Future Business Journal,
Journal Year:
2024,
Volume and Issue:
10(1)
Published: Nov. 27, 2024
Abstract
The
increasing
integration
of
AI
technologies
such
as
ChatGPT
in
educational
systems
calls
for
an
in-depth
understanding
the
factors
influencing
students’
intentions
to
use
these
tools.
This
study
explores
shaping
university
by
analysing
three
key
dimensions:
task
characteristics,
technology
characteristics
and
individual
characteristics.
Using
task-technology
fit
(TTF)
framework,
research
examined
how
elements
impact
alignment
between
tasks
ChatGPT’s
capabilities,
ultimately
driving
behavioural
intentions.
A
survey
393
students
from
a
Saudi
Arabian
was
conducted,
structural
equation
modelling
applied
assess
relationships
among
variables.
Results
indicated
that
all
significantly
influenced
TTF,
which
turn
had
positive
on
ChatGPT.
highlighted
importance
achieving
strong
TTF
encourage
effective
tools
academic
settings.
implications
this
suggest
institutions
should
focus
aligning
with
learning
enhance
their
intent
tools,
thereby
improving
performance.
Furthermore,
extended
model
context
AI-powered
particularly
line
Arabia’s
Vision
2030.
is
one
first
investigate
within
unique
cultural
technological
higher
education
system.
By
integrating
framework
local
regional
factors,
provides
novel
insights
into
drivers
usage
education,
offering
guidance
policy
broad
practices.
Journal of Consumer Sciences,
Journal Year:
2025,
Volume and Issue:
10(1), P. 27 - 58
Published: Jan. 31, 2025
Background:
Student
interest
in
entrepreneurial
pursuits
remains
low,
despite
the
significant
contributions
of
entrepreneurship
to
economic
growth.
Purpose:
This
study
investigates
factors
influencing
IPB
students'
adopting
AI-based
learning
through
lens
design
thinking,
emphasizing
role
communication
methods
and
their
impact
on
motivation
attitudes.
Methods:
adopts
a
mixed-method
design,
combining
quantitative
qualitative
approaches.
Quantitative
data
were
collected
via
an
online
survey
from
173
students,
with
166
valid
responses
after
cleaning.
analysis
was
conducted
using
descriptive
statistics
(SPSS
25)
Partial
Least
Squares
Structural
Equation
Modeling
(PLS-SEM).
The
aspect
involved
SCAMPER
within
thinking
framework
explore
AI
integration
education.
PICOS
applied
adoption
higher
education
comprehensively.
approach
provides
holistic
understanding
educational
contexts.
Findings:
Results
indicate
that
significantly
affects
intentions
engage
systems,
positively
impacting
attitudes
toward
AI.
Perceived
ease
use
also
influences
perceived
usefulness,
although
usefulness
does
not
directly
motivation.
Additionally,
interpersonal
interactions
mass
media
influence
while
awareness
have
effect.
Conclusion:
Expanding
requires
strategic
communication,
mainly
focusing
Design
Thinking’s
empathize
phase
understand
student
challenges.
By
iteratively
proposing
tools
prototype
phase,
institutions
can
develop
user-friendly,
engaging
solutions
tailored
needs,
fostering
engagement
learning.
Research
implication:
These
insights
suggest
targeted
strategies,
including
principles,
support
broader
adoption,
enhance
students’
experiences,
foster
new
generation
tech-savvy
entrepreneurs.
Journal of Computer Assisted Learning,
Journal Year:
2025,
Volume and Issue:
41(2)
Published: March 16, 2025
ABSTRACT
Background
With
the
integration
of
artificial
intelligence
into
educational
processes,
its
impact
remains
to
be
discovered.
Objective
The
aim
present
study
was
determine
whether,
after
a
7‐month
intervention
in
which
subject
taught,
students
improved
their
psychological
needs
for
competence,
autonomy
and
relatedness,
potentially
leading
an
increase
intrinsic
motivation
towards
learning.
Additionally,
examined
students'
use
ICT
influence
gender
along
intervention.
Methods
This
longitudinal
included
total
50
adolescents
from
Secondary
Education,
who
responded
series
scales
measure
main
constructs
perceived
autonomy,
relatedness
at
two
different
times
(T1
T2).
