UNSTRUCTURED
As
artificial
intelligence
(AI)
technologies
occupy
a
bigger
role
in
psychiatric
and
psychological
care
become
the
object
of
increased
research
attention,
industry
investment,
public
scrutiny,
tools
for
evaluating
their
clinical,
ethical,
user-centricity
standards
have
essential.
In
this
paper,
we
first
review
history
rating
systems
used
to
evaluate
AI
mental
health
interventions.
We
then
describe
recently
introduced
Framework
Tool
Assessment
Mental
Health
(FAITA-Mental
Health),
whose
scoring
system
allows
users
grade
platforms
on
key
domains,
including
credibility,
user
experience,
crisis
management,
agency,
equity,
transparency.
Finally,
demonstrate
use
FAITA-Mental
scale
by
systematically
applying
it
OCD
Coach,
generative
tool
readily
available
ChatGPT
store
designed
help
manage
symptoms
obsessive-compulsive
disorder.
The
results
offer
insights
into
utility
limitations
when
applied
“real-world”
space,
suggesting
that
framework
effectively
identifies
strengths
gaps
AI-driven
tools,
particularly
areas
such
as
acute
management.
also
highlight
need
stringent
guide
integration
manner
is
not
only
effective
but
safe
protective
users’
rights
welfare.
Social
anxiety
(SA)
has
become
increasingly
prevalent.
Traditional
coping
strategies
often
face
accessibility
challenges.
Generative
AI
(GenAI),
known
for
their
knowledgeable
and
conversational
capabilities,
are
emerging
as
alternative
tools
mental
well-being.
With
the
increased
integration
of
GenAI,
it
is
important
to
examine
individuals'
attitudes
trust
in
GenAI
chatbots'
support
SA.
Through
a
mixed-method
approach
that
involved
surveys
(n
=
159)
interviews
17),
we
found
individuals
with
severe
symptoms
tended
embrace
chatbots
more
readily,
valuing
non-judgmental
perceived
emotional
comprehension.
However,
those
milder
prioritized
technical
reliability.
We
identified
factors
influencing
trust,
such
ability
generate
empathetic
responses
its
context-sensitive
limitations,
which
were
particularly
among
also
discuss
design
implications
use
fostering
cognitive
practical
considerations.
Student
simulation
supports
educators
to
improve
teaching
by
interacting
with
virtual
students.
However,
most
existing
approaches
ignore
the
modulation
effects
of
course
materials
because
two
challenges:
lack
datasets
granularly
annotated
materials,
and
limitation
models
in
processing
extremely
long
textual
data.
To
solve
challenges,
we
first
run
a
6-week
education
workshop
from
N
=
60
students
collect
fine-grained
data
using
custom
built
online
system,
which
logs
students'
learning
behaviors
as
they
interact
lecture
over
time.
Second,
propose
transferable
iterative
reflection
(TIR)
module
that
augments
both
prompting-based
finetuning-based
large
language
(LLMs)
for
simulating
behaviors.
Our
comprehensive
experiments
show
TIR
enables
LLMs
perform
more
accurate
student
than
classical
deep
models,
even
limited
demonstration
approach
better
captures
granular
dynamism
performance
inter-student
correlations
classrooms,
paving
way
towards
''digital
twin''
education.
