Empathetic
and
coherent
responses
are
critical
in
automated
chatbot-facilitated
psychotherapy.
This
study
addresses
the
challenge
of
enhancing
emotional
contextual
understanding
large
language
models
(LLMs)
psychiatric
applications.
We
introduce
Emotion-Aware
Embedding
Fusion,
a
novel
framework
integrating
hierarchical
fusion
attention
mechanisms
to
prioritize
semantic
features
therapy
transcripts.
Our
approach
combines
multiple
emotion
lexicons,
including
NRC
Emotion
Lexicon,
VADER,
WordNet,
SentiWordNet,
with
state-of-the-art
LLMs
such
as
Flan-T5,
Llama
2,
DeepSeek-R1,
ChatGPT
4.
Therapy
session
transcripts,
comprising
over
2000
samples,
segmented
into
levels
(word,
sentence,
session)
using
neural
networks,
while
these
pooling
techniques
refine
representations.
Attention
mechanisms,
multi-head
self-attention
cross-attention,
further
features,
enabling
temporal
modeling
shifts
across
sessions.
The
processed
embeddings,
computed
BERT,
GPT-3,
RoBERTa,
stored
Facebook
AI
similarity
search
vector
database,
which
enables
efficient
clustering
dense
spaces.
Upon
user
queries,
relevant
segments
retrieved
provided
context
LLMs,
their
ability
generate
empathetic
contextually
responses.
proposed
is
evaluated
practical
use
cases
demonstrate
real-world
applicability,
AI-driven
chatbots.
system
can
be
integrated
existing
mental
health
platforms
personalized
based
on
data.
experimental
results
show
that
our
enhances
empathy,
coherence,
informativeness,
fluency,
surpassing
baseline
improving
LLMs’
intelligence
adaptability
for
Sustainability,
Год журнала:
2023,
Номер
15(17), С. 12983 - 12983
Опубликована: Авг. 29, 2023
In
the
ever-evolving
era
of
technological
advancements,
generative
artificial
intelligence
(GAI)
emerges
as
a
transformative
force,
revolutionizing
education.
This
review
paper,
guided
by
PRISMA
framework,
presents
comprehensive
analysis
GAI
in
education,
synthesizing
key
insights
from
selection
207
research
papers
to
identify
gaps
and
future
directions
field.
study
begins
with
content
that
explores
GAI’s
impact
specific
educational
domains,
including
medical
education
engineering
The
versatile
applications
encompass
assessment,
personalized
learning
support,
intelligent
tutoring
systems.
Ethical
considerations,
interdisciplinary
collaboration,
responsible
technology
use
are
highlighted,
emphasizing
need
for
transparent
models
addressing
biases.
Subsequently,
bibliometric
is
conducted,
examining
prominent
AI
tools,
focus,
geographic
distribution,
collaboration.
ChatGPT
dominant
tool,
reveals
significant
exponential
growth
2023.
Moreover,
this
paper
identifies
promising
directions,
such
GAI-enhanced
curriculum
design
longitudinal
studies
tracking
its
long-term
on
outcomes.
These
findings
provide
understanding
potential
reshaping
offer
valuable
researchers,
educators,
policymakers
interested
intersection
IEEE Access,
Год журнала:
2024,
Номер
12, С. 26839 - 26874
Опубликована: Янв. 1, 2024
Large
Language
Models
(LLMs)
recently
demonstrated
extraordinary
capability,
including
natural
language
processing
(NLP),
translation,
text
generation,
question
answering,
etc.
Moreover,
LLMs
are
a
new
and
essential
part
of
computerized
processing,
having
the
ability
to
understand
complex
verbal
patterns
generate
coherent
appropriate
replies
for
situation.
Though
this
success
has
prompted
substantial
increase
in
research
contributions,
rapid
growth
made
it
difficult
overall
impact
these
improvements.
Since
lot
on
is
coming
out
quickly,
getting
tough
get
an
overview
all
them
short
note.
Consequently,
community
would
benefit
from
but
thorough
review
recent
changes
area.
This
article
thoroughly
overviews
LLMs,
their
history,
architectures,
transformers,
resources,
training
methods,
applications,
impacts,
challenges,
paper
begins
by
discussing
fundamental
concepts
with
its
traditional
pipeline
phase.
It
then
provides
existing
works,
history
evolution
over
time,
architecture
transformers
different
resources
methods
that
have
been
used
train
them.
also
datasets
utilized
studies.
After
that,
discusses
wide
range
applications
biomedical
healthcare,
education,
social,
business,
agriculture.
illustrates
how
create
society
shape
future
AI
they
can
be
solve
real-world
problems.
Then
explores
open
issues
challenges
deploying
scenario.
Our
aims
help
practitioners,
researchers,
experts
pre-trained
goals.
