Advances in educational marketing, administration, and leadership book series,
Год журнала:
2024,
Номер
unknown, С. 175 - 198
Опубликована: Фев. 13, 2024
This
chapter
focuses
on
the
implications
of
improving
generative-AI
‘chatbot'
technologies
and
inevitable
unreliability
attendant
AI-text
detection
technologies.
The
goal
programmers
is
to
design
AIs
which
produce
text
indistinguishable
from
typical
human-written
text:
an
eventuality
that
will
render
detectors
redundant.
authors
outline
underpinning
mathematics
AI-generated
as
basis
detection,
how
this
leads
inherent
inaccuracies
uncertainties
in
detection.
proceeds
overview
institutions
have
work
with
both
growth
use
AI
detection:
cannot
avoid
rely
'tech'
police
it.
Students
need
be
taught
ethically
integrity
insight
sanctioned
when
they
do
not.
At
same
time,
resource
people
investigate
students
suspected
false
authorship,
whether
commissioning
a
human
ghost-writer
or
using
inappropriately.
2022 International Conference on Inventive Computation Technologies (ICICT),
Год журнала:
2024,
Номер
unknown
Опубликована: Апрель 24, 2024
This
research
study
explores
the
crucial
domain
of
upholding
textual
authenticity
by
introducing
a
comprehensive
method
for
identifying
AI-generated
content,
employing
BERT
(Bidirectional
Encoder
Representations
from
Transformers).
In
time
when
Artificial
Intelligence
(AI)
significantly
shapes
written
communication,
it
becomes
imperative
to
differentiate
between
text
produced
humans
and
that
generated
machines.
The
proposed
approach
utilizes
capabilities
delving
into
contextual
embedding,
revealing
complex
patterns
serve
as
indicators
AI
origin.
Through
meticulous
experimentation
evaluation,
we
substantiate
effectiveness
our
in
precisely
discerning
text.
contribution
adds
ongoing
endeavors
safeguard
integrity
human-authored
content
ever-evolving
digital
landscape.
Accountability in Research,
Год журнала:
2024,
Номер
unknown, С. 1 - 17
Опубликована: Март 22, 2024
Artificial
Intelligence
(AI)
language
models
continue
to
expand
in
both
access
and
capability.
As
these
have
evolved,
the
number
of
academic
journals
medicine
healthcare
which
explored
policies
regarding
AI-generated
text
has
increased.
The
implementation
such
requires
accurate
AI
detection
tools.
Inaccurate
detectors
risk
unnecessary
penalties
for
human
authors
and/or
may
compromise
effective
enforcement
guidelines
against
content.
Yet,
accuracy
tools
identifying
human-written
versus
content
been
found
vary
across
published
studies.
This
experimental
study
used
a
sample
behavioral
health
publications
problematic
false
positive
negative
rates
from
free
paid
assessed
100
research
articles
2016-2018
psychiatry
200
texts
produced
by
chatbots
(100
"ChatGPT"
"Claude").
detector
showed
median
27.2%
proportion
identified
as
AI-generated,
while
commercial
software
Originality.AI
demonstrated
better
performance
but
still
had
limitations,
especially
detecting
generated
Claude.
These
error
raise
doubts
about
relying
on
enforce
strict
around
generation
publications.
Naunyn-Schmiedeberg s Archives of Pharmacology,
Год журнала:
2024,
Номер
397(12), С. 9281 - 9294
Опубликована: Июль 6, 2024
Abstract
Scientific
fake
papers,
containing
manipulated
or
completely
fabricated
data,
are
a
problem
that
has
reached
dramatic
dimensions.
Companies
known
as
paper
mills
(or
more
bluntly
“criminal
science
publishing
gangs”)
produce
and
sell
such
papers
on
large
scale.
The
main
drivers
of
the
flood
pressure
in
academic
systems
(monetary)
incentives
to
publish
respected
scientific
journals
sometimes
personal
desire
for
increased
“prestige.”
Published
cause
substantial
scientific,
economic,
social
damage.
There
numerous
information
sources
deal
with
this
topic
from
different
points
view.
This
review
aims
provide
an
overview
these
until
June
2024.
Much
original
research
larger
datasets
is
needed,
example
extent
impact
especially
how
detect
them,
many
findings
based
small
datasets,
anecdotal
evidence,
assumptions.
A
long-term
solution
would
be
overcome
mantra
publication
metrics
evaluating
scientists
academia.
Journal of Chemical Education,
Год журнала:
2024,
Номер
101(7), С. 2740 - 2748
Опубликована: Июнь 17, 2024
The
effective
and
responsible
educational
application
of
ChatGPT
other
generative
artificial
intelligence
(GenAI)
tools
constitutes
an
active
area
exploration.
This
study
describes
assesses
the
implementation
a
structured,
GenAI-assisted
scientific
essay
writing
assignment
in
nucleic
acid
biochemistry.
