate Degree
2
1.4
Total
144
100.0
Table 3 shows that most respondents hold a Master’s degree (75.7%), indicating a high
level of advanced education within the sample. A significant proportion of students have a
Bachelor’s degree with MA units (16.0%), indicating that many are pursuing further studies. A
smaller segment has a Doctorate degree (4.2%), and a very minimal percentage have a Bachelor’s
degree (2.8%) or a post-doctoral degree (1.4%).
This high level of educational attainment among respondents suggests that teachers are
well-qualified and likely to have a strong foundation in both theoretical and practical aspects of
education. Their advanced educational background might also influence their perceptions and
awareness of AI, potentially leading to more informed and insightful perspectives on the topic.
Table 4. Distribution of respondents in terms of service length
Length in Service
Frequency
Percentage
1-10 years
87
60.42
11-20 years
48
33.33
21-30 years
9
6.25
Total
144
100.00
As indicated in Table 4, the majority of respondents (60.42%) have been in service for 1–
10 years, suggesting that most are relatively early in their careers and may offer fresh perspectives
along with greater adaptability to new technologies like AI. The next largest group, comprising
33.33%, has been in service for 11 to 20 years, offering a balance between experience and openness
to innovation. A smaller group, 6.25%, has been in service for 21–30 years, providing in-depth
expertise and historical perspectives on the educational landscape.
This distribution of experience levels is important because it reflects a diverse range of
teachers who can offer different insights into AI awareness and its potential effects on teaching
practices. The combination of newer and more experienced educators can enrich the understanding
and adoption of AI in education.

Table 5. Distribution of the respondents in terms of grade level taught
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Grade Level
Grade 1-6
Grade 7-10
Grage 11-12
Kinder
Total

Frequency
50
62
25
7
144

Percentage
34.7
43.1
17.4
4.9
100.00

Table 5 presents the distribution of respondents based on the grade levels they taught. A
significant portion of the respondents (43.1%) teach grades 7-10, indicating strong representation
from middle and junior high school educators. Teachers of grades 1-6 make up 34.7% of the sample,
while 17.4% teach grades 11-12, reflecting a solid presence of elementary and high school teachers.
A smaller percentage of respondents (4.9%) taught kindergarten.
The wide range of grade levels taught by the respondents provides a thorough perspective
on AI awareness across different educational stages. It is particularly valuable as each grade level
faces distinct challenges in integrating AI into their teaching methods. This diverse distribution
ensures that the study captures a broad array of educational experiences and viewpoints on AI.
Table 6. Distribution of the respondents in terms of AI and ICT Training
Training
Frequency
Percentage
AI Training
3
2.1
ICT Training
119
82.6
Both (ICT and AI Training)
22
15.3
Total
144
100.00
Table 6 illustrates the distribution of teachers based on their training in AI and Information
and Communications Technology (ICT). A significant majority of respondents (82.6%) have
received ICT training, indicating that most teachers possess fundamental technological skills.
However, only a small proportion (2.1%) received specific training in AI. Approximately 15.3% of
respondents have been trained in both AI and ICT, indicating more thorough preparation for
modern technological skills.
This distribution indicates that while ICT training is widespread among teachers, AIspecific training remains less common. To improve AI awareness and its integration into teaching,
greater emphasis should be placed on providing AI training to educators, helping them effectively
utilize AI tools in the classroom and stay updated with technological advancements.
Table 7. Mean level of respondents’ artificial intelligence awareness in terms of cognitive
awareness of artificial intelligence in teaching
Descriptive
Indicative Statement
Mean
SD
Interpretation
As a faculty member…
1. I know enough about AI applications and
programs.

