SHAPING THE FUTURE OF AI IN EDUCATION: ANALYZING KEY INFLUENCERS ON ROMANIAN TEACHER TRAINEES' WILLINGNESS TO INTEGRATE AI

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PUBLISHED DATE: - 28-09-2024
DOI: -

https://doi.org/10.37547/tajssei/Volume06Issue09-17

PAGE NO.: - 158-173

SHAPING THE FUTURE OF AI IN EDUCATION:
ANALYZING KEY INFLUENCERS ON
ROMANIAN TEACHER TRAINEES'
WILLINGNESS TO INTEGRATE AI

Asim-Ittah Gideon Attach

Department of Guidance and Counseling, University of Uyo, Uyo, Nigeria

Nelson Michael Etim

Philosophy of Education, University of Uyo, Nigeria

Inyang, Sifon Ime

Department of Guidance and Counseling, University of Uyo, Uyo, Nigeria

Oguzie, Blessing Akudo

Department of Guidance and Counseling, University of Uyo, Uyo, Nigeria

John Sunday Ekong

Educational Management and Planning, University of Uyo, Nigeria

INTRODUCTION

Artificial intelligence (AI) has evolved significantly
since the development of the first mathematical

model of the biological neuron in 1943 (McCulloch
& Pitts, 1943) and the landmark Dartmouth
Conference of 1956, considered the birthplace of

RESEARCH ARTICLE

Open Access

Abstract

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AI (McCarthy, 1998). As AI continues to advance,

UNESCO’s 2019 Beijing Consensus highlighted the

need for integrating AI into educational policies
and practices, with representatives from over 100
countries offering guidance on how AI can be
harnessed for education. Despite these initiatives,
research into the factors influencing teachers'
willingness to integrate AI remains underexplored,
particularly during their training stage (Davis,
1989).

The integration of artificial intelligence (AI) into
educational practices is becoming an increasingly
significant aspect of modern teaching. Future
educators must understand and appropriately
incorporate AI to enhance the efficiency and
effectiveness of the learning process. Studies by
Kim, Soyata, and Behnagh (2018) demonstrate
how AI technologies can provide real-time
feedback to teachers during presentations by
analyzing audio and visual elements, thereby
improving the overall quality of delivery and
audience engagement. Similarly, Woolf et al.
(2013) underscore the long-term potential of AI to
personalize learning experiences and improve
educational outcomes through the evaluation of
datasets on teaching behaviors, student
motivation, and social interaction.

Despite the promise AI holds for education, certain

challenges remain. Păvăloaia and Necula (2023)

highlight concerns regarding the high costs of
integrating AI technologies, the risk of job
displacement, and the potential increase in energy
consumption. Moreover, excessive reliance on
virtual environments can lead to dependency,
reduced empathy, and communication difficulties.
Security concerns also persist, as the vulnerability
of AI-generated data raises the possibility of
breaches or data theft (Pisica et al., 2023).

Although the benefits of AI are widely recognized,
its implementation within educational institutions
is far from straightforward. Bonsu and Baffour-

Koduah (2023) note that there is significant
pressure on educational institutions to establish
clear guidelines and standards for AI usage.
Additionally, Moorhouse and Kohnke (2023) argue
that teacher educators require ongoing support to
develop the skills necessary for effectively utilizing
AI in their practice. Integrating AI in education is a
complex process that demands careful planning to
ensure both teachers and students are able to fully
grasp and safely utilize the benefits of this
technology.

To design effective AI integration strategies, it is
essential to first understand the knowledge, skills,
and perceptions of both teachers and students. A
growing div of literature examines teacher
attitudes toward AI and general artificial
intelligence (GAI) technologies. Kaplan-Rakowski
et al. (2023) found that educators who possess a
more favorable view of GAI are more likely to
incorporate it into their teaching. Building on this
insight, the current research will assess the extent
to which Romanian student-teachers perceive the
advantages and disadvantages of using AI in their
future classrooms.

The benefits of AI for educators extend beyond
content generation. Chounta et al. (2022) found
that Estonian teachers, despite having limited
knowledge of AI, have successfully used AI tools to
access and utilize multilingual content. However,
there remains no consensus on best practices for
using AI in teaching and research. Fahrman et al.
(2020) attribute this to the evolving nature of
teacher skills and the inherent complexity of
understanding and researching these changes.

