STEM Education,
Journal Year:
2024,
Volume and Issue:
5(1), P. 445 - 465
Published: Jan. 1, 2024
<p>We
verified
a
pre-service
teachers'
Extended
Technology
Acceptance
Model
(ETAM)
for
AI
application
use
in
education.
Partial
least
squares
structural
equation
modeling
(PLS-SEM)
examined
data
from
400
teachers
Central
Visayas,
Philippines.
Perceived
usefulness
and
attitudes,
ease
of
intention
to
apps
were
significantly
correlated.
However,
subjective
norms,
experience,
voluntariness
did
not
affect
how
valuable
was
viewed
or
intended
be
used.
Attitudes
toward
mediated
specific
correlations
use.
These
findings
improve
the
ETAM
model
highlight
significance
user-friendly
interfaces,
educational
activities
highlighting
AI's
benefits,
institutional
support
enhance
adoption
applications
Despite
its
limitations,
this
study
establishes
foundation
further
research
on
settings.</p>
Computers and Education Open,
Journal Year:
2024,
Volume and Issue:
6, P. 100179 - 100179
Published: April 10, 2024
In
the
context
of
global
integration
and
increasing
reliance
on
Artificial
Intelligence
(AI)
in
education,
evaluating
AI
literacy
pre-service
teachers
is
crucial.
As
future
architects
educational
systems,
must
not
only
possess
pedagogical
expertise
but
also
a
strong
foundation
literacy.
This
quantitative
study
examines
among
529
Nigerian
university,
utilizing
structural
equation
modeling
(SEM)
for
comprehensive
analysis.
The
research
explores
various
dimensions
literacy,
revealing
that
profound
understanding
significantly
predicts
positive
outcomes
use,
detection,
ethics,
creation,
problem-solving.
However,
no
correlation
exists
between
knowledge
emotion
regulation
or
assumption
active
use
enhances
detection
capabilities.
identifies
trade-off
application
emphasizing
ethical
considerations
intertwined
with
emotional
persuasive
facets
use.
It
supports
link
creation
problem-solving,
foundational
role
shaping
diverse
aspects
teachers.
findings
offer
valuable
insights
educators,
administrators,
policymakers,
researchers
aiming
to
enhance
teacher
education
programs.
Computers and Education Open,
Journal Year:
2024,
Volume and Issue:
6, P. 100177 - 100177
Published: April 10, 2024
Motivated
by
a
holistic
understanding
of
AI
literacy,
this
work
presents
an
interdisciplinary
effort
to
make
literacy
measurable
in
comprehensive
way,
considering
generic
and
domain-specific
as
well
ethics.
While
many
assessment
tools
have
been
developed
the
last
2-3
years,
mostly
form
self-assessment
scales
less
frequently
knowledge-based
assessments,
previous
approaches
only
accounted
for
one
specific
area
competence,
namely
cognitive
aspects
within
literacy.
Considering
demand
development
different
professional
domains
reflecting
on
concept
competence
way
that
goes
beyond
mere
conceptual
knowledge,
there
is
urgent
need
methods
capture
each
three
dimensions
cognition,
behavior,
attitude.
In
addition,
competencies
ethics
are
becoming
more
apparent,
which
further
calls
very
matter.
This
paper
aims
provide
foundation
upon
future
instruments
can
be
built
provides
insights
into
what
framework
item
might
look
like
addresses
both
measures
than
just
knowledge-related
based
approach.
Journal for STEM Education Research,
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 12, 2024
Abstract
Artificial
intelligence
(AI)
is
becoming
increasingly
relevant,
and
students
need
to
understand
the
concept.
To
design
an
effective
AI
program
for
schools,
we
find
ways
expose
knowledge,
provide
learning
opportunities,
create
engaging
experiences.
However,
there
a
lack
of
trained
teachers
who
can
facilitate
students’
learning,
so
focus
on
developing
capacity
pre-service
teach
AI.
Since
engagement
known
enhance
it
necessary
explore
how
engage
in
This
study
aimed
investigate
teachers’
with
after
4-week
at
university.
Thirty-five
participants
took
part
reported
their
perception
7-factor
scale.
The
factors
assessed
survey
included
(cognitive—critical
thinking
creativity,
behavioral,
social),
attitude
towards
AI,
anxiety
readiness,
self-transcendent
goals,
confidence
We
used
structural
equation
modeling
approach
test
relationships
our
hypothesized
model
using
SmartPLS
4.0.
results
supported
all
hypotheses,
attitude,
anxiety,
being
found
influence
engagement.
discuss
findings
consider
implications
practice
policy.
Education Sciences,
Journal Year:
2024,
Volume and Issue:
14(7), P. 740 - 740
Published: July 6, 2024
This
study
explores
teachers’
acceptance
of
artificial
intelligence
in
education
(AIEd)
and
its
relationship
with
various
variables
pedagogical
beliefs.
Conducted
at
the
Universidad
Técnica
Particular
de
Loja
(UTPL,
Ecuador),
research
surveyed
425
teachers
across
different
disciplines
teaching
modalities.
