AI INTERNSHIP FOR YOUNG ACHIEVERS (AIYA ON CAMPUS)

Programme Dates: 8th – 17th June 2025

                               6th – 15th July 2025

Accepting Grade 9-12 Students (Age 14 years +)

Book Consultation

What is AIYA?

Experience AI. Expand Horizons. Empower Your Future.

The AI Internship for Young Achievers (AIYA) is a transformative 10-day on- campus experience at the National University of Singapore (NUS), designed for high-school students aged 14 years and above. Launched in 2020, this one-of-a- kind programme in its 10th Edition, blends academic excellence with hands-on experiential learning, equipping young minds with the knowledge, skills, and confidence to thrive in the age of AI.

 

You will explore cutting-edge Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) concepts through NUS faculty-led lectures, real-world case studies, and hands-on projects. You 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 provides a glimpse into university life at NUS, a globally ranked #8 institution, fostering independent living, cross-cultural engagement, and professional networking. Students also receive a Certificate of Completion from NUS with top performers earning a Letter of Recommendation—a valuable addition to their academic portfolio.

 

Join AIYA and take your first step toward becoming a Future-ready Global Innovator.

At a Glance
Duration : 2 weeks
Grades : Grades 9 - 12
Academic Fee : SGD 3499
Programme Outcome : Transcript from NUS
Application Deadline : 30th November, 2020
Duration : On Campus – 10 Days
Learner Profile : Grades 9 - 12
Internship Fee : USD 2,804
Start Date : June 2025
Duration : 12 weeks
Learner Profile : Grades 8 - 12
Internship Fee :

SGD 2379*
Application Deadline : June 21, 2021
Mode of Delivery : LIVE Online

Download Brochure

Download Brochure

Learning Intervention

INDICATIVE SESSION PLAN FOR AIYA EXPERIENTIAL ON CAMPUS – JUNE/JULY’2023

Conceptual Learning with NUS, On Campus at NUS, Singapore
Sessions Concepts Hands-On Assessment
Session - 1 Introduction to Artificial Intelligence
  • What is Artificial Intelligence?
  • History and State-of-Art Applications
  • Machine Learning Tools & Applications
  • Principles of problem solving and the search for solution
  • Introduction to google collab
  • Running the code and loading assets
  • Learn Python basics
  • Basics of python and syntax
  • Control statements in python
  • Quiz after each session
  • End Term Quiz
Session - 2 Understanding data and lifecycle
  • Types of data, data formats
  • How to process data and find information
  • Introduction to basic stats and its uses
  • ML lifecycle, models and training
  • Train, test and validation splits. K-folds
  • Loading datasets on google collab and mounting data
  • Introduction to pandas and data frames
  • Data Frame operations and splicing
  • Numpy basics and understanding
Session 3 Machine Learning Fundamentals
  • Supervised, Unsupervised and Reinforcement Learning
  • Prediction and Classification
  • Components of the performance evaluation
  • Linear Regression example
  • Building their own model from scratch
  • Understanding regression and propagation
  • Calculation of error and loss function in python
  • Plotting results using matplotlib
Session 4 Unsupervised Learning and other concepts
  • Clustering
  • Association - theory, not maths
  • Clustering example
  • Decision Tree’s Learning Algorithms {only Hunt's}
  • Optimal Attribute and Error types
  • Overfitting and Error Performance
  • Ensemble model construction(optional)
  • Clustering in python
  • Decision tree code overview
  • Project Guidance & Feedback
Session 5 Neural Networks and Deep Learning
  • Learning Inspired by the Brain
  • Notation
  • Network structures
  • Perceptrons
  • Multilayer Feed-Forward Networks
  • Back-propagation intro
  • Understanding MLPs in code
  • Neural network model in python
  • Calculating model statistics and performance.
  • Project Guidance and feedback.

PRACTICAL LEARNING SESSION - Hands - On Application with CG

Sessions Concepts Hands-On Project Assessment
Session - 1
  • Introduction to google collab
  • Running the code and loading assets
  • Learn Python basics
  • Basics of python and syntax
  • Control statements in python
  • All Hands-On
  • Project Progress Update and Review in every session
  • None
Session - 2
  • Loading datasets on google collab and mounting data
  • Introduction to pandas and data frames
  • DataFrame operations and splicing
  • Numpy basics and understanding
  • All Hands-On
Session 3 Building their own model from scratch
  • Understanding regression and propagation
  • Calculation of error and loss function in python
  • Plotting results using matplotlib
  • All Hands-On
Session 4
  • Ensemble model construction(optional)
  • Clustering in python
  • Decision tree code overview
  • All Hands-On
Session 5
  • Understanding MLPs in code
  • Neural network model in python
  • Calculating model statistics and performance.
  • All Hands-On

 

Session wise Agenda for Learning Intervention

Sessions with NUS

Mode

Sessions

Duration (hours)

Theory and Hands-On

Assessment

On Campus at NUS, Singapore

Session - 1

3 hours

  • Introduction to AI

  • A brief review on AI history

  • AI Applications: State of the art

  • AI, Machine Learning, and Deep Learning

  • Restrictions and constraints

  • Future of AI

  • Introduction to Google Colab (Python)

  • Introduction to Orange

  • Why Orange?

