Singapore

Teaching machine learning (ML) workflows to non-programmers
remains a challenge in introductory AI courses.
Traditionally, educators have turned to no-code tools such
as KNIME to lower barriers. With the rise of generative AI
(GenAI), students can now construct ML pipelines through
natural language prompts, potentially offering a new
“no-code” pathway. In a polytechnic-wide elective in
Singapore, students were given the choice of using either
KNIME or a GenAI chatbot for practical exercises and their
semester project. Survey responses, informal interviews,
and classroom observations revealed that both tools
supported conceptual learning, but students’ experiences
diverged: KNIME provided predictability and structured
guidance, while GenAI offered speed and flexibility yet
posed setup challenges and required coding familiarity.
Students valued having a choice, though this complicated
teaching logistics. Our experience suggests that GenAI can
complement—but not yet replace—traditional no-code
platforms, and that the design of introductory activities
is critical for adoption. We share lessons learned for
educators considering GenAI as an alternative in
workflow-based ML education.

AAAI 2026

Understanding the Effects of GenAI as No-Code Alternative for Teaching Machine Learning Workflows

teaching machine learning

genai

no-code

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

technical paper

Generative AI has moved from pilots to everyday practice,
delivering gains in productivity and accessibility while
surfacing present-day risks—hallucinations and reliability
failures, bias and unfairness, prompt-injection attacks,
and so on. These trends make AI safety education a core
competency. In this paper, we survey global AI safety
curricula and, in the Japanese context, observe strong
policy momentum but relatively few courses that explicitly
combine capability instruction with systematic safety
evaluation. In response, we developed a 7-week,
graduate-level intensive at a private science and
engineering university in Japan, with enrollment open to
international exchange students at both the undergraduate
and graduate levels. The curriculum progresses from
machine-learning foundations to generative models and
alignment, with introductory agent topics included to
support risk reasoning. Delivery combines weekly lectures,
invited talks from academia and industry, structured group
discussions, and a final presentation plus a paper-style
final project focused on risk evaluation and mitigation
planning. An end-of-course survey indicates high perceived
learning and positive experience and one student project
later resulted in a peer-reviewed workshop paper at ICLR 2025.

Beyond Prompting: AI Safety Education in the Generative AI Era

Research on how the popularization of generative Artificial
Intelligence (AI) tools is impacting learning environments
has led to hesitancy among educators to teach these tools
in classrooms, creating two observed disconnects.
Generative AI competency is increasingly valued in industry
but not in higher education, and students are experimenting
with generative AI without formal guidance. Grounded in
learning sciences literature, the authors believe students
across fields must be taught to responsibly and expertly
harness the potential of AI tools to ensure job market
readiness and positive outcomes. Computer Science
trajectories are particularly impacted, and while
consistently top ranked U.S. Computer Science departments
teach the mechanisms and frameworks underlying AI, few
appear to offer courses on applications for existing
generative AI tools. A course was developed at a private
research university to teach undergraduate and graduate
Computer Science students applications for generative AI
tools in software development. Two mixed method surveys
indicated students overwhelmingly found the course valuable
and effective. Co-authored by the professor and one of the
graduate students, this paper explores the context,
implementation, and impact of the course through data
analysis and reflections from both perspectives. It
additionally offers recommendations for replication in and
beyond Computer Science departments.

Bridging the Skills Gap: A Course Model for Modern Generative AI Education

As large language models (LLMs) and chatbots become
increasingly prevalent, there is an urgent need to create
engaging, age-appropriate learning activities that foster
foundational AI literacy with a focus on natural language
processing (NLP). This paper presents the iterative design
and implementation of three instructional activities that
introduce middle school learners (ages 11 to 14) to NLP
concepts through playful, hands-on experiences aligned with
the AI4K12 Big Idea of Natural Interaction. These
activities include: (1) an unplugged card game to develop
students' understanding of embeddings and similarity, (2)
an unplugged collaborative sentence-generation challenge
that illustrates how language models work, and (3) a
web-based educational game in which students design and
interact with chatbots. Each activity has been implemented
and refined through multiple iterations in diverse
educational contexts, including teacher professional
development workshops, summer camps, and middle school
classrooms. All activities are designed to be easy to set
up, require only commonly available classroom technology
(e.g., laptops) and a few inexpensive materials (e.g.,
decks of cards), and are supported with facilitation guides
and reflection prompts. Early implementations revealed some
confusion and the need for clearer instructions, but
post-refinement surveys showed that students found the
activities both enjoyable and educational. Findings suggest
that blending unplugged and digital formats enhances
comprehension, and that tailoring content to students'
local contexts supports engagement. By making these
activities and supporting materials openly available, this
work contributes to the growing ecosystem of K–12 AI
education resources and offers practical guidance for
integrating NLP concepts into classrooms.