Generational differences also play a role in
perceptions of AI. Chan and Lee (2023) revealed
that while Gen Z students are generally optimistic
about AI, Gen X and Gen Y teachers express
concerns about over-reliance on the technology
and its ethical and pedagogical implications. This
research will investigate how prospective teachers

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perceive their students’ expectations regarding AI,

as well as the ways in which student appreciation
for AI-savvy teachers influences the behavioral
intention to use AI in education. Ethical
considerations also remain central to the discourse
surrounding AI in education. Dalalah and Dalalah
(2023) argue that, without proper standards, AI
could compromise the authenticity and creativity
inherent in human-led teaching and learning.

In addition to examining teacher perceptions, a
second div of research explores student
interactions with AI. Chao et al. (2021) found that
students generally have a positive attitude toward
AI, though many share concerns regarding its use
in assessment processes. Ravi Kumar and Raman
(2022) reported that students value AI for its
potential in teaching and administrative tasks but
are wary of its application in admissions and
examinations. Doumat et al. (2022) further
observed that while a majority of students believe
AI assessments are more objective, only a small
percentage would prefer to be assessed by AI. This
ambivalence highlights the need for more refined
approaches to AI implementation.

Existing literature points to a gap in understanding
the behavioral intention of teachers to adopt AI
technologies. Choi, Jang, and Kim (2023) argue that
teachers are hesitant to integrate AI educational
tools, and there is little knowledge about their
perceptions of these technologies. Williamson and
Eynon (2020) further emphasize the lack of
understanding regarding how AI is used by both
students and teachers and how it can be effectively
implemented in educational settings. Given the
pivotal role that future teachers play in
implementing AI tools and influencing generations
of students, it is crucial to understand the factors
shaping their perceptions and willingness to adopt
AI in their teaching practices.

This study aims to fill a gap in the literature by
focusing on the perceptions of AI among future

teachers during their training stage. Prospective
teachers will play a critical role in shaping how AI
is used to develop the cognitive, social, and
communication skills of the next generation. The
research takes place within the context of
increasing global efforts to cultivate AI literacy in
early education, as demonstrated by initiatives
such as AI4ALL at Stanford and the International
Society for Technology in Education (ISTE).

Building on the Technology Acceptance Model
(TAM) established by Davis (1989), which is
widely used to explain the adoption of new
technologies, this research examines the factors
influencing prospective teachers' intention to use
AI. Just as Hasib et al. (2022) applied logistic
regression to predict student performance, this
study employs binary logistic regression to predict
the behavioral intention of future teachers to use
AI, based on their attitudes and perceptions of its
utility.

This study contributes to the existing div of
research by identifying and testing key factors that

influence future teachers’ intentio

n to use AI in

education. Unlike most studies that focus on

students’ attitudes toward AI in fields like

medicine, this research investigates the behavioral
intentions of Romanian student-teachers in
primary and secondary education. By using a
binary logistic regression model, this study
explores how different factors

—

such as AI

familiarity, perceptions of the educator’s evolving
role, and confidence in AI’s benefits—

influence the

willingness of future teachers to integrate AI into
their classrooms.

METHODOLOGY

This study aims to examine the intention of
prospective teachers to integrate artificial
intelligence (AI) into their educational practices
and to identify the factors that influence this
intention. To achieve this, a quantitative
exploratory research design was employed,

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utilizing a questionnaire-based survey to assess
the perceptions of students currently enrolled in
teacher training programs. The participants were
drawn from the Faculty of Education, Social

Sciences, and Psychology at Pitești Univer

sity

Centre, as well as its regional branch in Râmnicu
Vâlcea.

The survey consisted of a series of questions
designed to capture various aspects of the
participants' perceptions of AI. Specifically, the
questionnaire comprised 10 Likert-scale items
(ranging from "to a very small extent" to "to a very
large extent"), 4 multiple-choice questions, 1 open-
ended question, and 4 demographic questions. The
Likert-scale items aimed to assess participants'
attitudes toward AI integration, their perceived
ease of use, and their behavioral intention to
incorporate AI in teaching. The demographic
questions included age, gender, level of education,
and current professional status.

