A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Jan. 23, 2024
Abstract
Artificial
intelligence
models,
like
ChatGPT,
have
the
potential
to
revolutionize
higher
education
when
implemented
properly.
This
study
aimed
investigate
factors
influencing
university
students’
attitudes
and
usage
of
ChatGPT
in
Arab
countries.
The
survey
instrument
“TAME-ChatGPT”
was
administered
2240
participants
from
Iraq,
Kuwait,
Egypt,
Lebanon,
Jordan.
Of
those,
46.8%
heard
52.6%
used
it
before
study.
results
indicated
that
a
positive
attitude
were
determined
by
ease
use,
towards
technology,
social
influence,
perceived
usefulness,
behavioral/cognitive
influences,
low
risks,
anxiety.
Confirmatory
factor
analysis
adequacy
constructs.
Multivariate
demonstrated
significantly
influenced
country
residence,
age,
type,
recent
academic
performance.
validated
as
useful
tool
for
assessing
adoption
among
students.
successful
integration
relies
on
elements,
anxiety,
minimal
risks.
Policies
should
be
tailored
individual
contexts,
considering
variations
student
observed
this
Language: Английский
Prompt Engineering Paradigms for Medical Applications: Scoping Review
Journal of Medical Internet Research,
Journal Year:
2024,
Volume and Issue:
26, P. e60501 - e60501
Published: Sept. 10, 2024
Prompt
engineering,
focusing
on
crafting
effective
prompts
to
large
language
models
(LLMs),
has
garnered
attention
for
its
capabilities
at
harnessing
the
potential
of
LLMs.
This
is
even
more
crucial
in
medical
domain
due
specialized
terminology
and
technicity.
Clinical
natural
processing
applications
must
navigate
complex
ensure
privacy
compliance.
engineering
offers
a
novel
approach
by
designing
tailored
guide
exploiting
clinically
relevant
information
from
texts.
Despite
promise,
efficacy
prompt
remains
be
fully
explored.
Language: Английский
Knowledge graph validation by integrating LLMs and human-in-the-loop
Information Processing & Management,
Journal Year:
2025,
Volume and Issue:
62(5), P. 104145 - 104145
Published: April 9, 2025
Language: Английский
Prompt Engineering Paradigms for Medical Applications: Scoping Review (Preprint)
Published: May 14, 2024
BACKGROUND
Prompt
engineering,
focusing
on
crafting
effective
prompts
to
large
language
models
(LLMs),
has
garnered
attention
for
its
capabilities
at
harnessing
the
potential
of
LLMs.
This
is
even
more
crucial
in
medical
domain
due
specialized
terminology
and
technicity.
Clinical
natural
processing
applications
must
navigate
complex
ensure
privacy
compliance.
engineering
offers
a
novel
approach
by
designing
tailored
guide
exploiting
clinically
relevant
information
from
texts.
Despite
promise,
efficacy
prompt
remains
be
fully
explored.
OBJECTIVE
The
aim
study
review
research
efforts
technical
approaches
as
well
provide
an
overview
opportunities
challenges
clinical
practice.
METHODS
Databases
indexing
fields
medicine,
computer
science,
informatics
were
queried
order
identify
published
papers.
Since
emerging
field,
preprint
databases
also
considered.
Multiple
data
extracted,
such
paradigm,
involved
LLMs,
languages
study,
topic,
baselines,
several
learning,
design,
architecture
strategies
specific
engineering.
We
include
studies
that
apply
engineering–based
methods
domain,
between
2022
2024,
covering
multiple
paradigms
learning
(PL),
tuning
(PT),
design
(PD).
RESULTS
included
114
recent
studies.
Among
3
paradigms,
we
have
observed
PD
most
prevalent
(78
papers).
In
12
papers,
PD,
PL,
PT
terms
used
interchangeably.
While
ChatGPT
commonly
LLM,
identified
7
using
this
LLM
sensitive
set.
Chain-of-thought,
present
17
studies,
emerges
frequent
technique.
PL
papers
typically
baseline
evaluating
prompt-based
approaches,
61%
(48/78)
do
not
report
any
nonprompt-related
baseline.
Finally,
individually
examine
each
key
engineering–specific
reported
across
find
many
neglect
explicitly
mention
them,
posing
challenge
advancing
research.
CONCLUSIONS
addition
reporting
trends
scientific
landscape
guidelines
future
help
advance
field.
disclose
tables
figures
summarizing
available
hope
contributions
will
leverage
these
existing
works
better
Language: Английский
Evaluating AI Excellence: A Comparative Analysis of Generative Models in Library and Information Science
Science & Technology Libraries,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 14
Published: Oct. 7, 2024
This
study
compares
the
performance
of
GPT-3.5,
GPT-4,
Bard,
and
Gemini
in
answering
Library
Information
Science
(LIS)
questions.
Sixteen
questions
were
used
for
assessment,
with
two
independent
examiners
scoring
initial
successive
responses
from
each
AI
system.
Statistical
analyses,
including
one-way
Analysis
Variance
(ANOVA),
sample
t-test,
one-sample
employed
to
identify
differences.
The
results
revealed
consistency
generated
across
iterations
all
systems.
Significant
differences
observed
among
models,
Bard
consistently
underperforming
compared
Gemini.
uncovered
variability
examiners'
emphasized
need
multiple
evaluators
assessment.
Language: Английский