3.38

0.74

Moderate

2. I know enough about faculty members'
responsibilities regarding using AI in the
classroom.

3.38

0.82

Moderate

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Mean

SD

Descriptive
Interpretation

3. I am familiar with the fundamentals of creating
and executing lessons using AI tools and
programs.

3.19

0.93

Moderate

4. I am aware of the techniques used by AI
software to assess student work.

3.21

0.89

Moderate

3.29

0.74

Moderate

Indicative Statement
As a faculty member…

Overall

Table 7 presents the respondents’ cognitive awareness of artificial intelligence in teaching,
as assessed using various indicative statements. The overall mean score of 3.29 (SD = 0.74) was
classified as "Moderate." The mean scores for individual statements ranged from 3.19 to 3.38, all
falling under the "Moderate" category. This indicates that although teachers possess a basic
understanding of AI applications, their roles in teaching, lesson planning, and student assessment,
there is potential for further growth. The moderate level of cognitive awareness suggests that
teachers are somewhat familiar with AI in education but may require additional training and
support to fully incorporate AI tools and strategies into their teaching practices. Expanding AI
training programs and offering ongoing professional development could enhance students’
comprehension and use of AI in the classroom.
The findings align with Sysoyev (2023), who suggested that the use of AI tools in education
is still in its infancy. university professors frequently lack a thorough awareness of the possible
organizational, instructional, and learning uses of artificial intelligence. Many educators possess
only partial knowledge, which tends to focus narrowly on their specific teaching areas, limiting their
ability to fully grasp AI’s broader educational possibilities. Nonetheless, most faculty members have
a neutral or open attitude toward incorporating AI tools into their teaching. However, practical
experience with AI in education remains limited, with only isolated instances of specific
technologies being applied to particular aspects of discipline instruction.
Table 8. Mean level of respondents’ artificial intelligence awareness in terms of performance
awareness of artificial intelligence in teaching
Descriptive
Indicative Statement
Mean
SD
Interpretation
As a faculty member…
1. I can use AI applications to create various files.

3.11

0.95

Moderate

2. I can use AI tools to create course materials.

3.19

1.15

Moderate

3. I can use AI tools to summarize lengthy
documents.

3.38

1.00

Moderate

4. I can employ chatbots to answer questions from
learners.

3.22

1.05

Moderate

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Mean

SD

Descriptive
Interpretation

5. I can use AI (sound-making) software to turn
course textual materials into audio files.

2.85

1.03

Moderate

6. I will be able to use AI applications in the
classroom that focus on scientific rivalry,
challenges, and suspense.

2.91

0.97

Moderate

7. AI tools can be used to convert printed text or
images into editable text files.

3.03

1.09

Moderate

8. I can use AI tools to transform written texts into
instructional movies.

2.80

1.09

Moderate

Overall

3.06

0.84

Moderate

Indicative Statement
As a faculty member…

Table 8 presents teachers’ performance awareness regarding the use of artificial
intelligence in their teaching practices. The overall mean score of 3.06 (SD = 0.84) was classified as
"Moderate." The mean scores for individual statements ranged from 2.80 to 3.38, all falling under
the "Moderate" category. This suggests that teachers have a moderate level of awareness regarding
their ability to apply various AI tools in their teaching methods. The moderate level of performance
awareness indicates that although teachers are somewhat capable of using AI tools, they have
significant potential for improvement. Further training and resources focused on the practical
application of AI in education could help enhance teachers’ skills and confidence in using these
technologies.
The findings align with Uygun et al. (2024), who indicated that younger educators and those
with advanced academic qualifications tend to possess stronger practical knowledge of AI.
However, the study had certain limitations, such as a relatively small sample size and reliance on
the accuracy of participants’ responses. Despite these constraints, assessing teachers’ levels of AI
awareness is crucial for informing the development of AI-related educational programs. Gaining
insight into teachers’ perceptions and understanding of AI can guide the design of targeted training
initiatives and enhance educators’ skills in integrating AI technologies effectively into the
classroom.
Table 9. Mean level of respondents’ digital competence in terms of Information Processing
Descriptive
Indicative Statement
Mean
SD
Interpretation
As a faculty member…
1. I can find information on various search
engines.

