Education Sciences,
Год журнала:
2024,
Номер
14(12), С. 1363 - 1363
Опубликована: Дек. 12, 2024
Student
gender
differences
in
technology
acceptance
and
use
have
persisted
for
years,
giving
rise
to
equity
concerns
higher
education
(HE).
To
explore
if
such
extend
generative
artificial
intelligence
(genAI)
chatbot
use,
we
surveyed
a
large
Norwegian
HE
student
sample
(n
=
2692)
using
fully
mixed
concurrent
equal
status
design.
Our
findings
show
that
men
exhibit
more
frequent
engagement
with
genAI
chatbots
across
broader
spectrum
of
applications.
Further,
demonstrate
heightened
interest
as
tools
their
relevance
future
career
prospects.
Women
primarily
utilize
text-related
tasks
express
greater
regarding
critical
independent
thinking.
also
stronger
need
learn
how
determine
when
it
is
wise
trust
chatbots.
Consequences
are
discussed
the
individual,
society,
institutions
terms
social
reproduction,
diversity
competence,
equitable
teaching
practices.
F1000Research,
Год журнала:
2025,
Номер
14, С. 258 - 258
Опубликована: Март 4, 2025
Background
The
rapid
integration
of
Artificial
Intelligence
(AI)
in
education
offers
transformative
opportunities
to
enhance
teaching
and
learning.
Among
these
innovations,
Large
Language
Models
(LLMs)
like
ChatGPT
hold
immense
potential
for
instructional
design,
personalized
learning,
administrative
efficiency.
However,
integrating
tools
into
resource-constrained
settings
such
as
Nigeria
presents
significant
challenges,
including
inadequate
infrastructure,
digital
inequities,
teacher
readiness.
Despite
the
growing
research
on
AI
adoption,
limited
studies
focus
developing
regions,
leaving
a
critical
gap
understanding
how
educators
perceive
adopt
technologies.
Methods
We
adopted
hybrid
approach,
combining
Partial
Least
Squares
Structural
Equation
Modelling
(PLS-SEM)
Neural
Networks
(ANN)
uncover
both
linear
nonlinear
dynamics
influencing
behavioral
intention
(BI)
260
Nigerian
in-service
teachers
regarding
after
participating
structured
training.
Key
predictors
examined
include
Perceived
Ease
Use
(PEU),
Usefulness
(PUC),
Attitude
Towards
(ATC),
Your
Colleagues
(YCC),
Technology
Anxiety
(TA),
Teachers’
Trust
(TTC),
Privacy
Issues
(PIU).
Results
Our
PLS-SEM
results
highlight
PUC,
TA,
YCC,
PEU,
that
order
importance,
predictors,
explaining
15.8%
variance
BI.
Complementing
these,
ANN
analysis
identified
ATC,
PUC
most
factors,
demonstrating
substantial
predictive
accuracy
with
an
RMSE
0.87.
This
suggests
while
drives
PEU
positive
attitudes
are
foundational
fostering
engagement
Conclusion
need
targeted
professional
development
initiatives
teachers’
competencies,
reduce
technology-related
anxiety,
build
trust
ChatGPT.
study
actionable
insights
policymakers
educational
stakeholders,
emphasizing
importance
inclusive
ethical
ecosystem.
aim
empower
support
AI-driven
transformation
resource-limited
environments
by
addressing
contextual
barriers.
Journal of New Approaches in Educational Research,
Год журнала:
2025,
Номер
14(1)
Опубликована: Март 17, 2025
Abstract
This
study
investigates
the
configurational
impact
of
generative
artificial
intelligence
(GenAI)
tools
on
self-regulated
learning
(SRL)
across
various
educational
levels
using
a
28-week
fuzzy-set
qualitative
comparative
analysis
(fsQCA)
approach.
The
research
explores
how
factors
such
as
technological
proficiency,
user
engagement,
skills,
and
feedback
quality
interact
with
functionalities
GenAI
to
enhance
SRL
capacities.
Data
were
collected
through
semi-structured
surveys
assessments
from
diverse
sample
undergraduate
postgraduate
students.
findings
reveal
that
synergistic
relationship
between
learner
characteristics
tool
affordances
significantly
boosts
skills.
Key
configurations
identified
include
critical
role
high-quality
functionalities,
importance
positive
attitudes
moderating
effect
interface
experience.
underscores
necessity
tailoring
meet
individual
needs
highlights
potential
these
technologies
create
adaptive,
personalized
environments.
results
advocate
for
strategic
integration
in
practices
support
pathways,
contributing
global
discourse
digital
pedagogy
enhancement
learning.