IEEE Transactions on Learning Technologies,
Год журнала:
2023,
Номер
17, С. 629 - 641
Опубликована: Окт. 16, 2023
This
research
project
investigates
the
impact
of
prompt
engineering,
a
key
aspect
chat
generative
pretrained
transformer
(ChatGPT),
on
college
students'
information
retrieval
in
flipped
classrooms.
In
recent
years,
an
increasing
number
students
have
been
using
AI-based
tools,
such
as
ChatGPT
rather
than
traditional
engines
to
learn
and
complete
course
assignments.
Despite
this
growing
trend,
previous
has
largely
overlooked
influence
engineering
use
effective
strategies
for
improving
quality
learning
settings.
To
address
gap,
study
examines
obtained
from
classroom
by
evaluating
its
effectiveness
task
completion
among
26
novice
undergraduates
same
major
cohort.
The
experimental
results
provide
evidence
that
proficient
mastery
improves
ChatGPT.
Consequently,
acquiring
proficiency
can
maximize
positive
ChatGPT,
obtain
high-quality
information,
enhance
their
efficiency
Computers and Education Artificial Intelligence,
Год журнала:
2024,
Номер
6, С. 100219 - 100219
Опубликована: Апрель 3, 2024
This
study
investigates
the
impact
of
activity-based
learning
and
utilization
ChatGPT
on
students'
academic
performance
within
educational
framework.
The
aims
to
assess
effectiveness
in
comparison
traditional
methods,
while
also
evaluating
potential
benefits
drawbacks
integrating
as
an
tool.
employs
a
comparative
approach,
analyzing
outcomes
students
exposed
versus
those
using
conventional
methods.
Additionally,
examines
usage
education
through
surveys
trials
determine
its
contribution
personalized
feedback,
interactive
learning,
innovative
teaching
findings
reveal
that
enhances
engagement,
motivation,
critical
thinking
skills.
Students
participating
demonstrate
improved
achievement,
which
is
attributed
their
active
involvement
practical
application
knowledge.
Similarly,
integration
offers
novel
avenues
for
individualized
assistance,
fostering
understanding
exploration
complex
concepts.
In
conclusion,
proves
be
student-centered
approach
by
participation
engagement.
showcases
enhance
experiences
conversations
methodologies,
despite
considerations
regarding
limitations
ethical
implications.
Journal of Intelligent Communication,
Год журнала:
2024,
Номер
4(1)
Опубликована: Апрель 26, 2024
The
emergence
of
generative
artificial
intelligence
(AI)
technologies,
such
as
large
language
models
(LLMs)
like
ChatGPT,
has
precipitated
a
paradigm
shift
in
the
realms
academic
writing,
plagiarism,
and
intellectual
property.
This
article
explores
evolving
landscape
English
composition
courses,
traditionally
designed
to
develop
critical
thinking
through
writing.
As
AI
becomes
increasingly
integrated
into
sphere,
it
necessitates
reevaluation
originality
purpose
learning
research
frameworks
governing
property
(IP)
plagiarism.
paper
commences
with
statistical
analysis
contrasting
actual
use
LLMs
dishonesty
educator
perceptions.
It
then
examines
repercussions
AI-enabled
content
proliferation,
referencing
limitation
three
books
self-published
per
day
September
2023
by
Amazon
due
suspected
influx
AI-generated
material.
discourse
extends
potential
accelerating
akin
contributions
digital
humanities
computational
linguistics,
highlighting
its
accessibility
general
public.
further
delves
implications
on
pedagogical
approaches
contemplating
impact
communication
skills,
while
also
considering
role
bridging
divide
socio-economic
disparities.
Finally,
proposes
revisions
writing
curricula,
adapting
transformative
influence
contexts.
IEEE Communications Magazine,
Год журнала:
2024,
Номер
62(11), С. 84 - 90
Опубликована: Янв. 8, 2024
The
evolution
of
generative
artificial
intelligence
(GenAI)
constitutes
a
turning
point
in
reshaping
the
future
technology
different
aspects.
Wireless
networks
particular,
with
blooming
self-evolving
networks,
represent
rich
field
for
exploiting
GenAI
and
reaping
several
benefits
that
can
fundamentally
change
way
how
wireless
are
designed
operated
nowadays.
To
be
specific,
large
models
envisioned
to
open
up
new
era
autonomous
which
multi-modal
trained
over
various
Telecom
data,
fine-tuned
perform
downstream
tasks,
eliminating
need
building
training
dedicated
AI
each
specific
task
paving
realization
general
(AGI)-
empowered
networks.
In
this
article,
we
aim
unfold
opportunities
reaped
from
integrating
into
domain.
first
highlight
applications
defining
potential
use-cases
revealing
insights
on
associated
theoretical
practical
challenges.
Furthermore,
unveil
6G
through
connecting
multiple
on-device
models,
hence,
paves
collective
paradigm.
Finally,
put
forward-looking
vision
will
key
realize