4.15

0.90

High

2. I can locate trustworthy information online
using sophisticated search techniques.

1.90

0.30

Low

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Indicative Statement

Mean

SD

Descriptive
Interpretation

3. To compare and evaluate the accuracy of the
material I find, I apply a few criteria.

3.79

0.93

Moderate

4. Once files or content are saved or stored, I can
access them later.

4.01

0.97

High

5. I can store materials from the internet in many
formats. I can use cloud storage services, such
as Google Drive.

4.12

0.93

High

Overall

3.59

0.68

Moderate

Table 9 outlines the digital competence of teachers in terms of information processing. The
overall mean score of 3.59 (SD = 0.68) was categorized as "Moderate." Teachers demonstrated
strong competence in using various search engines (Mean = 4.15) and storing information in
different formats (Mean = 4.12). However, their ability to use advanced search strategies to locate
reliable information was low (Mean = 1.90), highlighting a key area for improvement. Their
competence in using filters to assess information reliability (Mean = 3.79) and saving content (Mean
= 4.01) ranged from moderate to high. While teachers are skilled at basic digital tasks and cloud
services, additional training in advanced search techniques may be beneficial to improve their
ability to find trustworthy information. This could include workshops or professional development
focused on effective search methods and evaluation of online sources.
In light of these findings, it may be construed that in order for teachers to be competent in
digital transformation and digital technologies, inculcation of both theoretical and practical
learning on digitalization should start as early as the undergraduate level of the field they are
specializing in and continue through the professional stages they may be climbing (Ferikoglu &
Akgun, 2022).
Table 10. Mean level of respondents’ digital competence in terms of Communication
Descriptive
Indicative Statement
Mean
SD
Interpretation
As a faculty member…
1. I can communicate with others using video calls
or chat using basic features (e.g. voice
messaging, SMS, text exchange).

4.42

0.89

High

2. I can use collaboration tools and contribute to
e.g. shared documents/files created by others.

4.24

0.92

High

3. I actively participate in online spaces and use
several online services (e.g. public services, ebanking, online shopping…).

4.14

0.89

High

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Mean

SD

Descriptive
Interpretation

4. I can use advanced communication tool features
(e.g. video conferencing, data sharing,
application sharing).

4.11

0.90

High

5. I pass on or share knowledge with others online
(e.g. via social networking tools or in online
communities).

4.06

0.91

High

Overall

4.19

0.82

High

Indicative Statement
As a faculty member…

Table 10 highlights teachers’ digital competence in communication. With an overall mean
score of 4.19 (SD = 0.82), categorized as "High," teachers demonstrate strong abilities in this area.
They excel at using basic communication tools like video calls and chats (Mean = 4.42) and
effectively utilize the advanced features of these tools (Mean = 4.11). High proficiency was also
evident in their use of collaboration tools (Mean = 4.24) and participation in online spaces and
services (Mean = 4.14). In addition, they scored well in sharing knowledge online through various
platforms (Mean = 4.06).
This high level of competence reflects teachers’ readiness to integrate digital
communication tools into their teaching practices, which is a critical skill for creating dynamic and
interactive learning environments, especially in blended or remote learning settings.
The findings align with Annus (2024) who emphasized that a teacher’s primary role must
shift from being the sole knowledge provider. Rather than simply presenting information, teachers
are now expected to guide students toward developing independence and collaborative thinking
skills. In the digital age, self-sufficiency is crucial and includes the capacity to effectively search for,
locate, and use information.
Table 11. Mean level of respondents’ digital competence in terms of Content Creation
Descriptive
Indicative Statement
Mean
SD
Interpretation
As a faculty member…
1. I can produce simple digital content (e.g. text,
tables, images, audio files) in at least one format
using digital tools.

4.11

0.90

High

2. o I can make basic editing of content produced
by others (e.g., adding and deleting).