Results
results
showed
that,
regardless
frequency
academic
or
non‐academic
ICT,
statistically
significant
improvements
were
observed
only
need
relatedness.
Likewise,
analysis
structural
equation
models
revealed
that
initial
competence
(T1)
predictor
(T1),
having
this
essential
further
improving
(T2).
Similarly,
each
basic
time
point
significantly
predicted
same
final
(T2),
with
considerably
high
explained
variances.
Conclusions
These
shed
some
light
on
potential
effect
AI‐based
interventions
can
have
secondary
education
students.
Batı anadolu eğitim bilimleri dergisi,
Journal Year:
2025,
Volume and Issue:
16(1), P. 1422 - 1445
Published: April 4, 2025
Son
yıllarda
yapay
zekâ
alanında
kaydedilen
ilerlemeler,
eğitim
dâhil
olmak
üzere
birçok
sektörde
önemli
yansımalar
oluşturmuştur.
Özellikle
büyük
dil
modellerinin
içerik
üretimi,
özerk
araçlar
ve
farklı
disiplinlerdeki
rolü,
süreçlerine
de
etki
etmektedir.
Bu
çalışmanın
amacı,
öğretmenlerin
eğitimde
kullanımı
konusundaki
öz
yeterlik
inançlarını
ölçmek
amacıyla
güvenilir
geçerli
bir
ölçek
geliştirmektir.
Araştırma,
Kayseri
ili
Melikgazi
ilçesinde,
branşlarda
görev
yapan
221
öğretmenle
yürütülmüştür.
Nicel
araştırma
yaklaşımı
ile
tasarlanan
çalışma,
alanyazında
belirtilen
geliştirme
aşamalarına
dayanarak,
taslak
ölçeğin
oluşturulmasıyla
başlamıştır.
Taslak
katılımcılara
uygulandıktan
sonra,
elde
edilen
veriler
sırasıyla
açımlayıcı
doğrulayıcı
faktör
analizi
incelenmiştir.
Ölçeğin
güvenirliğini
belirlemek
Cronbach
Alfa,
Spearman
Brown
Guttman
Split
Half
iç
tutarlılık
katsayısı
hesaplanmış
sonuçlar
alanyazın
karşılaştırılmıştır.
analizler
sonucunda,
17
maddeden
üç
bileşenden
oluşan
ortaya
konmuştur.
ölçek,
konusunda
değerlendirmek
için
kaynak
teşkil
The Electronic Journal of e-Learning,
Journal Year:
2024,
Volume and Issue:
22(6), P. 18 - 33
Published: June 18, 2024
This
study
investigates
the
factors
influencing
adoption
of
Generative-AI
tools
amongst
Thai
university
students,
employing
Technology
Acceptance
Model
(TAM)
as
a
theoretical
framework.
Data
from
911
higher
education
students
10
different
Universities
Health
Sciences,
Sciences
and
Technology,
Social
Humanities,
Vocational
Fields
were
analysed
via
Structural
Equation
Modelling
(SEM).
The
instrument
used
in
collecting
data
was
questionnaire.
Results
indicated
that
Expected
Benefits,
Perceived
Usefulness,
Attitude
Toward
Behavioural
Intention
all
significantly
impacted
student
Generative
AI.
Intriguingly,
Ease
Use
negatively
correlated
with
challenging
conventional
TAM
assumptions.
underscores
need
to
address
language
barriers,
foster
culture
innovation,
establish
ethical
guidelines
promote
responsible
AI
use
within
education.
Despite
inherent
limitations,
this
research
contributes
our
understanding
educational
settings
helps
inform
strategies
for
equitable
access
innovation.
result
demonstrated
easier
tool
use,
less
value
leaners
seemed
see
it
their
learning
process.
It
can
be
implied
get
more
intuitive,
learners
think
they're
helpful.
These
finding
challenges
few
those
assumptions
we
usually
make
model.
also
points
out
characteristic
which
affects
preferences
expectation.
Another
showed
impact
barrier
on
non-native
English
speaker
obstruct
user
experience
services.
Moreover,
role
universities
fostering
both
integration
implementation
By
providing
supportive
environment
encourages
experimentation,
redesign
learning,
empowering
faculty
instructors
investigate
how
applied
across
disciplines,
developing
play
critical
shaping
effective
into
next
landscape.