  • Setting up your system

  • Creating Your First Workflow

  • Introduction to Preprocessing of Data Using Python

  • Checking and handling missing values

  • Handling categorical data (Label encoding, One-hot encoding)

  • Standardize/Normalize continuous data

  • PCA transformation

  • Data spitting

Continuous Assessment MCQ Quiz

On Campus at NUS, Singapore

Session - 2

3 hours

  • Machine Learning Methods Using Orange

    • Importing the data files

    • Understanding our Data

    • Missing Values and Imputation

    • Training your First Model

    • Supervised Learning Algorithms

      • Logistic Regression

      • Random Forest

      • Support Vector Machine

      • K-Nearest Neighbor

      • Neural Network

      • Comparison of Different Models

    • Clustering Algorithms

      • Hierarchical Clustering

      • K-Means

    • Visualization

      • Data Table

      • Scatter Plot

      • Mosaic Display

      • Sieve Diagram

      • Rank

      • Rad Viz

Continuous Assessment MCQ Quiz

On Campus at NUS, Singapore

Session - 3

3 hours

  • Machine Learning Methods Using Orange

    • Clustering and its applications

      • Clustering

        • Definition of Clustering Task

        • Popular Clustering Algorithms: K-means, Hierarchical, DBSCAN

      • Applications

        • Customer segmentation, Image segmentation, Anomaly detection

    • Decision Tree and its applications

      • Decision Tree

        • Definition of Decision Tree task

        • Explain decision tree method

      • Applications

        • Credit risk assessment, Disease diagnosis, Predictive maintenance

Continuous Assessment MCQ Quiz

On Campus at NUS, Singapore

Session - 4

3 hours

  • Machine Learning Case Studies and Application

    • Using Orange and Python

      • Healthcare: Predicting Disease Outcomes (CLASSIFICATION)

      • Finance: Fraud Detection (OUTLIER DETECTION)

      • Marketing: Customer Segmentation (CLUSTERING)

    • Machine Learning Case Studies and Application Using Orange and Python

      • Retail: Personalized Recommendations (RECOMMENDER SYSTEMS)

    • LLM (Large Language Model) Applications

Continuous Assessment MCQ Quiz

 

Session - 5

3 hours

  • Course Summarization

  • Project Presentations

     

Continuous Assessment MCQ Quiz

 

DAY 9:00 AM – 12:00 PM          2:00 PM – 5:00 PM  6:00 PM – 8:00 PM
Day 1 (Sun) Arrival in Singapore
 		 Check-in at 2:00 PM followed by the programme orientation  
Day 2 (Mon) NUS Session 1 Project Guidance & Mentorship by 
CG Teaching Assistants		 Self-Directed Group Work on Project

Day 3  (Tue)

 NUS Session 2 Project Guidance & Mentorship by 
CG Teaching Assistants		 Self-Directed Group Work on Project
Day 4 (Wed) NUS Session 3

Project Guidance & Mentorship by 
CG Teaching Assistants

 Self-Directed Group Work on Project
Day 5  (Thu) NUS Session 4

Project Guidance & Mentorship by 
CG Teaching Assistants

 Self-Directed Group Work on Project
Day 6    (Fri)

Project Guidance & Mentorship by CG Teaching Assistants

City Tour (Esplanade, Merlion, Marina Bay Sands, China Town, Little India)

 Self-Directed Group Work on Project
Day 7
​​​​​​​(Sat)

                                                            Visit to Universal Studios - 11 AM to 6 PM
Day 8
(Sun)		                                Visit to Sentosa - (Skyride and Luge Ride)  Self-Directed Group Work on Project
Day 9
(Mon)		 Project Guidance & Mentorship by 
CG Teaching Assistants		 NUS Session 5 – Project presentation and Evaluation  Valedictory and Dinner
Day 10
(Tue)

Checkout (before 12:00 PM) and Proceed to Airport

                                      Departure from Singapore

 

Assessment Criteria

Assessment Component Weightage
Weekly Group Assignments/Quiz 20%
End Term Quiz 40%
Project Presentation 40%
testimonial