4.11

0.90

High

3. I know how to apply licenses and copyrights
and when necessary.

4.15

0.90

High

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Mean

SD

Descriptive
Interpretation

4. I can modify simple functions of software and
applications can be modified by changing the
default settings.

3.62

1.02

Moderate

5. I can use several programming languages. I
know how to design, create, and modify
databases using computer tools.

3.13

1.09

Moderate

3.82

0.76

High

Indicative Statement
As a faculty member…

Overall

Table 11 outlines teachers’ digital competence in content creation, with an overall mean
score of 3.82 (SD = 0.76), classified as "High." Teachers exhibited strong skills in creating simple
digital content (Mean = 4.11) and performing basic edits (Mean = 4.11). They also displayed a solid
grasp of licenses and copyright application (Mean = 4.15). However, their proficiency in modifying
software settings (Mean = 3.62) and working with programming languages (Mean = 3.13) was
moderate.
The high level of competence in basic content creation tasks indicates that teachers are
well-prepared to integrate digital tools into their instructional methods. Nevertheless, the
moderate programming and software modification scores highlight opportunities for additional
training. Improving these skills could enable teachers to produce more advanced digital content
and maximize the capabilities of educational technologies.
Reflecting findings similar to those of Annus (2024), the present study highlights AI’s
transformative potential in education. We identified key areas and technologies where AI could
significantly impact teaching and learning. Machine learning and data analytics, for instance, will
allow educational systems to better address individual student needs. Therefore, students need to
learn how to leverage these available technologies. However, critical thinking and strong
understanding are necessary for students to accurately interpret and apply AI tools effectively. In
this regard, teachers play a crucial role in guiding students to use AI resources responsibly and
ethically. Our survey revealed that educators, across age groups and disciplines, generally welcome
intelligent systems that enhance teaching support.
Table 12. Mean level of respondents’ digital competence in terms of Safety
Descriptive
Indicative Statement
Mean
SD
Interpretation
As a faculty member…
1. I can take basic steps to protect my devices (e.g.
using anti-viruses and passwords).

3.91

0.86

High

2. I use different passwords to access equipment,
devices, and digital services, and I modify them
periodically.

3.82

0.99

High

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Indicative Statement

Mean

SD

Descriptive
Interpretation

3. I understand the health risks associated with
the use of digital technology (e.g., risk of
addiction).

4.13

0.88

High

4. I take basic measures and actions to save energy.

4.07

0.87

High

5. I understand the positive and negative impacts
of technology on the environment.

4.30

0.84

High

Overall

4.05

0.76

High

Table 12 highlights teachers’ digital competence in the area of safety, with an overall mean
score of 4.05 (SD = 0.76), categorized as "High." Teachers demonstrated strong abilities in basic
device protection (Mean = 3.91) and regularly updating passwords (Mean = 3.82). They also scored
highly for understanding the health risks associated with digital technology (Mean = 4.13) and
adopting basic energy-saving measures (Mean = 4.07). The highest competence was observed in
recognizing the environmental impact of technology, both positive and negative (Mean = 4.30).
This high level of safety awareness reflects teachers' preparedness to safeguard their
devices and use technology responsibly. These skills are essential for fostering a secure and
sustainable digital environment for both teachers and students. Ongoing training and professional
development can further reinforce and enhance these competencies.
As noted by Karsenti (2019), platforms such as Google, Facebook, YouTube, and Wikipedia
were almost unheard of two decades ago. Today, they rank among the most widely utilized digital
tools worldwide. In this rapidly evolving landscape, digital technology has significantly influenced
the economic, social, and cultural development of societies. With new technologies increasingly
shaping daily life and resonating with younger generations, educational institutions must adapt by
integrating these tools. Moreover, in line with 21st-century objectives, nations expect teachers and
students alike to embody the qualities of informed and responsible digital citizens.
Table 13. Mean level of respondents’ digital competence in terms of Problem Solving
Descriptive
Indicative Statement
Mean
SD
Interpretation
As a faculty member…
1. I find support when a technical problem occurs
or when using a new program.

3.94

0.86

High

2. I know that digital tools can help me solve
problems.

3.99

0.88

High

3. I am aware of new technological developments.
I understand how new tools work.

3.86

0.80

High

4. I am aware of I need to update my digital skills
regularly.

4.23

0.78

High

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Adv. J. STEM. Ed.