From Embeddings to Chatbots: Playful NLP Activities for Middle School AI Literacy

Minimizing invasive diagnostic procedures is a central goal
in medical imaging. Perineural invasion (PNI), a critical
prognostic factor where tumors infiltrate nerves, remains
difficult to confirm noninvasively, as its features are
often imperceptible in conventional MRI. PNI research is
severely hampered by data scarcity. Our study utilized a
dataset collected over a decade at Samsung Medical Center
(SMC), initially comprising 306 patients. After rigorous
quality control, the final cohort included 128 T1-weighted
hepatobiliary phase MRI scans, exhibiting significant class
imbalance (44 PNI-positive/84 PNI-negative). To address
these challenges, we present NeoNet, the first integrated
end-to-end 3D deep learning framework for PNI prediction in
cholangiocarcinoma that avoids reliance on radiomics or
handcrafted features. NeoNet integrates three modules: (1)
NeoSeg, utilizing a Tumor-Localized ROI Crop (TLCR)
algorithm; (2) NeoGen, a 3D Latent Diffusion Model (LDM)
with ControlNet, conditioned on anatomical masks to
generate synthetic image patches, specifically balancing
the dataset to a 1:1 ratio; and (3) NeoCls, the final
prediction module. For NeoCls, we developed the
PNI-Attention Network (PattenNet), which uses the frozen
LDM encoder and specialized 3D Dual Attention Blocks (DAB)
designed to detect subtle intensity variations and spatial
patterns indicative of PNI. In rigorous 5-fold
cross-validation, NeoNet outperformed baseline 3D models.
By leveraging synthetic data for balanced training,
PattenNet achieved the highest performance with a maximum
AUC of 0.7903.

NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework
for Non-Invasive Prediction of Perineural Invasion via
Generation-Driven Classification

Recent work has established learned k-space acquisition pat-
terns as a promising direction for improving reconstruction
quality in accelerated Magnetic Resonance Imaging (MRI).
Despite encouraging results, most existing research focuses
on acquisition patterns optimized for a single dataset or
modality, with limited consideration of their transferabil-
ity across imaging domains. In this work, we demonstrate
that the benefits of learned k-space sampling can extend
beyond the training domain, enabling superior reconstruc-
tion performance under domain shifts. Our study presents
two main contributions. First, through systematic evalua-
tion across datasets and acquisition paradigms, we show
that models trained with learned sampling patterns exhibit
improved generalization under cross-domain settings. Sec-
ond, we propose a novel method that enhances domain ro-
bustness by introducing acquisition uncertainty during
train-
ing—stochastically perturbing k-space trajectories to simu-
late variability across scanners and imaging conditions. Our
results highlight the importance of treating k-space
trajectory
design not merely as an acceleration mechanism, but as an
ac-
tive degree of freedom for improving domain generalization
in MRI reconstruction.

On The Role of K-Space Acquisition in MRI Reconstruction
Domain-Generalization

Phenotyping from electronic health records (EHRs) requires
integrating structured signals and unstructured narratives,
yet existing multimodal models often rely on latent
embeddings that obscure reasoning and fail to capture
evolving patient dynamics. We propose a purely LLM-driven
approach that fuses modalities directly in explicit textual
space, enabling transparent knowledge representation and
interpretable reasoning for disease phenotyping. Using the
MIMIC-III dataset, we combine hospital-course sections from
discharge summaries with synthesized ICU time-series
narratives to jointly capture chronic and acute processes.
Across 25 benchmark diseases, LLM-based named entity
recognition, relation classification, and narrative
synthesis yield average F1 improvements of 10-14% over
baselines. A detailed case study on congestive heart
failure further examines the reasoning mechanisms
underlying phenotyping using an LLM-as-judge evaluation
setup, demonstrating how structured extraction, temporal
narratives, and Chain-of-Thought prompting interact to
elicit clinically faithful and auditable reasoning,
suggesting extensibility to diverse phenotypes.

Enhancing LLM-Based Phenotyping via Structured KnowledgeRepresentation and Chain-of-Thought Reasoning on EHR Data

Simulating high-fidelity patients offers a powerful avenue
for studying complex diseases while addressing the
challenges of fragmented, biased, and privacy-restricted
real-world data. In this study, we introduce SynthAgent, a
novel Multi-Agent System (MAS) framework designed to model
obesity patients with comorbid mental disorders, including
depression, anx- iety, social phobia, and binge eating
disorder. SynthAgent integrates clinical and medical
evidence from claims data, population surveys, and
patient-centered literature to con- struct personalized
virtual patients enriched with personal- ity traits that
influence adherence, emotion regulation, and lifestyle
behaviors. Through autonomous agent interactions, the
system simulates disease progression, treatment response,
and life management across diverse psychosocial contexts.
Evaluation of more than 100 generated patients demonstrated
that GPT-5 and Claude 4.5 Sonnet achieved the highest fi-
delity as the core engine in the proposed MAS framework,
outperforming Gemini 2.5 Pro and DeepSeek-R1. SynthA- gent
thus provides a scalable and privacy-preserving frame- work
for exploring patient journeys, behavioral dynamics, and
decision-making processes in both medical and psycho-
logical domains.