Certificate Of Completion NUS

testimonial

Individual Letter of Recommendations for top project group from NUS

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Pedagogy
Learning Goals

INDICATIVE SESSION PLAN FOR AIYA EXPERIENTIAL ON CAMPUS – JUNE/JULY’2023

Conceptual Learning with NUS, On Campus at NUS, Singapore
Sessions Concepts Hands-On Assessment
Session - 1 Introduction to Artificial Intelligence
  • What is Artificial Intelligence?
  • History and State-of-Art Applications
  • Machine Learning Tools & Applications
  • Principles of problem solving and the search for solution
  • Introduction to google collab
  • Running the code and loading assets
  • Learn Python basics
  • Basics of python and syntax
  • Control statements in python
  • Quiz after each session
  • End Term Quiz
Session - 2 Understanding data and lifecycle
  • Types of data, data formats
  • How to process data and find information
  • Introduction to basic stats and its uses
  • ML lifecycle, models and training
  • Train, test and validation splits. K-folds
  • Loading datasets on google collab and mounting data
  • Introduction to pandas and data frames
  • Data Frame operations and splicing
  • Numpy basics and understanding
Session 3 Machine Learning Fundamentals
  • Supervised, Unsupervised and Reinforcement Learning
  • Prediction and Classification
  • Components of the performance evaluation
  • Linear Regression example
  • Building their own model from scratch
  • Understanding regression and propagation
  • Calculation of error and loss function in python
  • Plotting results using matplotlib
Session 4 Unsupervised Learning and other concepts
  • Clustering
  • Association - theory, not maths
  • Clustering example
  • Decision Tree’s Learning Algorithms {only Hunt's}
  • Optimal Attribute and Error types
  • Overfitting and Error Performance
  • Ensemble model construction(optional)
  • Clustering in python
  • Decision tree code overview
  • Project Guidance & Feedback
Session 5 Neural Networks and Deep Learning
  • Learning Inspired by the Brain
  • Notation
  • Network structures
  • Perceptrons
  • Multilayer Feed-Forward Networks
  • Back-propagation intro
  • Understanding MLPs in code
  • Neural network model in python
  • Calculating model statistics and performance.
  • Project Guidance and feedback.

PRACTICAL LEARNING SESSION - Hands - On Application with CG

Sessions Concepts Hands-On Project Assessment
Session - 1
  • Introduction to google collab
  • Running the code and loading assets
  • Learn Python basics
  • Basics of python and syntax
  • Control statements in python
  • All Hands-On
  • Project Progress Update and Review in every session
  • None
Session - 2
  • Loading datasets on google collab and mounting data
  • Introduction to pandas and data frames
  • DataFrame operations and splicing
  • Numpy basics and understanding
  • All Hands-On
Session 3 Building their own model from scratch
  • Understanding regression and propagation
  • Calculation of error and loss function in python
  • Plotting results using matplotlib
  • All Hands-On
Session 4
  • Ensemble model construction(optional)
  • Clustering in python
  • Decision tree code overview
  • All Hands-On
Session 5
  • Understanding MLPs in code
  • Neural network model in python
  • Calculating model statistics and performance.
  • All Hands-On

 

Curriculum

Session wise Agenda for Learning Intervention

Sessions with NUS

Mode

Sessions

Duration (hours)

Theory and Hands-On

Assessment

On Campus at NUS, Singapore

Session - 1

3 hours

  • Introduction to AI

  • A brief review on AI history

  • AI Applications: State of the art

  • AI, Machine Learning, and Deep Learning

  • Restrictions and constraints

  • Future of AI

  • Introduction to Google Colab (Python)

  • Introduction to Orange

  • Why Orange?

  • Setting up your system

  • Creating Your First Workflow

  • Introduction to Preprocessing of Data Using Python

  • Checking and handling missing values

  • Handling categorical data (Label encoding, One-hot encoding)