Indicative Statement

Mean

SD

Descriptive
Interpretation

5. I frequently update my digital skills to decrease
my limits and increase my knowledge.

3.94

0.82

High

Overall

3.99

0.72

High

Table 13 presents the teachers’ digital competence in terms of problem-solving skills. The
overall mean score of 3.99 (SD = 0.72) was classified as "High." Teachers demonstrated high
competence in finding support for technical problems (Mean = 3.94) and knowing that digital tools
can help solve problems (Mean = 3.99). There was also a high awareness of new technological
developments (Mean = 3.86) and the need for regularly updating digital skills (Mean = 4.23). The
frequent updating of digital skills to increase knowledge also scores high (Mean = 3.94).
The high level of problem-solving competence indicates that teachers are well-equipped to
handle technological challenges and adapt to new digital tools. This is crucial for maintaining
effective teaching practices in a rapidly evolving digital landscape. Continuous professional
development and access to support resources can further enhance these competencies, ensuring
that teachers stay current with technological advancements and integrate them effectively into
their teaching methods.
Table 14. Test of Significant Difference in the Respondents’ Level of IA Awareness when grouped
according to their profile
Profile
df
Chi-square
p-value
Remarks
Age

124

100.357

.941

Not Significant

Sex

4

3.092

.543

Not Significant

Highest Educational Attainment

16

9.938

.870

Not Significant

Length of Service

88

93.943

.313

Not Significant

Grade Level Taught

12

8.326

.759

Not Significant

Relevant Trainings

8

15.906

.044

Significant

Table 14 summarizes the test for significant differences in respondents' AI awareness
levels based on various profile attributes. Statistical significance was assessed using the p-value,
with results indicating that only attendance in relevant training programs showed a significant
difference (p = .044). This finding highlights that participation in AI and ICT-related training
significantly enhances teachers’ awareness of AI, emphasizing the critical role of continuous
professional development in keeping educators informed and skilled in AI technologies.
Similarly, Uygun et al. (2024) found that younger teachers and those with higher academic
qualifications generally possess a stronger practical understanding of AI. However, this study has
some limitations, including a small sample size and reliance on the accuracy of participant
responses. Despite these challenges, understanding teachers’ awareness of AI is vital for designing
AI-focused educational programs. By exploring teachers’ perspectives and knowledge, targeted
training initiatives can be developed to enhance teachers’ capacity to effectively integrate AI
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Adv. J. STEM. Ed.

technologies into teaching practices.
Table 15. Test of the Significant Relationship between Teachers’ AI Awareness and Digital
Competence
rVariables
Strength
p-value
Remarks
value
AI Awareness and Digital
Competence

0.662

Moderate

.000

Significant

Table 15 presents the test results for the significant relationship between teachers’ AI
awareness and their digital competence. The r-value, which measures the strength of the
relationship, is 0.662, indicating a moderate positive correlation. This suggests that as teachers’ AI
awareness increases, their digital competence also improves. The p-value of .000 confirms that this
relationship is statistically significant.
This result underscores the strong connection between AI awareness and digital
competence among teachers. By enhancing AI awareness through targeted training and
professional development, teachers can improve their overall digital skills. This highlights the need
for comprehensive training programs that simultaneously address both AI awareness and digital
competence, equipping educators to integrate technology more effectively into their teaching
practices.
The profound impact of modern technology across various sectors, with education being
among the most significantly affected. The global transition to the fifth generation of the Internet,
known as the Internet of Things (IoT), has opened up new opportunities in education. The
integration of Artificial Intelligence (AI) into teaching and learning has gained considerable
traction.
Educational institutions worldwide are increasingly adopting AI-driven strategies to
enhance administrative and instructional functions. This integration is vital for improving
operational efficiency, streamlining educational management, and boosting learning outcomes. The
past decade has seen a significant rise in AI applications for learning support and enhancement, a
trend that accelerated during the COVID-19 pandemic as schools and universities turned to AI tools
to facilitate remote learning and ensure educational continuity.
In addition, AI technology companies have experienced a notable increase in user
registrations from the education sector, indicating a market shift toward AI adoption. This growth
reflects both the sector’s confidence in AI solutions and its recognition of AI’s potential to drive
meaningful improvements in learning environments. As AI technologies continue to evolve, their
integration into education is expected to deepen, with future applications becoming more
sophisticated and tailored to meet the specific needs of educators and learners.
CONCLUSIONS
Considering the study findings, the following conclusions were drawn: The findings of this
study indicate that among the various profile attributes examined, only attendance in AI- and ICTrelated training demonstrated a statistically significant influence on teachers’ awareness of
artificial intelligence (p = .044). This suggests that professional development initiatives specifically
targeting emerging technologies play a crucial role in shaping educators’ understanding of AI.
Consequently, the hypothesis that all profile variables bear no significant influence on AI awareness
is partially upheld, highlighting the critical importance of targeted training programs in fostering
technological literacy among teachers.
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Adv. J. STEM. Ed.