Given the exploratory nature of this study and the
challenges associated with obtaining a random
representative sample, a purposive sampling
method was employed. The selection criterion
focused on individuals studying education sciences
and teacher training who had some level of
awareness about AI technologies (Jurconi et al.,
2022). This allowed for the collection of relevant
data from those most likely to encounter AI in their
future careers.

The rationale for using a quantitative approach lies
in the objective to construct a predictive model
that identifies the key factors influencing
prospect

ive teachers’ intention to use AI.

Quantitative research facilitates the collection of a
large number of responses in a structured format,
which can be systematically analyzed and
validated using statistical techniques (Bell &
Waters, 2018). Furthermore, the objectivity of
quantitative research ensures that the results are
quantifiable, enabling potential generalization of

the findings to a broader population.

The questionnaire was distributed via email to all
undergraduate and graduate students enrolled in
the Pedagogy of Primary and Pre-school Education
program, as well as the Early Childhood Education

Master's program, at both Pitești University Centre

and Râmnicu Vâlcea Territorial Centre. Most
participants were either already employed in, or
intending to pursue, careers in primary or pre-
school education. The data collection period
spanned from May to August 2023, and responses
were gathered through Google Forms. Data
analysis was conducted using SPSS software,
which enabled the use of advanced statistical
methods to evaluate the relationships between
variables. A total of 270 valid responses were
received from a potential pool of 370 students,
resulting in a response rate of 73%.

Recognizing that AI integration in education is still
in its nascent stages, particularly in primary and
secondary education in Romania, the study sought
to explore the perceptions of prospective teachers
without requiring prior hands-on experience with
AI. Participation in the study was voluntary, and

the respondents’ anonymity

was maintained

throughout the process. The consent to participate
was implied by the completion of the survey, and
respondents were informed that the results would
be disseminated in aggregate form.

As detailed in Table 1, the sample predominantly
comprised female respondents (97.4%), a
demographic characteristic typical of primary and
pre-school education sectors globally. In terms of
age distribution, the study captured responses
from a balanced representation of Generation Z
(41.48%) and Generation Y (45.18%) participants,
with a smaller proportion from Generation X
(13.33%). This age distribution suggests a high
level of familiarity with new technologies among
the respondents. All participants were pursuing
either Bachelor's or Master's degrees and were in

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the process of preparing for their future careers. Of
the total respondents, 21.1% were current
teachers in pre-school and primary education,

39.62% were students intending to become
teachers, and 39.25% had not yet decided on their
future career paths.

Table 1: Gender, age, level of education and current occupation or career plan of the

respondents

This study examines the perspectives of two
distinct groups of prospective teachers: Group 1

—

students who have previously used AI, and Group
2

—

students who have not used AI. Several key

factors influencing the relationship between
educators and AI were explored, including:

i.

Knowledge of AI

ii.

Level of interaction and previous exposure

to AI tools

iii.

Readiness to integrate AI into teaching

practices

iv.

Desire for further AI-related training

v.

Perceived impact of AI on student-teacher

interactions

vi.

Improvement of the learning experience

vii.

Simplification of administrative tasks

viii. Expectations of students regarding AI
integration in the learning process

Following this analysis, a binary logistic regression
was conducted to predict the intention of future

teachers to adopt AI in their teaching practices.
The variables used in this predictive model are as
follows:

1. Dependent Variable

: I17

–

Behavioural

Intention to Use AI in future teaching careers, as
outlined in the Technology Acceptance Model
(TAM).

