Chatbots Put to the Test in Math and Logic Problems: A Comparison and Assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard
AI,
Journal Year:
2023,
Volume and Issue:
4(4), P. 949 - 969
Published: Oct. 24, 2023
In
an
age
where
artificial
intelligence
is
reshaping
the
landscape
of
education
and
problem
solving,
our
study
unveils
secrets
behind
three
digital
wizards,
ChatGPT-3.5,
ChatGPT-4,
Google
Bard,
as
they
engage
in
a
thrilling
showdown
mathematical
logical
prowess.
We
assess
ability
chatbots
to
understand
given
problem,
employ
appropriate
algorithms
or
methods
solve
it,
generate
coherent
responses
with
correct
answers.
conducted
using
set
30
questions.
These
questions
were
carefully
crafted
be
clear,
unambiguous,
fully
described
plain
text
only.
Each
question
has
unique
well-defined
answer.
The
divided
into
two
sets
15:
Set
A
consists
“Original”
problems
that
cannot
found
online,
while
B
includes
“Published”
are
readily
available
often
their
solutions.
was
presented
each
chatbot
times
May
2023.
recorded
analyzed
responses,
highlighting
strengths
weaknesses.
Our
findings
indicate
can
provide
accurate
solutions
for
straightforward
arithmetic,
algebraic
expressions,
basic
logic
puzzles,
although
may
not
consistently
every
attempt.
However,
more
complex
advanced
tasks,
chatbots’
answers,
appear
convincing,
reliable.
Furthermore,
consistency
concern
conflicting
answers
when
same
multiple
times.
To
evaluate
compare
performance
chatbots,
we
quantitative
analysis
by
scoring
final
based
on
correctness.
results
show
ChatGPT-4
performs
better
than
ChatGPT-3.5
both
Bard
ranks
third
original
A,
trailing
other
chatbots.
achieves
best
performance,
taking
first
place
published
B.
This
likely
due
Bard’s
direct
access
internet,
unlike
ChatGPT
which,
designs,
do
have
external
communication
capabilities.
Language: Английский
AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z
Turki Alanzi,
No information about this author
Abdullah A Alsalem,
No information about this author
Hessah Alzahrani
No information about this author
et al.
Cureus,
Journal Year:
2023,
Volume and Issue:
unknown
Published: Nov. 27, 2023
Study
purpose:
This
study
aims
to
analyze
various
influencing
factors
among
generations
X
(Gen
X),
Y
Y),
and
Z
Z)
of
artificial
intelligence
(AI)-powered
mental
health
virtual
assistants.
Methods:
A
cross-sectional
survey
design
was
adopted
in
this
study.
The
sample
consisted
outpatients
diagnosed
with
illnesses,
such
as
anxiety,
depression,
schizophrenia,
behavioral
disorders.
questionnaire
designed
based
on
the
(performance
expectancy,
effort
social
influence,
facilitating
conditions,
behavioural
intention)
identified
from
unified
theory
acceptance
use
technology
model.
Ethical
approval
received
Ethics
Committee
at
Imam
Abdulrahman
Bin
Faisal
University,
Saudi
Arabia.
Results:
total
506
patients
participated
study,
over
80%
having
moderate
high
experience
using
AI
ANOVA
results
for
performance
expectancy
(PE),
(EE),
influence
(SI),
conditions
(FC),
intentions
(BI)
indicate
that
there
are
statistically
significant
differences
(p
<
0.05)
between
Gen
X,
Y,
participants.
Conclusion:
findings
underscore
significance
considering
generational
attitudes
perceptions,
demonstrating
more
positive
stronger
assistants,
while
appears
be
cautious.
Language: Английский
Overviewing Biases in Generative AI-Powered Models in the Arabic Language
Advances in computational intelligence and robotics book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 361 - 390
Published: Feb. 28, 2025
Natural
Language
Processing
(NLP)
is
an
emerging
field
often
integrated
into
Artificial
Intelligence
(AI)
technologies.
NLP
has
significantly
advanced,
leading
to
the
widespread
use
of
generative
AI-powered
(Gen-AI)
models
across
various
domains.
However,
while
Gen-AI
systems
have
been
successfully
implemented
in
several
languages,
AI-based
language
still
face
considerable
challenges
and
shortcomings,
including
generating
biases
sensitive
languages
like
Arabic.
Therefore,
primary
objective
this
chapter
provide
overview
Gen-AI-powered
context
Arabic
language,
exploring
sources
these
biases,
their
implications,
potential
strategies
for
mitigation.
The
underscore
need
ongoing
research
development
create
more
equitable
accurate
AI
systems.
By
understanding
origins
implications
implementing
effective
mitigation
strategies,
we
can
work
towards
that
better
serve
diverse
linguistic
communities.