In contrast, the hypothesis positing no significant relationship between teachers’ AI
awareness and their digital competence is rejected. The analysis yielded an r-value of 0.662,
denoting a moderate positive correlation, and a p-value of .000, confirming statistical significance.
These results suggest that increased familiarity with AI concepts is closely associated with higher
levels of digital competence.
Theoretically, this study contributes to the growing body of literature on digital
transformation in education by reinforcing the interdependence between conceptual awareness of
artificial intelligence and broader digital competencies. This underscores the notion that AI
awareness is not an isolated construct but is intricately linked to educators’ capacity to operate
effectively within digitally mediated environments. This insight aligns with and extends existing
models of technological integration, such as the Technological Pedagogical Content Knowledge
(TPACK) framework, by emphasizing the foundational role of AI literacy in achieving
comprehensive digital competence. As such, the findings provide a valuable lens through which
future training programs and policy interventions can be developed to bridge gaps in both
awareness and practical digital skill sets among educators.
Recommendations
Based on the study findings, the following recommendations were made:
1. Given that participation in relevant training was found to significantly enhance teachers’
awareness of artificial intelligence, it is recommended that educational institutions develop
and implement structured, ongoing professional development programs focused on AI and
ICT integration. These training initiatives should be accessible, modular, and contextspecific, allowing educators to build their competencies progressively while aligning with
their teaching needs. Incorporating hands-on workshops, case studies, and practical
applications of AI tools in the classroom can further support the effective translation of
knowledge into practice. Institutionalizing such training efforts, schools can ensure that
teachers remain well-informed about technological advancements and are equipped to
apply them meaningfully in instructional settings.
2. To further develop teachers’ skills, it is crucial to support continuous professional
development through workshops, seminars, and certification programs focused on AI and
digital tools. Regular training can help teachers improve their knowledge and digital
competence, enabling them to apply AI effectively in their classrooms.
3. Considering the strong connection between AI awareness and training, educational
policymakers may design AI-focused programs that specifically address teachers’ needs.
These programs should emphasize the practical use of AI in educational settings and
highlight how it can enhance student learning outcomes.
4. Schools should foster collaborative learning environments where teachers can exchange
ideas, strategies, and challenges related to AI use. This could be supported through peer
learning, online discussion forums, or mentorship programs that encourage shared
learning experiences.
5. Teachers may be encouraged to engage in AI-related research or pilot initiatives. Direct
involvement in AI projects can deepen students’ understanding of its applications in
education and enhance their digital skills, leading to better teaching practices and student
outcomes.
LIMITATIONS AND FUTURE RESEARCH
This study provides valuable insights into the relationship between teachers’ AI awareness
and digital competence, but it is limited by its scope and methodology. The sample was drawn from
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Adv. J. STEM. Ed.

a specific educational context, which may have affected the generalizability of the findings. In
addition, the use of self-reported data introduces the risk of bias, as participants may have
misjudged their actual competence or awareness. The correlational design also limits the ability to
draw causal conclusions, leaving room for other influencing factors that were not examined.
Future research should consider broader and more diverse samples to validate these
findings across different regions and school systems. Qualitative methods, such as interviews and
observations, can enrich the data and provide a deeper context. Longitudinal studies would also
help track the long-term effects of AI-related training. Finally, further investigation into
institutional factors, such as infrastructure, leadership support, and policy—could offer a more
comprehensive understanding of the factors that enable effective AI integration in education.

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