  • Standardize/Normalize continuous data

  • PCA transformation

  • Data spitting

Continuous Assessment MCQ Quiz

On Campus at NUS, Singapore

Session - 2

3 hours

  • Machine Learning Methods Using Orange

    • Importing the data files

    • Understanding our Data

    • Missing Values and Imputation

    • Training your First Model

    • Supervised Learning Algorithms

      • Logistic Regression

      • Random Forest

      • Support Vector Machine

      • K-Nearest Neighbor

      • Neural Network

      • Comparison of Different Models

    • Clustering Algorithms

      • Hierarchical Clustering

      • K-Means

    • Visualization

      • Data Table

      • Scatter Plot

      • Mosaic Display

      • Sieve Diagram

      • Rank

      • Rad Viz

Continuous Assessment MCQ Quiz

On Campus at NUS, Singapore

Session - 3

3 hours

  • Machine Learning Methods Using Orange

    • Clustering and its applications

      • Clustering

        • Definition of Clustering Task

        • Popular Clustering Algorithms: K-means, Hierarchical, DBSCAN

      • Applications

        • Customer segmentation, Image segmentation, Anomaly detection

    • Decision Tree and its applications

      • Decision Tree

        • Definition of Decision Tree task

        • Explain decision tree method

      • Applications

        • Credit risk assessment, Disease diagnosis, Predictive maintenance

Continuous Assessment MCQ Quiz

On Campus at NUS, Singapore

Session - 4

3 hours

  • Machine Learning Case Studies and Application

    • Using Orange and Python

      • Healthcare: Predicting Disease Outcomes (CLASSIFICATION)

      • Finance: Fraud Detection (OUTLIER DETECTION)

      • Marketing: Customer Segmentation (CLUSTERING)

    • Machine Learning Case Studies and Application Using Orange and Python

      • Retail: Personalized Recommendations (RECOMMENDER SYSTEMS)

    • LLM (Large Language Model) Applications

Continuous Assessment MCQ Quiz

 

Session - 5

3 hours

  • Course Summarization

  • Project Presentations

     

Continuous Assessment MCQ Quiz
Schedule

 

DAY 9:00 AM – 12:00 PM          2:00 PM – 5:00 PM  6:00 PM – 8:00 PM
Day 1 (Sun) Arrival in Singapore
 		 Check-in at 2:00 PM followed by the programme orientation  
Day 2 (Mon) NUS Session 1 Project Guidance & Mentorship by 
CG Teaching Assistants		 Self-Directed Group Work on Project

Day 3  (Tue)

 NUS Session 2 Project Guidance & Mentorship by 
CG Teaching Assistants		 Self-Directed Group Work on Project
Day 4 (Wed) NUS Session 3

Project Guidance & Mentorship by 
CG Teaching Assistants

 Self-Directed Group Work on Project
Day 5  (Thu) NUS Session 4

Project Guidance & Mentorship by 
CG Teaching Assistants

 Self-Directed Group Work on Project
Day 6    (Fri)

Project Guidance & Mentorship by CG Teaching Assistants

City Tour (Esplanade, Merlion, Marina Bay Sands, China Town, Little India)

 Self-Directed Group Work on Project
Day 7
​​​​​​​(Sat)

                                                            Visit to Universal Studios - 11 AM to 6 PM
Day 8
(Sun)		                                Visit to Sentosa - (Skyride and Luge Ride)  Self-Directed Group Work on Project
Day 9
(Mon)		 Project Guidance & Mentorship by 
CG Teaching Assistants		 NUS Session 5 – Project presentation and Evaluation  Valedictory and Dinner
Day 10
(Tue)

Checkout (before 12:00 PM) and Proceed to Airport

                                      Departure from Singapore

 

Assessment

Assessment Criteria

Assessment Component Weightage
Weekly Group Assignments/Quiz 20%
End Term Quiz 40%
Project Presentation 40%

Admissions

Certificates

AIYA HIGH ACHIEVERS CERTIFICATE

AIYA High Achiever Certificate recognizes participants who demonstrate academic excellence, good teamwork, sound character, strong leadership, and a passion for creativity and innovation.

Associates

National University of Singapore

The NUS School of Computing traces its roots back to the Nanyang University Department of Computer Science that was established in 1975 – the first of its kind in Singapore. Since then, it has developed into one of the leading computing schools in the world, with faculty members who are both internationally recognised researchers and inspiring teachers. The school offers undergraduate and graduate degree programmes across the full spectrum of the field of computing, including Computer Science, Information Systems, Computer Engineering, Business Analytics and Information Security, as well as specialisations in emerging areas of importance such as artificial intelligence, fintech, blockchain, analytics and security.

Dr. TAN Wee Kek

Dr. TAN Wee Kek

Associate Professor

Department of Information Systems & Analytics
School of Computing
National University of Singapore

Dr. TAN Wee Kek is currently an Associate Professor in the Department of Information Systems & Analytics at the School of Computing, National University of Singapore. He is also a Fellow of the prestigious NUS Teaching Academy. He graduated with a Doctor of Philosophy in Information Systems in July 2013 and a Bachelor of Computing in Information Systems (1st Class Honours) in July 2007, both from the National University of Singapore. Prior to this, he attended Singapore Polytechnic and graduated with a Diploma in Computer Information Systems with Merit in July 2001.