SynthAgent: A Multi-Agent LLM Framework for Realistic
Patient Simulation - A Case Study in Obesity with Mental
Health Comorbidities

Immunohistochemistry (IHC) is crucial for cancer diagnosis
to predict treatment response and determine prognosis.
However, IHC analysis remains heavily manual,
time-intensive, and prone to variability. Prior Artificial
Intelligence (AI) models for IHC analysis were based on a
single modality, while state-of-the-art (SOTA)
vision-language models (VLMs), such as CLIP-based PLIP,
have shown great promise in other medical analysis tasks
such as Hematoxylin and Eosin (H&E)-stained histopathology
classification. However, their hard contrastive loss makes
them prone to false negatives. We propose HIS2LIP, the
first VLM for multimodal IHC biomarker-stained images and
text pairs. HIS2LIP improves upon the SOTA CLIP-based
models by exploiting a novel Weighted Inter- and
Intra-modal Soft Embeddings Contrastive Loss (WISE-CL) to
mitigate false negatives, and leveraging domain-specific
contextual information (e.g., tissue types and biomakers)
to improve representation learning. We present three
HIS2LIP variants: HIS2CLIP, HIS2PLIP, and HIS2BiomedCLIP,
based respectively on CLIP, PLIP, and BioMedCLIP backbones.
We fine-tune HIS2LIP variants on MIHIC, the largest
publicly available dataset of IHC image patches, enriching
it with high-quality captions generated using Large
Language Models GPT-4 and Llama-3 in close collaboration
with an expert pathologist who also meticulously validated
the generated captions. An exhaustive evaluation against
SOTA VLMs demonstrates the superiority of HIS2LIP variants,
achieving multiplicative gains in zero-shot classification
of up to 3.5× over CLIP on the MIHIC dataset, and
improvements of 3.21×, 1.16×, and 1.28× in image-to-text,
text-to-text, and image-to-image retrieval, respectively.
Moreover, HIS2LIP reduces patch-level analysis time from
∼15 minutes to ∼6 seconds, while maintaining high accuracy.
Code available at:
https://github.com/his2lip/his2lip-HIS2LIP-model--for-IHC-Image-Analysis.

HIS2LIP: Leveraging Weighted Inter- and Intra-modal Soft
Embeddings Contrastive Loss for Fine-Grained IHC Image
Analysis

Liver tumor segmentation, dynamic enhancement regression,
and classification are critical for clinical assessment and
diagnosis. However, no prior work has attempted to achieve
these tasks simultaneously in an end-to-end framework,
primarily due to the lack of an effective framework that
captures inter-task relevance for mutual improvement and
the absence of a mechanism to effectively extract dynamic
MRI information. To address these challenges, we propose
the Multi-Task Interaction adversarial learning Network
(MTI-Net), a novel integrated framework designed to tackle
these tasks simultaneously. MTI-Net incorporates
Multi-domain Information Entropy Fusion (MdIEF), which
utilizes entropy-aware, high-frequency spectral information
to effectively integrate features from both frequency and
spectral domains, enhancing the extraction and utilization
of dynamic MRI data. The network also introduces a task
interaction module that establishes higher-order
consistency between segmentation and regression, thus
fostering inter-task synergy and improving overall
performance. Additionally, we designed a novel task-driven
discriminator (TDD) to capture internal high-order
relationships between tasks. For dynamic MRI information
extraction, we employ a shallow Transformer network to
perform positional encoding, which captures the
relationships within dynamic MRI sequences. In experiments
on a dataset of 238 subjects, MTI-Net demonstrates high
performance across multiple tasks, indicating its strong
potential for assisting in the clinical assessment of liver
tumors. The source code will be publicly available.

Adversarial Multi-Task Learning for Liver Tumor
Segmentation, Dynamic Enhancement Regression, and
Classification

In pharmacovigilance, analyzing drug safety cases is often
time consuming due to the abundance of laboratory data,
complex medical histories, and intricate temporal
relationships. Agentic AI systems can significantly reduce
case processing time by assisting medical reviewers in
surfacing clinically relevant evidence. However, previous
studies have highlighted that large language models alone
lack causal reasoning and evidence-based interpretability.

To address these limitations, we present DRAGON, a
knowledge-grounded safety case analysis framework that
integrates disproportionality analysis to generate and
prioritize potential adverse event hypotheses. The system
demonstrates how structured medical knowledge and
statistical evidence can be combined to support a reliable,
explainable case assessment and can be readily extended
with causal inference modules for deeper clinical reasoning.

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