2. Independent Variables

:

2.1. I1

–

Previous use of AI

2.2. I3

–

Sufficient knowledge of AI and ability to

explain its concepts

2.3. I4

–

Ability to leverage the benefits of AI in

educational settings

2.4. I6

–

Perception that students appreciate

teachers who use AI

2.5. I8

–

Perception that AI alters the role of

educators

2.6. I10

–

Belief that AI will enhance student-

teacher interactions

2.7. I14

–

Self-assessment of ability to teach

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using AI (ease of use, as defined by TAM)

2.8. I15

–

Perception that future teachers must

master and use AI

2.9. I16

–

View that the advantages of AI

outweigh its disadvantages (perceived usefulness,
as defined by TAM)

The examination of behavioural intention to use AI
is critical within the TAM framework, as it serves
as a precursor to actual usage (Davis, 1989). Table

2 presents the reliability measures for both
constructs: attitude toward AI use and perception
of AI usefulness. For each construct, the Cronbach's

Alpha (α) exceeded 0.700, the Average Variance

Extracted (AVE) was greater than 0.500, and the
Composite Reliability (CR) surpassed 0.700,
demonstrating strong internal consistency across
the study's variables (Henseler & Sarstedt, 2013;

Nemțanu et al., 2021).

Table 2: Validation of data

Based on the literature review and existing studies,
several hypotheses have been developed to
examine the factors influencing the behavioural
intention of prospective teachers to integrate AI
into their teaching practices. Research by Labrague
et al. (2023) demonstrates that prior exposure to
AI technologies, coupled with knowledge and
competence in AI usage, leads to a more positive
perception of AI and a higher likelihood of
incorporating it into professional practices. In light
of this, the following hypotheses are formulated:

1. H1

: Previous use of AI significantly and

positively influences the behavioural intention to
use AI in future teaching careers.

2. H2

: Knowledge of AI significantly and positively

influences the behavioural intention to use AI in
future teaching careers.

Further, Ali (2017) argues that teachers'
willingness to adopt AI is shaped by their students'
needs and expectations. Teachers who observe
their students' enthusiasm and positive
experiences with AI are more inclined to integrate
AI into their teaching methods. This finding leads
to the third hypothesis:

3. H3

: Students' expectations and requirements for

new AI technologies significantly and positively
influence the behavioural intention to use AI in
future teaching careers.

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Moreover, teachers' understanding of their role in
guiding students' use of technology, as well as their
own perception of AI's utility in their work, is
critical. Teachers will have a key responsibility in
ensuring AI is used ethically and effectively,
particularly in evaluating student performance
and ensuring the accuracy of data (Owan et al.,
2023). This leads to the formulation of the fourth
hypothesis:

4. H4

: The perception of AI-induced changes in the

educator's role significantly and positively
influences the behavioural intention to use AI in
future teaching careers.

However, the adoption of AI in education can be
hindered by external barriers (e.g., limited access
to hardware, software, and training) and internal
barriers (e.g., lack of trust and negative attitudes
toward AI). Rowston, Bower, and Woodcock
(2022) argue that teachers' beliefs, confidence, and
attitudes play a critical role in determining
whether they incorporate AI into their teaching.
Based on this, the following hypothesis is
proposed:

5. H5

: The perception of one's own ability and

confidence to use AI in teaching significantly and
positively influences the behavioural intention to
use AI in future teaching careers.

Finally, Chan and Hu (2023) found that university
students in Hong Kong were more willing to adopt
General AI (GAI) technology as they recognized its
benefits for learning and academic tasks. This

insight supports the following hypothesis:

6. H6

: The perception of AI's advantages

outweighing

its

disadvantages

(perceived

usefulness) significantly and positively influences
the behavioural intention to use AI in future
teaching careers.

RESULT AND DISCUSSION

Extent of familiarity of future teachers with AI

Upon analyzing the data, it was found that 77.41%
of students enrolled in teacher education
programs were familiar with AI, 27.04% had
conducted further research on AI, but only 20%
had actually used AI. To better understand how AI
usage influences perceptions, the data were
divided into two groups: Group 1

–

students who

had used AI, and Group 2

–

those who had only

heard or read about AI but had not used it.

Within Group 1, 59.26% had used AI for
educational purposes, 48.15% out of curiosity, and
37.04% for work-related or entertainment tasks.
The relatively low rate of AI usage can be
attributed to the early stage of AI technology
development.

Respondents' self-assessed knowledge of AI was
rated as average, with Group 1 demonstrating a
higher level of understanding, as shown in Table 3.
Notably, interest in improving AI usage in teaching
was high across both groups, with over 76% of
respondents expressing a desire to attend training
courses focused on AI integration in education.