Briefly,
students
created,
evaluated,
iteratively
refined
essays
response
to
feedback
independent
literature
research,
identifying
several
strengths
shortcomings
large
language
model
citation
practices.
scaffolded
structure
aimed
prepare
for
writing,
majority
class
cohort
ultimately
indicated
improved
understanding
GenAI
functionality
prompt
engineering,
as
well
interest
additional
usage
applications.
Moreover,
valued
instructional
guidance
on
engagement
with
engineering
opportunities
afforded
by
this
exercise.
However,
discontentment
AI-produced
citations
was
common,
26%
supporting
references
were
found
be
nonexistent.
content
evaluation
generation
strategies
uncovered
here
may
facilitate
successful
ChatGPT-guided
assignments
contexts.
Science Editing,
Год журнала:
2024,
Номер
11(2), С. 96 - 106
Опубликована: Авг. 20, 2024
While
generative
artificial
intelligence
(AI)
technology
has
become
increasingly
competitive
since
OpenAI
introduced
ChatGPT,
its
widespread
use
poses
significant
ethical
challenges
in
research.
Excessive
reliance
on
tools
like
ChatGPT
may
intensify
concerns
scholarly
articles.
Therefore,
this
article
aims
to
provide
a
comprehensive
narrative
review
of
the
issues
associated
with
using
AI
academic
writing
and
inform
researchers
current
trends.
Our
methodology
involved
detailed
examination
literature
related
research
We
conducted
searches
major
databases
identify
additional
relevant
articles
cited
literature,
from
which
we
collected
analyzed
papers.
identified
categorized
into
problems
faced
by
authors
nonacademic
platforms
detection
acceptance
AI-generated
content
reviewers
editors.
explored
eight
specific
highlighted
thorough
five
key
topics
ethics.
Given
that
often
do
not
disclose
their
training
data
sources,
there
is
substantial
risk
unattributed
plagiarism.
must
verify
accuracy
authenticity
before
incorporating
it
article,
ensuring
adherence
principles
integrity
ethics,
including
avoidance
fabrication,
falsification,
arXiv (Cornell University),
Год журнала:
2024,
Номер
11, С. 1395934 - 1395934
Опубликована: Янв. 1, 2024
ChatGPT,
the
most
accessible
generative
artificial
intelligence
(AI)
tool,
offers
considerable
potential
for
veterinary
medicine,
yet
a
dedicated
review
of
its
specific
applications
is
lacking.
This
concisely
synthesizes
latest
research
and
practical
ChatGPT
within
clinical,
educational,
domains
medicine.
It
intends
to
provide
guidance
actionable
examples
how
AI
can
be
directly
utilized
by
professionals
without
programming
background.
For
practitioners,
extract
patient
data,
generate
progress
notes,
potentially
assist
in
diagnosing
complex
cases.
Veterinary
educators
create
custom
GPTs
student
support,
while
students
utilize
exam
preparation.
aid
academic
writing
tasks
research,
but
publishers
have
set
requirements
authors
follow.
Despite
transformative
potential,
careful
use
essential
avoid
pitfalls
like
hallucination.
addresses
ethical
considerations,
provides
learning
resources,
tangible
guide
responsible
implementation.
A
table
key
takeaways
was
provided
summarize
this
review.
By
highlighting
benefits
limitations,
equips
veterinarians,
educators,
researchers
harness
power
effectively.
Heliyon,
Год журнала:
2024,
Номер
10(12), С. e32976 - e32976
Опубликована: Июнь 1, 2024
Extensive
use
of
AI-generated
texts
culminated
recently
after
the
advent
large
language
models.
Although
AI
text
generators,
such
as
ChatGPT,
is
beneficial,
it
also
threatens
academic
level
students
may
resort
to
it.
In
this
work,
we
propose
a
technique
leveraging
intrinsic
stylometric
features
documents
detect
ChatGPT-based
plagiarism.
The
were
normalized
and
fed
classical
classifiers,
k-Nearest
Neighbors,
Decision
Tree,
Naïve
Bayes,
well
ensemble
XGBoost
Stacking.
A
thorough
examination
classifier
was
conducted
by
using
Cross-Fold
validation,
hyperparameters
tuning,
multiple
training
iterations.
results
show
efficacy
both
learning
classifiers
in
distinguishing
between
human
ChatGPT
writing
styles
with
noteworthy
performance
where
100
%
achieved
for
accuracy,
recall,
precision
metrics.
Moreover,
proposed
outperformed
state-of-the-art
result
on
same
dataset
highlighting
superiority
feature
style
extraction
method
over
TF-IDF
techniques.
applied
generated
mixed
texts,
paragraphs
are
written
humans.
that
98
classified
correctly
either
or
human.
last
contribution
consists
authorship
attribution
single
document
accuracy
reached
92.3
%.
