About the Programme

Explore AI. Experience Innovation. Build Your Future.

The AI Internship for Young Achievers (AIYA) is a transformative 10-day experience at the National University of Singapore (NUS), designed for high-school students aged 15 years and above. This one-of-a-kind programme blends academic excellence with hands-on experiential learning, giving students a unique opportunity to immerse themselves in the rapidly evolving world of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).

Through a carefully curated curriculum, students will engage with cutting-edge AI concepts through faculty-led sessions, real-world case studies, and hands-on projects. They will also gain insights into AI applications across industries such as healthcare, finance, and technology, while developing critical thinking, adaptability, and collaboration skills.

Beyond technical knowledge, AIYA offers students an immersive experience of university life at the National University of Singapore, ranked #15 in the QS World University Rankings 2026. The programme fosters independent living, cross-cultural engagement, and meaningful professional networking opportunities, preparing students to become future-ready global leaders.

NUS Singapore Campus

About NUS

National University of Singapore (NUS) is a premier multidisciplinary research university in Singapore dedicated to the study of technology, innovation, and its growing influence on the global economy, sustainability, and society. NUS serves as a leading centre for research and intellectual dialogue, bringing together academicians, researchers, and industry experts from around the world.

The university focuses on key areas such as engineering, business, technology and innovation, sustainability, and social development. Through its research initiatives, conferences, and collaborations, NUS contributes significantly to thought leadership and discussions related to Asia and the world.

For students, NUS provides a unique environment that combines academic excellence with applied experiential learning. Students gain exposure to research methodologies, analytical thinking, innovation frameworks, and real-world problem solving within a globally respected university ecosystem.

NUS offers students an enriching platform to engage with contemporary global challenges, particularly through interdisciplinary research and innovation projects aligned with the United Nations Sustainable Development Goals (SDGs).

Learning Intervention

Classroom Training

Understand fundamental AI concepts in your area of passion

Hands-on Sessions

Apply AI techniques through real-life practical sessions

Lab Visits

Witness how industries innovate through AI

Project Work

Complete technological innovation projects using AI

Group Presentation

Present to NUS faculty and professionals

Session Schedule

Comprehensive AI sessions delivered by NUS ISAS faculty with hands-on practical learning

NUS ISAS Faculty Session Schedule

15 Academic Hours

AI and MLData AnalyticsDeep Learning

Session	Duration	Topics Covered
1	3 hours	Introduction to AI and its Impact AI basics including machine learning, deep learning, and data science • AI's impact on medicine, entertainment, environmental science & education • Interactive AI demos (e.g., Google's Teachable Machine)
2	3 hours	Data-Analytic Thinking The data pipeline: data types, acquisition & preparation • Data exploration: statistics & exploratory data analysis (EDA) • Data presentation: visualization techniques & statistical analysis
3	3 hours	AI Technologies Artificial Neural Networks (ANN) & backpropagation • Deep learning architectures: CNN, RNN & Transformers • Hands-on: implementing a simple neural network using TensorFlow
4	3 hours	AI Applications in Different Fields AI in healthcare, finance, sports & education • Field-specific breakout sessions & case study discussions • Project brainstorming: teams form and settle on project ideas
5	3 hours	Project Development and Presentation Team project work with instructor guidance • Student presentations: inspiration, process & impact • Reflection, learning takeaways & closing discussion

13 hours

Introduction to AI and its Impact

AI basics including machine learning, deep learning, and data science • AI's impact on medicine, entertainment, environmental science & education • Interactive AI demos (e.g., Google's Teachable Machine)

23 hours

Data-Analytic Thinking

The data pipeline: data types, acquisition & preparation • Data exploration: statistics & exploratory data analysis (EDA) • Data presentation: visualization techniques & statistical analysis

33 hours

AI Technologies

Artificial Neural Networks (ANN) & backpropagation • Deep learning architectures: CNN, RNN & Transformers • Hands-on: implementing a simple neural network using TensorFlow

43 hours

AI Applications in Different Fields

AI in healthcare, finance, sports & education • Field-specific breakout sessions & case study discussions • Project brainstorming: teams form and settle on project ideas

53 hours

Project Development and Presentation

Team project work with instructor guidance • Student presentations: inspiration, process & impact • Reflection, learning takeaways & closing discussion

Assessment Criteria(Conducted by NUS)

Continuous Assessment (MCQ Quizzes)20%

End Term Assessment (MCQ)30%

Project Assessment50%

Programme Completion Documents

Letter of Evaluation NUS ISAS

Letter of Recommendation NUS ISAS

Project Case Studies

Real projects developed by our students, showcasing innovation and practical application of skills

FareCast – AI Flight Fare Prediction System

by Jia Keniya, Vrishti Dhakan, Arshia Sharma, Vivaan Aggarwal, Yash Agrawal

Developed a machine learning–based solution to predict airline ticket prices by analyzing factors such as travel dates, airline, route, duration, and days left before departure. The system helps users make cost-effective booking decisions and understand airline pricing trends.

Technologies Used:

Python

Machine Learning

Random Forest

Gradient Boosting

Orange ML

Outcome:

Achieved high prediction accuracy, enabling travelers and businesses to plan trips efficiently and reduce airfare costs.

Predicting Life Expectancy Using Machine Learning

by A. Aishwarya, Isha Narra, Garvit Bhandari, Praney Guglani, Pranav Pezhery

Built a data-driven machine learning model to predict life expectancy by analyzing healthcare, economic, lifestyle, and environmental factors. The project focuses on uncovering key influences on public health outcomes to support informed decision-making.

Technologies Used:

Python

Machine Learning

Random Forest

Gradient Boosting

XGBoost

Orange ML

Outcome:

Delivered accurate life expectancy predictions along with actionable insights for healthcare planning, insurance modeling, and preventive strategies.

Error 404: Asteroid Not Found – Asteroid Collision Risk Prediction

by Varalika Khurana, Aarav Bhandari, Aditya Rao, Garvit Gupta, Sudarsh Saini

Developed a machine learning model to predict collision risks of near-Earth objects using orbital and astronomical data. The system automates asteroid threat assessment and provides rapid risk predictions to support planetary defense efforts.

Technologies Used:

Python

Machine Learning

Random Forest

Gradient Boosting

Neural Networks

SMOTE

Outcome:

Achieved high prediction accuracy, enabling faster identification of potentially hazardous asteroids and supporting disaster prevention initiatives.