Advances in educational technologies and instructional design book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 101 - 130
Published: Sept. 27, 2024
This
chapter
explored
the
potential
of
generative
AI
in
context
geoinformatics
training.
Generative
techniques
can
generate
realistic
synthetic
data
to
support
tasks
like
land
cover
classification
and
object
detection.
Moreover,
AI-generated
datasets
help
students
develop
skills
remote
sensing,
GIS,
spatial
analysis
without
limitations
real-world
data.
Interactive
simulations
provide
immersive
learning
for
disaster
management
urban
planning,
despite
requiring
significant
resources.
Additionally,
AI-generated,
diverse
geospatial
analytics
Customizable
examples
improve
outcomes,
while
instructional
content
boost
educational
resource
quality.
The
also
included
demonstration
how
be
used
course
material
preparation
imparting
training
undergraduate
students.
Advances in public policy and administration (APPA) book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 195 - 212
Published: Jan. 17, 2025
Deepfake
technology,
a
form
of
Generative
Artificial
Intelligence
(Gen-AI),
allows
for
the
manipulation
individuals'
voices
and
images
to
generate
fake
videos
where
people
appear
be
saying
or
doing
things
they
never
actually
said
did.
This
has
led
concerns
about
copyright
infringement
other
rights
violations,
though
this
study
specifically
focuses
on
issues.
Dealing
with
these
violations
global
scale
presents
significant
urgent
challenges.
However,
necessity
providing
fair
compensation
creators
using
their
work
is
not
just
step,
but
fundamental
requirement
towards
allowing
legal
use
deepfake
technology.
As
solution,
suggests
remuneration
model
address
infringements
related
deepfakes.
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 89 - 122
Published: Feb. 28, 2025
The
chapter
integrates
traditional
decision-making
models
with
selecting
and
implementing
Artificial
Intelligence
(AI)
technologies,
specifically
on
Generative
AI,
Machine
Learning,
Deep
Learning.
It
provides
a
comprehensive
analysis
of
eight
widely
used
frameworks—SWOT
Analysis,
Resource-Based
View,
Core
Competencies,
Porter's
Five
Forces,
Ansoff
Matrix,
PESTLE
6
Thinking
Hats,
OODA
Loop—and
evaluates
their
relevance
adaptability
in
guiding
senior
executives
through
the
complexities
AI
adoption.
emphasizes
that
integrating
technologies
is
not
merely
technical
challenge
but
strategic
endeavor
must
align
organizational
objectives
to
achieve
sustainable
competitive
advantage,
recommending
nuanced
approach
may
require
synchronization
multiple
address
multifaceted
nature
integration.
gives
knowledge
necessary
leverage
effectively,
ensuring
investments
contribute
long-term
success.
Processes,
Journal Year:
2025,
Volume and Issue:
13(5), P. 1413 - 1413
Published: May 6, 2025
The
cornerstone
of
the
global
economy,
oil
and
gas
reservoir
development,
faces
numerous
challenges
such
as
resource
depletion,
operational
inefficiencies,
safety
concerns,
environmental
impacts.
In
recent
years,
integration
artificial
intelligence
(AI),
particularly
general
(AGI),
has
gained
significant
attention
for
its
potential
to
address
these
challenges.
This
review
explores
current
state
AGI
applications
in
sector,
focusing
on
key
areas
data
analysis,
optimized
decision
knowledge
management,
etc.
AGIs,
leveraging
vast
datasets
advanced
retrieval-augmented
generation
(RAG)
capabilities,
have
demonstrated
remarkable
success
automating
data-driven
decision-making
processes,
enhancing
predictive
analytics,
optimizing
workflows.
exploration,
AGIs
assist
interpreting
seismic
geophysical
surveys,
providing
insights
into
subsurface
reservoirs
with
higher
accuracy.
During
production,
enable
real-time
analysis
data,
predicting
equipment
failures,
drilling
parameters,
increasing
production
efficiency.
Despite
promising
applications,
several
remain,
including
quality,
model
interpretability,
need
high-performance
computing
resources.
paper
also
discusses
future
prospects
highlighting
multi-modal
AI
systems,
which
combine
textual,
numerical,
visual
further
enhance
processes.
conclusion,
revolutionize
development
by
driving
automation,
efficiency,
improving
safety.
However,
overcoming
existing
technical
organizational
will
be
essential
realizing
full
this
sector.
Journal of Computer-Mediated Communication,
Journal Year:
2024,
Volume and Issue:
29(6)
Published: Sept. 25, 2024
Abstract
Human
assumption
of
superior
performance
by
machines
has
a
long
history,
resulting
in
the
concept
“machine
heuristic”
(MH),
which
is
mental
shortcut
that
individuals
apply
to
automated
systems.
