Extending the Technology Acceptance Model: The Role of Subjective Norms, Ethics, and Trust in AI Tool Adoption Among Students
Computers and Education Artificial Intelligence,
Год журнала:
2025,
Номер
unknown, С. 100379 - 100379
Опубликована: Фев. 1, 2025
Язык: Английский
Latent profiles of AI learning conditions among university students: Implications for educational intentions
Contemporary Educational Technology,
Год журнала:
2025,
Номер
17(2), С. ep565 - ep565
Опубликована: Янв. 30, 2025
This
investigation
aimed
to
ascertain
latent
profiles
of
university
students
predicated
on
fundamental
factors
influencing
their
intentions
acquire
knowledge
in
artificial
intelligence
(AI).
The
study
scrutinized
four
dimensions:
supportive
social
norms,
facilitating
conditions,
self-efficacy
AI
learning,
and
perceived
utility
AI.
Through
the
utilization
profile
analysis
(LPA),
endeavored
unveil
distinct
subgroups
delineated
by
unique
amalgamations
these
factors.
was
carried
out
with
a
cohort
391
from
diverse
academic
disciplines.
LPA
disclosed
five
students:
Cautious
Participants,
Enthusiastic
Advocates,
Reserved
Skeptics,
Pragmatic
Acceptors,
Disengaged
Critics.
These
categories
showed
somewhat
different
goals
learn
AI;
Advocates
highest
intention
while
Critics
lowest.
findings
enhance
growing
corpus
research
education
higher
providing
sophisticated
variation
among
about
attitudes
preparedness
Subgroups
show
that
learners
need
educational
strategies
interventions
meet
needs
attitudes.
is
changing
many
fields,
therefore
college
must
it
prepare
for
it.
advance
impact
curriculum
policy.
Язык: Английский
What Influences College Students Using AI for Academic Writing? - A Quantitative Analysis Based on HISAM and TRI Theory
Computers and Education Artificial Intelligence,
Год журнала:
2025,
Номер
unknown, С. 100391 - 100391
Опубликована: Март 1, 2025
Язык: Английский
The Impact of Artificial Intelligence on Personalized Learning in Higher Education: A Systematic Review
Trends in Higher Education,
Год журнала:
2025,
Номер
4(2), С. 17 - 17
Опубликована: Март 26, 2025
The
integration
of
artificial
intelligence
in
education
has
the
potential
to
revolutionize
personalized
learning
by
adapting
instructional
methods,
content,
and
pace
individual
needs
students.
This
systematic
review
investigates
into
within
higher
education.
An
extensive
literature
search
was
conducted
across
multiple
databases,
yielding
17,899
records
from
which
45
studies
met
inclusion
criteria.
risk
bias
assessed
using
a
standardized
ranking
system.
follows
PRISMA
guidelines
ensure
transparency
study
selection,
data
extraction,
synthesis.
findings
are
synthesized
examine
how
AI-driven
solutions
enhance
adaptive
learning,
improve
student
engagement,
streamline
administrative
processes.
results
indicate
that
AI
technologies
can
significantly
optimize
educational
outcomes
tailoring
content
feedback
learner
needs.
However,
several
challenges
persist,
such
as
ethical
concerns,
privacy
issues,
necessity
for
effective
teacher
training
support
technology
integration.
analysis
reveals
considerable
transform
practices,
while
also
emphasizing
importance
establishing
evaluation
frameworks
conducting
longitudinal
studies.
implications
these
critical
educators,
policymakers,
university
administrators
aiming
leverage
innovation
sustainable
transformation.
Язык: Английский
A cross-country analysis of self-determination and continuance use intention of AI tools in business education: Does instructor support matter?
Computers and Education Artificial Intelligence,
Год журнала:
2025,
Номер
unknown, С. 100402 - 100402
Опубликована: Апрель 1, 2025
Язык: Английский
Integrating Generative AI in Contemporary Research Writing: Exploring Postgraduates’ Knowledge and Willingness to Use GenAI in the Upper East Region of Ghana
American Journal of Technology,
Год журнала:
2024,
Номер
3(1), С. 33 - 51
Опубликована: Дек. 14, 2024
Aim:
In
the
21st
century
era
where
academic
research
boundaries
are
increasingly
blurred
by
technological
innovations,
it
is
critical
to
understand
postgraduate
students'
willingness
embrace
Generative
AI
as
a
ally
reshape
their
scholarly
work
and
lift
writing
experience
newer
heights.
This
study
assessed
knowledge
use
(GenAI)
tool.
Methods:
was
quantitative
cross-sectional
survey
design
employed
collect
primary
data
from
sample
of
238
selected
an
accessible
population
588.
The
members
were
considered
using
convenient
sampling
method.
Data
collected
through
structured
closed-ended
questionnaire
instruments
which
self-designed
piloted.
questionnaires'
Cronbach's
reliability
coefficient
0.
913:
value
reflects
good
internal
consistency,
well
serves
show
robustness
instrument
measure
objectives
this
study.
used
period
3
weeks
collect,
clean,
analyze
descriptive
statistics
simple
linear
regression
analysis
(SPSS
20.0).
Results:
showed
that
postgraduates
have
moderate
high
level
(M
3.189
-
3.706)
GenAI
2.966
3.349).
A
significantly
predicted
for
purposes,
(r²
=.
023,
B
=
0.12,
p
0.021).
Recommendation:
Educational
institutions
should
implement
targeted
training
programs
on
tools
enhance
students’
competency
confidence
in
these
research.
Язык: Английский