Table 3: Extent of knowledge of AI

Category of results

Group 1 –

Group 2 –

Used AI

Did not use AI

How well can they define/explain what AI entails

3.12

2.85

I know how to take advantage of AI in school/kindergarten

3.12

2.64

Note: arithmetic weighted average on a scale from: 1 – to a very small extent; 5 – to a very large extent

Source: Researcher, 2024

Changes brought by AI in the educator’s role

Regarding the changes AI may bring to education,

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approximately

half

of

the

respondents,

irrespective of their group, believe that AI will be
used as a teaching tool in the future. A notable
example of this is seen in China, where 60,000
schools implemented an AI-driven system for
automatic essay grading, achieving a 92% accuracy
rate, comparable to human assessment (UNESCO,
2019a). This system utilizes AI neural networks
and deep learning algorithms to compare student
essays with human evaluations.

According to Table 4, the second most anticipated
role for AI is as a virtual teaching assistant, with
30% of Group 1 respondents, those who have used
AI, seeing this as a possibility. Meanwhile, 27% of
Group 2, who have not used AI, believe AI will help

personalize learning experiences more than
serving as a virtual assistant. Only a small
percentage (6-7%) believe that AI will not change
the educational process significantly.

An interesting divergence between the groups
emerges regarding the potential for AI to replace
teachers. While 5% of Group 2 respondents view
AI as a potential threat that could replace
educators, none of the Group 1 respondents, those
with AI experience, consider this a plausible
scenario. This aligns with findings by Edwards and
Cheok (2018), who argue that machines lack the
social and emotional capabilities necessary for
meaningful interaction with students.

Table. 4: Changes brought by AI in education and the role of an educator

Source: Researcher, 2024

To gain a clearer understanding of how the

educator’s role may evolve with the integration of
AI into education, the respondents’ key estimates

are presented in Table 4. Both groups

—

those with

and without AI experience

—

share a common

perception that AI will shift the e

ducator’s role

from traditional instruction and knowledge
delivery to that of a facilitator and mentor, guiding
students through the learning process. This
transformation is especially noted among those

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who have previously used AI, who also highlight

AI’s

potential to enhance the speed of feedback to

students and facilitate personalized learning
experiences.

Additionally, AI is seen as a tool to alleviate some
of the routine administrative tasks, such as
document centralization and result tracking,
enabling teachers to focus more on student
engagement. AI could also extend learning time by
offering students support and answering
questions outside the classroom environment.

As shown in Table 5, there is a consensus between

both groups regarding the expectations of
students,

particularly

preschool

and

schoolchildren, for teachers to integrate AI into
their teaching practices. With average ratings of
3.87 and 3.49, respondents acknowledge that
these expectations make AI integration a necessity
for future teachers to stay relevant and maintain
student interest in educational activities. Other
anticipated benefits of AI integration include
improved student-teacher interaction, enhanced
learning experiences for students, and a shift
toward more personalized education.

Table 5: Reasons, causes and effects for/of using AI in education

Source: Researcher, 2024

Regardless of their group affiliation, respondents
uniformly agree that future teachers should be
proficient in and utilize AI within their teaching
activities. The data reveals that current ability
levels to use AI in teaching (averaging 3.00 and
2.79) are lower compared to the anticipated use of
AI in future teaching roles (averaging 3.62 and
3.29). This disparity suggests a positive inclination
towards AI technology, indicating confidence in its
future application and perceived usefulness in
educational settings. Notably, there is a minimal

difference in the intention to use AI between the
two groups (3.62 for those with AI experience
versus 3.29 for those without), reflecting a
widespread eagerness among future educators to
incorporate AI into the teaching-learning process,
regardless of their current exposure to such
technology or their generational digital skills.

A comprehensive overview of AI integration into
teaching practices is detailed in Table 6. Both
groups exhibit a shared understanding of the
necessity of using AI for generating educational

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content and presenting it interactively for young
learners. AI is anticipated to significantly support
personalized learning experiences and function as
a virtual assistant available around the clock. This

expectation aligns with Kim et al.'s (2020) findings
that AI is particularly beneficial when face-to-face
interaction is not feasible.

