Journal of language and cultural education,
Journal Year:
2024,
Volume and Issue:
12(3), P. 16 - 22
Published: Dec. 1, 2024
Abstract
Undergraduate
students’
attitudes
towards
Artificial
Intelligence
(AI)
in
developing
countries
like
Vietnam
are
rarely
explored
despite
AI’s
increasing
presence
higher
education.
This
study
aims
to
investigate
the
of
undergraduate
students
AI.
A
quantitative
research
method
was
used,
involving
a
self-reported
survey
questionnaire.
The
sample
consisted
460
(196
males
and
264
females)
from
five
public
private
universities
Ho
Chi
Minh
City,
Vietnam.
Data
collection
took
place
through
cross-sectional
November
December
2023.
General
Attitudes
Towards
Scale
(GAAIS),
originally
developed
validated
English
by
Schepman
Rodway
(2020),
adapted
Vietnamese
for
this
study.
scale
comprised
20
items
evaluate
analysis
included
descriptive
statistics,
Cronbach’s
alpha
coefficient,
t-tests,
one-way
Analysis
Variance
(ANOVA).
results
indicated
Alpha
value
0.705
total
variable,
demonstrating
acceptable
reliability.
Consequently,
displayed
moderately
positive
findings
also
revealed
no
significant
difference
based
on
gender,
but
there
notable
variation
student’s
year
at
university.
European Journal of Education,
Journal Year:
2025,
Volume and Issue:
60(1)
Published: Jan. 31, 2025
ABSTRACT
To
explore
the
opportunities
and
challenges
of
artificial
intelligence
(AI)
in
nursing
its
impact.
Bibliographic
review
using
Arksey
O'Malley's
framework,
enhanced
by
Levac,
Colquhoun
O'Brien
following
PRISMA
guidelines,
including
qualitative
mixed
studies.
MeSH
terms
keywords
such
as
education
ethical
considerations
were
used
databases
PubMed,
Scopus,
Web
Science,
CINAHL,
IEEE
Xplore
Google
Scholar.
Of
all,
53
studies
included,
highlighting
various
AI
integration
for
personalised
learning,
training
improvement
evaluation.
Highlighting
related
to
academic
integrity,
accuracy,
data
privacy
security,
development
critical
thinking
skills.
The
offers
significant
advantages
improving
quality
effectiveness
education,
equitable
access,
this
reason,
faculty
should
be
geared
toward
education.
Data & Metadata,
Journal Year:
2025,
Volume and Issue:
4, P. 221 - 221
Published: Jan. 13, 2025
AI
has
revolutionized
the
workplace,
significantly
impacting
nursing
profession.
Attitudes
toward
AI,
defined
as
workers’
perceptions
and
beliefs
about
its
utility
effectiveness,
are
critical
for
adoption
efficient
use
in
clinical
settings.
Factors
such
age,
marital
status,
education
level
may
influence
this
relationship,
affecting
job
performance.
This
study
examines
of
attitude
on
performance
with
among
Peruvian
nurses,
while
also
assessing
how
sociodemographic
characteristics
moderate
relationship.
A
descriptive
cross-sectional
design
was
used
a
sample
249
nurses
aged
24
to
53
years
(M
=
35.58,
SD
8.3).
Data
were
collected
using
two
validated
scales:
Brief
Artificial
Intelligence
Job
Performance
Scale
(BAIJPS)
Attitude
(AIAS-4).
Descriptive
statistics,
Pearson
correlations,
multiple
linear
regression
applied.
significant
positive
correlation
found
between
(r
0.43,
p
<
0.01).
Age
(β
-0.177,
0.05),
divorced
status
-8.144,
0.01),
having
bachelor’s
degree
-3.016,
0.05)
negatively
associated
performance,
being
from
Selva
region
had
effect
4.182,
0.05).
favorable
positively
influences
nurses’
highlighting
need
interventions
that
enhance
perception.
Age,
suggesting
strategies
should
be
tailored
different
demographic
groups.
BMC Nursing,
Journal Year:
2025,
Volume and Issue:
24(1)
Published: April 23, 2025
Minimally
invasive
cardiac
intervention
(MICI)
patients
remain
at
high
risk
of
readmission
and
mortality
during
their
post-discharge
phase,
with
30-day
rates
up
to
10%.
Although
technological
innovations,
especially
AI-driven
solutions,
hold
promise
for
improving
outcomes,
there
is
a
pressing
need
clarify
the
full
spectrum
patient
demands
transition
from
hospital
home.
This
study
aimed
systematically
identify
these
guide
development
solutions
that
reduce
improve
clinical
outcomes.
A
convergent
parallel
mixed-methods
design
was
employed
inform
interventions
in
transitional
care.
Quantitative
qualitative
data
were
collected
137
MICI
recruited
four
hospitals
(June-August
2024).
Quantitatively,
23-item
survey
analyzed
using
Kano
model,
revealing
no
"must-be"
demands-indicating
accustomed
lack
guidance
post-discharge.
However,
health
monitoring,
medication
guidance,
symptom
management,
personalized
exercise
plans
identified
as
"one-dimensional"
significantly
impact
satisfaction.
Additionally,
continuous
monitoring
dietary
planning
emerged
"attractive"
features
could
enhance
care
quality
without
negatively
affecting
satisfaction
if
absent.
Qualitative
interviews
uncovered
importance
comorbidity
psychological
support
financial
transparency,
which
not
fully
captured
data.
The
integration
findings
underscores
systems
knowledge-based
AI
tools
revolutionize
process
patients.
integrated
analysis
highlights
significant
Key
recommendations
include:
(1)
deploying
management
systems,
(2)
designing
tools,
(3)
creating
accessible,
platforms
reliable
medical
information.
In
addition,
transparency
are
areas
call
our
attention.
By
aligning
patient-centered
leveraging
AI's
capabilities,
future
interventions-particularly
China
have
potential
address
healthcare
staffing
constraints
due
limitations
study,
insights
require
further
validation
exploration.
Digital Health,
Journal Year:
2025,
Volume and Issue:
11
Published: April 1, 2025
Objective
Integrating
artificial
intelligence
(AI)
in
healthcare
presents
significant
opportunities
and
challenges
for
nurses
other
professionals.
AI
adoption
may
influence
nurses’
work
environment
overall
healthcare.
This
study
aimed
to
describe
the
level
of
knowledge,
attitudes,
practices,
barriers
among
Jordan
their
on
intent
stay
job
positions.
Methods
A
descriptive
correlational
cross-sectional
was
conducted
working
governmental
hospitals
Jordan.
Data
were
collected
using
two
validated
instruments
analyzed
statistics,
Pearson
correlation,
multivariate
regression.
Results
The
results
showed
that
mean
scores
barriers,
as
follows:
3.91
(0.67),
4.15
(0.51),
3.98
(0.56),
3.93
(0.62),
4.17
(0.49),
respectively.
While
attitudes
(
r
=
.64,
β
.34,
P
<
.001)
practices
.58,
.29,
significantly
predicted
stay,
negatively
correlated
with
it
−.42,
−.14,
.05).
Conclusion
positive
attitude
practical
engagement
Could
enhance
while
undermine
retention.
Addressing
these
factors
through
targeted
training
policy
reforms
is
crucial
nursing
workforce
stability.