The
UTAUT2
model
analyzed
dimensions
like
performance
expectations,
effort
social
influence,
facilitating
conditions,
hedonic
motivation,
usage
behavior,
intention
to
use
AIEd.
Results
showed
a
high
level
among
teachers,
influenced
by
factors
age,
gender,
modality.
Additionally,
it
was
found
that
constructivist
beliefs
correlated
positively
AIEd
adoption.
These
insights
are
valuable
for
understanding
integration
educational
settings.
International Journal of Educational Technology in Higher Education,
Journal Year:
2024,
Volume and Issue:
21(1)
Published: Oct. 16, 2024
Abstract
Faculty
perspectives
on
the
use
of
artificial
intelligence
(AI)
in
higher
education
are
crucial
for
AI’s
meaningful
integration
into
teaching
and
learning,
yet
research
is
scarce.
This
paper
presents
a
study
designed
to
gain
insight
faculty
members’
(
N
=
122)
AI
self-efficacy
distinct
latent
profiles,
perceived
benefits,
challenges,
use,
professional
development
needs
related
AI.
The
respondents
saw
greater
equity
as
greatest
benefit,
while
students
lack
literacy
was
among
with
majority
interested
development.
Latent
class
analysis
revealed
four
member
profiles:
optimistic,
critical,
critically
reflected,
neutral.
optimistic
profile
moderates
relationship
between
usage.
adequate
support
services
suggested
successful
sustainable
digital
transformation.
Journal of Digital Learning in Teacher Education,
Journal Year:
2024,
Volume and Issue:
40(3), P. 159 - 172
Published: July 1, 2024
Guided
by
the
Technology
Acceptance
Model,
researchers
designed
a
Google
Form
survey
to
explore
elementary
preservice
teachers'(PSTs')
perceptions
of
using
Generative
AI
(GenAI)
as
part
an
authentic
literacy
methods
course
activity.
Following
activity,
responses
qualitative
were
analyzed
learn
about
PSTs'
experience
GenAI
in
developing
questions
for
read-aloud.
Findings
indicated
that
many
PSTs
perceived
useful
teaching
tool.
In
addition,
they
shared
their
concerns
may
limit
creativity
and
teacher
agency.
We
also
found
positive
correlation
between
use
activity
intentions
future.
The
study
adds
current
literature
TAM
with
underscores
value
promoting
critical
reasoning
among
PSTs.
Pedagogical
implications
educators
are
discussed.
Cogent Education,
Journal Year:
2025,
Volume and Issue:
12(1)
Published: Jan. 4, 2025
This
study
aims
to
reveal
the
use
of
artificial
intelligence
(AI)
in
accounting
classes,
analyze
factors
that
influence
educators
AI
continuously
learning,
and
describe
challenges
ethics
developing
AI.
The
research
population
is
(teachers
lecturers)
Indonesia
who
are
members
Professional
Alliance
Accounting
Educators
throughout
Indonesia.
sampling
method
used
was
purposive
sampling.
data
collection
a
questionnaire
distributed
online
via
Google
form
platform,
which
gathered
230
responses,
including
146
teachers
84
lecturers.
descriptive
analysis
structural
equation
model
were
data.
findings
show
Canva
most
widely
tool,
followed
by
ChatGPT.
Teachers
lecturers
primarily
create
learning
materials
write
academic
articles.
results
only
performance
expectancy
gender
significantly
impact
intention
education.
Conversely,
competence
key
affecting
actual
usage
behavior
learning.
In
addition,
various
exist
using
AI,
issues
related
effectiveness
efficiency,
IT
ethics,
fostering
student
engagement
interaction.
European Journal of Educational Research,
Journal Year:
2025,
Volume and Issue:
14(1), P. 249 - 265
Published: Jan. 8, 2025
This
study
aims
to
design,
produce,
and
validate
an
information
collection
instrument
evaluate
the
opinions
of
teachers
at
non-university
educational
levels
on
quality
training
in
artificial
intelligence
(AI)
applied
education.
The
questionnaire
was
structured
around
five
key
dimensions:
(a)
knowledge
previous
experience
AI,
(b)
perception
benefits
applications
AI
education,
(c)
training,
(d)
expectations
courses
(e)
impact
teaching
practice.
Validation
performed
through
expert
judgment,
which
ensured
internal
validity
reliability
instrument.
Statistical
analyses,
included
measures
central
tendency,
dispersion,
consistency,
yielded
a
Cronbach's
alpha
.953,
indicating
excellent
reliability.
findings
reveal
generally
positive
attitude
towards
emphasizing
its
potential
personalize
learning
improve
academic
outcomes.
However,
significant
variability
teachers'
experiences
underscores
need
for
more
standardized
programs.
validated
emerges
as
reliable
tool
future
research
perceptions
contexts.
From
practical
perspective,
provides
framework
assessing
teacher
programs
offering
valuable
insights
improving
policies
program
design.
It
enables
deeper
exploration
field
still
early
stages
implementation.
supports
development
targeted
initiatives,
fostering
effective
integration
into
practices.