Table 6: Ways of using AI in education (% of total respondents)

Group 1

–

Group 2

–

Category of results

Used AI

Did not

use AI

- AI can create intelligent/interactive content and boost student

69%

50%

engagement

- AI can help personalise the learning experience

52%

42%

- AI can help as a ready-to-use virtual assistant 24/7

41%

27%

- AI can automate administrative processes

37%

21%

- Based on the collected data, AI can be used to update curriculum

35%

11%

and instructional methods

- AI can automate the assessment process

20%

13%

Source: Researcher, 2024

Notable differences are evident between the two
groups regarding AI's role in updating curriculum
and training methods. Specifically, 35% of Group 1
respondents

—

those with AI experience

—

support

this use of AI, compared to just 11% of Group 2
respondents

—

those without AI experience. This

disparity can be attributed to Group 1's better
understanding of AI's capability to process and
analyze large datasets.

Another interesting finding is the variation in
attitudes towards AI involvement in the evaluation
process, with Group 2 showing notable reluctance.
This aligns with Doumat et al. (2022), who report
that only 26% of students favor AI-based
assessments. In contrast, Samarescu (2021) argues
that AI could enhance the assessment process by
providing detailed and personalized feedback.

Respondents were also invited to suggest
additional AI applications through an open-ended

question: “What is the most difficult task AI can
help with?” Many highlighted AI's potential in
“designing teaching activities,” followed by
reducing

“administrative

tasks”

through

automation. This would allow educators to focus
more on teaching. Additionally, respondents see

value in AI’s ability to “capture and maintain

students' attention,” making the learning

experience more engaging and dynamic. Lastly,

there is interest in integrating AI for “developing
and managing online courses.”

Future teachers’

behavioural intention to use

AI

The study investigates the behavioral intention of
future teachers to use AI and identifies key factors
influencing this intention through binary logistic
regression. The goal was to determine the
regression equation that predicts whether future
teachers are inclined to incorporate AI into their
teaching practices.

In the analysis, the dependent variable, I17
(Behavioral Intention to Use AI in Future Career),
was coded as 1 for those intending to use AI and 0
for those not intending to. Compared to the
baseline model (Model 0), which had an accuracy
rate of 80.7%, the regression model including
independent variables (Model 1) improved the
prediction accuracy to 89.3%.

Validation tests showed that Model 0, constructed
using only the constant, was valid (B=1.433;
S.E.=0.154;

Wald=86.246;

Sig.=0.000;

Exp(B)=4.192). The Chi-square test confirmed that
Model 1 is significantly better than the baseline

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model (Sig.=0.000; Chi-square=120.206; df=6).
Model 1's -2 Log likelihood=144.373 and Cox &
Snell R Square=0.359 indicate a strong fit. The
Nagelkerke R² value of 0.575 suggests that 57.5%
of the variance in the dependent variable (I17) is
explained by the predictors in Model 1.
Additionally, the Hosmer and Lemeshow test
confirmed Model 1's validity, with a p-value > 0.05
(Chi-square=14.960; Sig.=0.60; df=8).

Results from Model 1, presented in Table 7, reveal

that two factors significantly influence the
intention to use AI: self-confidence in using AI
(I14) and the perceived advantages of AI over its
disadvantages (I16). These factors notably
increase the likelihood of future teachers adopting
AI in their careers. In contrast, other factors such
as prior use of AI (I1), knowledge about AI (I3),
student demands for AI (I6), and the perception of

AI’s impact on the educator’s role (I8) do not

significantly affect the intention to use AI.

Table 7: Variables in equation for Model 1

B

S.E. Wald df

Sig. Exp(B)

95% C.I. pt EXP(B

Inferior Superior

S I1. Use of AI

1.421 .769 3.417

1

.065

4.141

.918

18.682

T I3. Knowledge of AI

-.005 .220

.001

1

.980

.995

.646

1.530

E I6. Required by students

.321 .208 2.379

1

.123

1.378

.917

2.073

P I8. Change of

.141 .217

.420

1

.517

1.151

.753

1.760

1

educator’s role

I14. Able to teach using AI

.716 .249 8.285

1

.004

2.046

1.257

3.331

I16. More advantages than

1.684 .291 33.537

1

.000

5.387

3.047

9.526

disadvantages

Constant

-6.526 1.149 32.288

1

.000

.001

a. Variable(s) introduced for step 1: I1, I3, I6, I8, I14, I16.