Language: Английский
Understanding question-answering systems: Evolution, applications, trends, and challenges
Amer Farea,
No information about this author
Frank Emmert‐Streib
No information about this author
Engineering Applications of Artificial Intelligence,
Journal Year:
2025,
Volume and Issue:
156, P. 110997 - 110997
Published: May 22, 2025
Language: Английский
An inquiry smart chatbot system for Al-Zaytoonah University of Jordan
Bulletin of Electrical Engineering and Informatics,
Journal Year:
2024,
Volume and Issue:
13(4), P. 2758 - 2773
Published: June 1, 2024
Chatbots
are
important
in
artificial
intelligence
(AI)
and
natural
language
processing
(NLP).
The
development
of
the
chatbot
is
viewed
as
a
continuous
issue
field.
This
suitable
for
Arabic
chatbots
that
not
widely
available.
study
aims
to
fill
gap
by
creating
an
system
university
admissions.
uses
deep
neural
network
model
manually
constructed
dataset
conversation
pairings,
utilizing
Jordanian
dialect
from
Al-Zaytoonah
University
Jordan’s
(ZUJ)
website.
efficiently
answers
most
user
queries,
improving
counseling
experience
reducing
workload
admissions
department.
adoption
this
also
minimizes
website
traffic
congestion.
contributes
improvement
technology
learning-based
optimized
admissions,
demonstrating
its
potential
impact
Arabic-speaking
context.
Future
research
can
further
enhance
system’s
capabilities
applicability
other
disciplines.
Language: Английский
The Evolution of Transformers in Education: A Literature Review
Chaimaa Bouafoud,
No information about this author
Khalid Zine-Dine,
No information about this author
Abdellah Madani
No information about this author
et al.
Published: June 28, 2024
Language: Английский
Deep Learning in Written Arabic Linguistic Studies: A Comprehensive Survey
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 172196 - 172233
Published: Jan. 1, 2024
Language: Английский
PixieGPT: Design and Implementation of a Generative Pre-Trained Transformer for Universities of Bangladesh
Hasan Islam,
No information about this author
Mehedi Hasan,
No information about this author
Sumiaya Ahmed
No information about this author
et al.
Published: Feb. 20, 2024
In
a
densely
populated
country
like
Bangladesh,
universities
grapple
with
the
challenge
of
efficiently
addressing
myriad
queries
from
large
student
body,
leading
to
heightened
workload
for
university
stakeholders.
To
tackle
these
challenges,
we
introduce
PixieGPT,
tailor-made
Generative
Pre-Trained
Transformer
Bangladeshi
universities.
PixieGPT
significantly
mitigates
by
adeptly
handling
common
university-related
queries,
thereby
enhancing
user
experience.
The
hierarchical
structure
plays
crucial
role
in
managing
diverse
thousands
students
about
system.
solution
introduces
modular
knowledge
base
(KB)
simpler
complexities,
intricacies
volumes
queries.
is
designed
way
so
that
also
adaptable
implementation
other
worldwide
based
on
requirements
particular
administrative
nature
facilitates
easy
adaptation
minor
changes
specific
requirements,
ensuring
seamless
integration
process.
This
paper
delves
into
PixieGPT's
design,
emphasizing
its
pivotal
mitigating
challenges
stakeholders
Bangladesh.
incorporation
BERT
Natural
Language
Understanding(NLU)
and
GPT
models
Generation(NLG)
enhances
capabilities,
contributing
scalability
efficiency
presented
use
case
underscores
practical
benefits
positioning
it
as
promising
globally
similar
operational
frameworks.
Language: Английский
Pixiegpt: Design and Implementation of a Generative Pre-Trained Transformer for Universities of Bangladesh
Hasan Islam,
No information about this author
Mehedi Hasan,
No information about this author
Sumiaya Ahmed
No information about this author
et al.
Published: Jan. 1, 2024
Language: Английский
Transformative Conversational AI: Sentiment Recognition in Chatbots via Transformers
Sadam Hussain Noorani,
No information about this author
Sheharyar Khan,
No information about this author
Awais Mahmood
No information about this author
et al.
Published: Nov. 17, 2023
Recent
years
have
seen
a
dramatic
increase
in
the
use
of
conversational
artificial
intelligence
(CAI)
for
both
academic
and
commercial
applications,
primarily
context
chatbots
AI
virtual
assistants.
The
user's
engagement
produces
human
like
responses.
However,
capacity
to
discern
sentiments
respond
adequately
is
one
major
difficulties
faced
by
conversation
systems.
In
present
study,
we
propose
transformer-based
framework
sentiment-aware
chatbot.
suggested
transformer
neural
network
architecture
that
highly
parallelizable
solely
dependent
on
self-attention
mechanism.
A
model
controls
variable-sized
input
using
stacks
layers
rather
than
deep
networks
or
CNNs.
this
manner,
language
creation
carried
out
cutting-edge
pre-trained
CTRL,
which
can
easily
adapt
various
models
without
needing
architectural
adaptations.
Our
was
trained
DailyDialogues
dataset
evaluated
automated
metrics.
Findings
from
experiments
confirm
that,
terms
content
quality
emotion
perception,
our
technique
works
better
baselines.
Language: Английский