Organiser:
Biography:
Dr.
Norma
Alias
received
her
Ph.D. in
Industrial
Computing
(Supercomputer)
at
Universiti
Kebangsaan
Malaysia.
She is
the
Committee
of
Synthetics
Biology
RG and
Future
Ready
Educator
4.0.
Handling
30
webinars
per year
at the
local
and
international
levels.
She is
among 40
UTM
research
excellence
in the
year
2006,
Venus
Distinguished
Women
Award,
and a
mentor
for the
SUNSHINE++
program.
She
chairs 4
international
conferences,
serves
as MSMK
Chief
Editor,
and is
an
Associate
Editorial
Board
member
for 38
international
journals.
Her
research
encompasses
big data
analytics
on
high-performance
computing,
validation
of
complex
mathematical
models,
and
solving
grand-challenge
applications
in
nanotechnology,
IoT, and
AI-smart
digital
solutions
toward
Industry
5.0.
Introduction:
The rapid advancement of Artificial
Intelligence (AI) is fundamentally reshaping
the educational landscape, making it a
critical pillar for achieving Sustainable
Development Goal 4 (Quality Education)
within a Sustainable Society (SS). The
integration of cutting-edge AI technology
into educational ecosystems to create
adaptive, flexible, and future-ready
learning environments is the main topic of
this special session. It seeks to
investigate how cutting-edge AI algorithms,
machine learning, deep learning, predictive
analytics, and learning-trajectory modelling
can personalize educational experiences ,
may improve student outcomes, optimise
teaching strategies, and customise
educational experiences. This session will
provide researchers and practitioners a
platform to exchange innovative results,
address ethical challenges, and work
together on AI-driven solutions that empower
educators and students for a sustainable
future by bridging the gap between
theoretical AI advancements and real-world
pedagogical applications.
Topics:
The
session invites original research
contributions, case studies, and review
articles on topics including, but not
limited to:
1. Machine Learning & Deep Learning in
Education: Applications of neural networks,
Natural Language Processing (NLP), and
computer vision for automated assessment,
educational content generation, and
real-time student engagement analysis.
2. Predictive Analytics in Education:
Utilizing AI algorithms to predict student
performance, identify at-risk learners, and
forecast dropout rates to enable timely,
data-driven interventions.
3. Learning Trajectory Modeling: Analyzing
and mapping student knowledge acquisition
trajectories over time to optimize
curriculum design, recommend resources, and
personalize long-term learning plans.
4. Advanced AI Algorithms for Adaptive
Learning: Development and evaluation of
novel algorithms that dynamically adjust the
difficulty, pacing, and modality of
educational content based on real-time
learner feedback and cognitive states.
5. Generative AI for Teaching and Learning:
Leveraging Large Language Models (LLMs) and
generative AI to create interactive learning
materials, virtual teaching assistants, and
immersive, simulation-based educational
environments.
6. Ethical AI, Fairness, and Privacy in
EdTech: Addressing algorithmic bias,
ensuring transparency, and safeguarding
student data privacy when deploying machine
learning models in educational settings.
7. AI for Inclusive and Accessible
Education: Utilizing AI technologies to
bridge educational divides, provide
real-time translation/transcription, and
support learners with special needs, thereby
promoting lifelong learning for all members
of a sustainable society.
8. AI-Driven Pedagogic: Exploring new
teaching methodologies powered by AI,
including game-based learning, intelligent
feedback systems, and collaborative learning
environments.
Alignment
with AISS 2026:
This special session proposal is
designed to align seamlessly with the core
themes and tracks of the AISS 2026
conference:
Conference Focus: Directly addresses the
conference's focus on applying AI
technologies to realize a universal
Sustainable Society (SS), specifically by
targeting Sustainable Development Goal 4
(Quality Education) .
Track Alignment:
a) Track 1: AI Computing and Control: The
proposed topics on "Advanced AI Algorithms
for Adaptive Learning" and "Learning
Trajectory Modeling" directly relate to
complex computing and control algorithms.
b) Track 2: Other AI Technologies: This
session serves as a prime example of an
application area for other AI Technologies,
including Generative AI, NLP, and Computer
Vision.
c) Track 4: AI Technology aided Sustainable
Development: The entire session is a direct
application of AI Technology aided
Sustainable Development, demonstrating how
AI contributes to the social and human
capital aspects of sustainability.