Source: Researcher, 2024

The results for variable I14 (B=0.716;
Exp(B)=2.046; Sig.=0.004) support the validation
of Hypothesis H5: The perception of one's own
ability and confidence in using AI positively
influences the intention to use AI in a future
teaching career. Specifically, individuals who
believe they are capable of using AI in teaching are
twice as likely to integrate AI into their educational
practices.

Similarly, the results for variable I16 (B=1.684;
Exp(B)=5.387; Sig.=0.000) validate Hypothesis H6:
Perceived advantages of AI over its disadvantages
significantly and positively impact the intention to
use AI in future teaching. This finding underscores
the importance of presenting AI in a positive light
from the outset, as it significantly increases the
likelihood of future teachers adopting AI in their

work.

These results align with Al Darayseh’s (2023)

research, which highlights that behavioral

intentions are shaped by factors such as “expected
benefits” and “ease of use.” However, the values for

variables I1 (Use of AI) (B=1.421; Exp(B)=4.141;
Sig.=0.065), I6 (Student Requirements) (B=0.321;
Exp(B)=1.378; Sig.=0.123), and I8 (Changes in
Educator's Role) (B=0.141; Exp(B)=1.151;
Sig.=0.517) indicate a positive relationship with
the behavioral intention to use AI (I17), but these
relationships are not statistically significant (Sig. >
0.05). Thus, Hypotheses H1, H3, and H4 are
partially validated, suggesting that while these
factors may influence the intention to use AI, their
effects are not strong enough to be deemed
significant.

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On the other hand, the value for variable I3
(Knowledge of AI) (B=-0.005; Exp(B)=0.995;
Sig.=0.980) reveals that mere knowledge of AI does
not significantly affect the intention to use AI in a
future teaching career. This result invalidates
Hypothesis H2: Knowledge of AI significantly and
positively influences the behavioral intention to
use AI in the future teaching career. Despite
varying levels of knowledge or digital skills, there
remains a strong interest in improving and
utilizing AI in education due to its potential
benefits.

CONCLUSION

AI has the potential to significantly complement
and enhance the role of educators, leading to more
effective and personalized learning experiences.
The findings from this study, involving 270 future
teachers, highlight that while 77% have heard of
AI, only 20% have used it, primarily for personal
educational purposes. However, there is
considerable interest in further training, with 76%
expressing a desire to learn more about AI,
indicating an awareness of its potential in
educational development.

Interestingly, a small percentage (5%) of those
who have not used AI perceive it as a threat, fearing
it may replace teachers. In contrast, most
respondents value AI's potential as a teaching tool
or virtual assistant to enhance the student learning
experience. Although only 62.96% currently feel
capable of using AI in their future teaching roles,
81.48% acknowledge the need to master and
incorporate AI technology. Key benefits identified

by respondents include AI’s ro

le in creating

educational content, enhancing interactivity,
extending learning time, and automating
administrative tasks. These applications are
expected to shift the teacher's role from traditional
instruction to that of a facilitator and mentor,
providing personalized and instantaneous
feedback. The regression analysis reveals that the

intention to use AI in future teaching careers is
significantly influenced by confidence in using AI
(perceived ease of use) and recognizing its
advantages (perceived usefulness). Surprisingly,
these factors outweigh the impact of previous AI

use, student expectations, and awareness of AI’s
impact on the educator’s role.

The study reinforces the belief that AI will
fundamentally enhance education, with the
transition influenced by teachers' confidence and
positive examples of AI use. This research
integrates and evaluates several predictors of the
behavioral intention to use AI in education within
a single regression model. It underscores the
relevance of these predictors, particularly
confidence in AI and perceived benefits, which are
crucial for fostering AI adoption.

The findings stress the importance of building
confidence in AI use and highlighting its benefits to
encourage future adoption. This insight can inform
the development of training programs and
educational policies aimed at accelerating AI
integration into teaching practices. Future
research should include qualitative approaches to
explore motivations and behavioral intentions in
greater depth. Expanding studies to active AI users
and evolving educational contexts will be crucial
for updating perceptions and factors influencing AI
adoption in education.

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