This
article
provides
formal
explication
this
and
develops
new
scale
based
on
three
studies
(Combined
N
=
1129).
Measurement
items
were
derived
from
an
open-ended
survey
(Study
1,
270).
These
then
administered
closed-ended
2,
448)
identify
their
dimensionality
through
exploratory
factor
analysis
(EFA).
Lastly,
we
conducted
another
3,
411)
verify
structure
obtained
Study
2
employing
confirmatory
(CFA).
Analyses
resulted
validated
seven
reflect
level
MH
identified
six
sets
descriptive
labels
for
(expert,
efficient,
rigid,
superfluous,
fair,
complex)
serve
as
formative
indicators
MH.
Theoretical
practical
implications
are
discussed.
Contemporary Educational Technology,
Journal Year:
2024,
Volume and Issue:
16(4), P. ep536 - ep536
Published: Oct. 22, 2024
<b>Purpose:</b>
This
study
aims
to
provide
an
analysis
of
students’
perceptions
the
role
generative
artificial
intelligence
(GenAI)
tools
in
education,
through
five
axes:
(1)
level
knowledge
and
awareness,
(2)
acceptance
readiness,
(3)
GenAI
(4
(level
awareness
potential
concerns
challenges,
(5)
The
impact
on
achieving
sustainable
development
goals
education.<br
/>
<b>Materials
methods:</b>
followed
a
descriptive
quantitative
methodology
based
surveying
questionnaire.
sample
consisted
1390
students
from
15
Saudi
universities.<br
<b>Results:</b>
have
positive
towards
as
high
adopting
these
tools.
In
addition,
are
highly
aware
improving
their
understanding
complex
concepts,
developing
skills,
self-efficacy,
learning
outcomes,
providing
feedback,
making
meaningful.
results
also
confirm
general
challenges.
A
relationship
exists
between
scientific
specializations,
computer
sciences
showed
greater
regarding
whereas
agricultural
goals.<br
<b>Conclusions:</b>
offers
valuable
insights
adoption
higher
there
is
urgent
need
consider
appropriate
use
policies,
spreading
creating
systems
capable
detecting
unethical
cases.
Asian Journal of Logistics Management,
Journal Year:
2024,
Volume and Issue:
3(2), P. 104 - 125
Published: Nov. 3, 2024
This
paper
examines
the
use
of
generative
AI
in
human
resource
management
(HRM),
emphasizing
improvement
operational
efficiency
and
decision-making
processes.
The
study
used
a
literature
based
approach,
combining
information
from
peer
reviewed
journals,
books,
research
articles
industry
reports
to
examine
adoption
into
HR
tasks,
such
as
recruiting,
employee
engagement,
performance
management.
demonstrates
that
significantly
enhances
recruiting
by
decreasing
time
hire
more
precisely
matching
applicants
with
job
specifications.
Moreover,
AI-driven
technologies
strengthen
engagement
personalizing
interactions
automating
routine
enabling
professionals
concentrate
on
key
objectives.The
study's
uniqueness
is
its
thorough
assessment
ethical
dilemmas
challenges
related
AI,
including
algorithmic
bias
privacy
issues.
To
address
these
dangers,
emphasizes
need
include
justice
openness
deployment.
results
indicate
while
has
potential
for
significant
improvements,
governance
essential
appropriate
use.For
strategic
workforce
management,
managers
must
also
being
aware
constraints.
However,
there
are
certain
limitations,
relying
solely
current
biases
inherent
sources.
Subsequent
needs
empirical
validation
formulation
frameworks
direct
implementation
resources.
offers
comprehensive
view
advantages
obstacles
associated
integration
HRM,
highlighting
responsible
balanced
implementation.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(22), P. 9963 - 9963
Published: Nov. 15, 2024
Generative
AI
techniques,
such
as
Adversarial
Networks
(GANs),
Variational
Autoencoders
(VAEs),
and
transformers,
have
revolutionized
consumer
behavior
prediction
by
enabling
the
synthesis
of
realistic
data
extracting
meaningful
insights
from
large,
unstructured
datasets.
However,
despite
their
potential,
effectiveness
these
models
in
practical
applications
remains
inadequately
addressed
existing
literature.
This
study
aims
to
investigate
how
generative
can
effectively
enhance
implications
for
real-world
marketing
customer
engagement.
By
systematically
reviewing
31
studies
focused
on
e-commerce,
energy
modeling,
public
health,
we
identify
contributions
improving
personalized
marketing,
inventory
management,
retention.
Specifically,
transformer
excel
at
processing
complicated
sequential
real-time
insights,
while
GANs
VAEs
are
effective
generating
predicting
behaviors
churn
purchasing
intent.
Additionally,
this
review
highlights
significant
challenges,
including
privacy
concerns,
integration
computing
resources,
limited
applicability
scenarios.
