Despite
the
potential
benefits
of
generative
Artificial
Intelligence
(genAI),
concerns
about
its
psy-chological
impact
on
medical
students,
especially
with
regard
to
job
displacement,
are
apparent.
This
pilot
study,
conducted
in
Jordan
during
July–August
2024,
aimed
examine
specific
fears,
anxieties,
mistrust,
and
ethical
students
could
harbor
towards
genAI.
Using
a
cross-sectional
survey
design,
data
were
collected
from
164
studying
across
various
academic
years,
employing
structured
self-administered
questionnaire
an
internally
consistent
FAME
scale—representing
Fear,
Anxiety,
Mistrust,
Ethics
comprising
12
items,
three
items
for
each
construct.
The
results
indicated
variable
levels
anxiety
genAI
among
participating
students:
34.1%
reported
no
role
their
future
careers
(n
=
56),
while
41.5%
slightly
anxious
61),
22.0%
somewhat
36),
2.4%
extremely
4).
Among
constructs,
Mistrust
was
most
agreed
upon
(mean:
12.35±2.78),
followed
by
construct
10.86±2.90),
Fear
9.49±3.53),
Anxiety
8.91±3.68).
Sex,
level,
Grade
Point
Average
(GPA)
did
not
significantly
affect
students’
perceptions
However,
there
notable
direct
association
between
general
elevated
scores
constructs
scale.
Prior
exposure
previous
use
modify
These
findings
highlighted
critical
need
refined
educational
strategies
address
integration
training.
demonstrated
pervasive
anxiety,
fear,
regarding
deployment
healthcare,
indicating
necessity
curriculum
modifi-cations
that
focus
specifically
these
areas.
Interventions
should
be
tailored
increase
familiarity
competency,
which
would
alleviate
apprehension
equip
physicians
engage
this
inevitable
technology
effectively.
study
also
importance
incorporating
discussions
into
courses
mistrust
human-centered
aspects
Conclusively,
calls
proactive
evolution
education
prepare
AI-driven
healthcare
practices
shortly
ensure
well-prepared,
confident,
ethically
informed
professional
interactions
technologies.
International Journal of Human-Computer Interaction,
Год журнала:
2024,
Номер
unknown, С. 1 - 21
Опубликована: Март 3, 2024
The
increased
prevalence
of
human-AI
collaboration
is
reshaping
the
manufacturing
sector,
fundamentally
changing
nature
human
work
and
training
needs.
While
high
automation
improves
performance
when
functioning
correctly,
it
can
lead
to
problematic
(e.g.,
defect
detection
accuracy,
response
time)
operators
are
required
intervene
assume
manual
control
decision-making
responsibilities.
As
AI
capability
reaches
higher
levels
human–AI
becomes
ubiquitous,
addressing
these
issues
crucial.
Proper
worker
training,
focusing
on
skill-based,
cognitive,
affective
outcomes,
nurturing
motivation
engagement,
be
a
mitigation
strategy.
However,
most
research
in
has
prioritized
effectiveness
technology
for
rather
than
how
design
influences
key
success
longevity.
current
study
explored
workers
using
an
system
affected
their
motivation,
skill
acquisition.
Specifically,
we
manipulated
level
decision
selection
used
102
participants
quality
task.
Findings
indicated
that
fully
automated
negatively
impacted
perceived
autonomy,
self-determined
behavioral
task
acquisition
during
training.
Conversely,
partially
AI-enhanced
enabling
better
adapt
failure
by
developing
necessary
skills.
results
suggest
involving
as
aid
selector,
yields
more
positive
outcomes.
This
approach
ensures
aspect
not
overlooked,
maintaining
balance
between
technological
advancement
development,
engagement.
These
findings
applied
enhance
real-world
practices
designing
programs
develop
operators'
technical,
methodological,
personal
skills,
though
companies
may
face
challenges
allocating
substantial
resources
redevelopment
continuously
adapting
keep
pace
with
evolving
technology.
International Journal of Latest Technology in Engineering Management & Applied Science,
Год журнала:
2024,
Номер
XIII(II), С. 48 - 56
Опубликована: Янв. 1, 2024
This
paper
focuses
on
governance
frameworks
as
a
means
of
addressing
the
pressing
need
to
identify
ethical
challenges
surrounding
applications
artificial
intelligence
(AI)
in
making
critical
business
decisions,
help
businesses
navigate
issues
AI-driven
decision-making.
The
study
employs
qualitative
methodology
investigate
current
literature,
assess
regulatory
frameworks,
examine
case
studies
from
real
world,
and
suggest
moral
guidelines
address
dilemmas
concerns
AI
applications.Results
point
variety
problems,
including
algorithmic
biases,
data
storage
procedures,
AI-powered
decisions.
places
strong
emphasis
issues,
which
is
consistent
with
responsible
development.
Assessing
environments,
research
pinpoints
opportunities
for
enhancement
efficiency.
recommendations
emphasize
continued
significance
promote
public
awareness,
developer
accountability,
user
empowerment,
stringent
regulations.
prioritize
societal
well-being
individual
privacy
encourage
deployment
AI.Therefore,
complex
intersection
privacy,
researchers,
policymakers,
developers,
users
can
benefit
substantially
insights
provided
by
this
research.
Despite
the
potential
benefits
of
generative
Artificial
Intelligence
(genAI),
concerns
about
its
psy-chological
impact
on
medical
students,
especially
with
regard
to
job
displacement,
are
apparent.
This
pilot
study,
conducted
in
Jordan
during
July–August
2024,
aimed
examine
specific
fears,
anxieties,
mistrust,
and
ethical
students
could
harbor
towards
genAI.
Using
a
cross-sectional
survey
design,
data
were
collected
from
164
studying
across
various
academic
years,
employing
structured
self-administered
questionnaire
an
internally
consistent
FAME
scale—representing
Fear,
Anxiety,
Mistrust,
Ethics
comprising
12
items,
three
items
for
each
construct.
The
results
indicated
variable
levels
anxiety
genAI
among
participating
students:
34.1%
reported
no
role
their
future
careers
(n
=
56),
while
41.5%
slightly
anxious
61),
22.0%
somewhat
36),
2.4%
extremely
4).
Among
constructs,
Mistrust
was
most
agreed
upon
(mean:
12.35±2.78),
followed
by
construct
10.86±2.90),
Fear
9.49±3.53),
Anxiety
8.91±3.68).
Sex,
level,
Grade
Point
Average
(GPA)
did
not
significantly
affect
students’
perceptions
However,
there
notable
direct
association
between
general
elevated
scores
constructs
scale.
Prior
exposure
previous
use
modify
These
findings
highlighted
critical
need
refined
educational
strategies
address
integration
training.
demonstrated
pervasive
anxiety,
fear,
regarding
deployment
healthcare,
indicating
necessity
curriculum
modifi-cations
that
focus
specifically
these
areas.
Interventions
should
be
tailored
increase
familiarity
competency,
which
would
alleviate
apprehension
equip
physicians
engage
this
inevitable
technology
effectively.
study
also
importance
incorporating
discussions
into
courses
mistrust
human-centered
aspects
Conclusively,
calls
proactive
evolution
education
prepare
AI-driven
healthcare
practices
shortly
ensure
well-prepared,
confident,
ethically
informed
professional
interactions
technologies.