 

His current primary research interests focus on consumer-based information technology (e.g., online decision aids, social computing, virtual worlds and consumer cloud services). Most of his research is based on design science, a well-established problem-solving paradigm that has been widely adopted in information systems research. His current secondary research interests focus on information systems education.

 

His work has been published or is forthcoming in journals such as Journal of the American Society for Information Science and Technology (JASIST), Decision Support Systems (DSS), Communications of the Association for Information Systems (CAIS), and Journal of Information Systems Education (JISE). His work has also been presented or is forthcoming in conferences such as ACM SIGMIS Computer Personnel Research Conference (SIGMIS-CPR), IFIP Working Group 8.2 Working Conference (IFIP WG8.2), European Conference on Information Systems (ECIS), Americas Conference on Information Systems (AMCIS), and International Conference on Human-Computer Interaction (ICHCI).

DR. SANKA RASNAYAKA

DR. SANKA RASNAYAKA

Lecturer, School of Computing (SoC)

Subject Expert: Discrete Structures, Biometric Authentication, Design and Analysis of Algorithms, Programming Methodology

 

Professional Career: Joined the National University of Singapore’s School of Computing as a Lecturer in January 2022. He is driven by a passion for applying his expertise to solve real-world problems and excels in both leadership and collaborative roles

 

Educational Career: PhD in Computer Science, School of Computing, National University of Singapore and a BSc (Hons) in Computer Science and Engineering, University of Moratuwa, Sri Lanka

National University of Singapore

The NUS School of Computing traces its roots back to the Nanyang University Department of Computer Science that was established in 1975 – the first of its kind in Singapore. Since then, it has developed into one of the leading computing schools in the world, with faculty members who are both internationally recognised researchers and inspiring teachers. The school offers undergraduate and graduate degree programmes across the full spectrum of the field of computing, including Computer Science, Information Systems, Computer Engineering, Business Analytics and Information Security, as well as specialisations in emerging areas of importance such as artificial intelligence, fintech, blockchain, analytics and security.

Dr. TAN Wee Kek

Dr. TAN Wee Kek

Associate Professor

Department of Information Systems & Analytics
School of Computing
National University of Singapore

Dr. TAN Wee Kek is currently an Associate Professor in the Department of Information Systems & Analytics at the School of Computing, National University of Singapore. He is also a Fellow of the prestigious NUS Teaching Academy. He graduated with a Doctor of Philosophy in Information Systems in July 2013 and a Bachelor of Computing in Information Systems (1st Class Honours) in July 2007, both from the National University of Singapore. Prior to this, he attended Singapore Polytechnic and graduated with a Diploma in Computer Information Systems with Merit in July 2001.

 

His current primary research interests focus on consumer-based information technology (e.g., online decision aids, social computing, virtual worlds and consumer cloud services). Most of his research is based on design science, a well-established problem-solving paradigm that has been widely adopted in information systems research. His current secondary research interests focus on information systems education.

 

His work has been published or is forthcoming in journals such as Journal of the American Society for Information Science and Technology (JASIST), Decision Support Systems (DSS), Communications of the Association for Information Systems (CAIS), and Journal of Information Systems Education (JISE). His work has also been presented or is forthcoming in conferences such as ACM SIGMIS Computer Personnel Research Conference (SIGMIS-CPR), IFIP Working Group 8.2 Working Conference (IFIP WG8.2), European Conference on Information Systems (ECIS), Americas Conference on Information Systems (AMCIS), and International Conference on Human-Computer Interaction (ICHCI).

DR. SANKA RASNAYAKA

DR. SANKA RASNAYAKA

Lecturer, School of Computing (SoC)

Subject Expert: Discrete Structures, Biometric Authentication, Design and Analysis of Algorithms, Programming Methodology

 

Professional Career: Joined the National University of Singapore’s School of Computing as a Lecturer in January 2022. He is driven by a passion for applying his expertise to solve real-world problems and excels in both leadership and collaborative roles

 

Educational Career: PhD in Computer Science, School of Computing, National University of Singapore and a BSc (Hons) in Computer Science and Engineering, University of Moratuwa, Sri Lanka

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Frequently Asked Questions

What is the AIYA?

The A.I. Internship is an On Campus – 10 Days full of learning, It is conducted by National University of Singapore (NUS).

It is conducted by:

  • National University of Singapore (NUS) School of Computing Singapore
  • Amazon (AWS)

It is 15 days On Campus - internship programme, conducted at National University of Singapore, (NUS) School of Computing, Singapore.

It is an academic internship, which is a hands-on guided experience in an academic environment to hone industry-relevant skills and knowledge in a chosen domain with mentorship and training by university professor(s) and industry professional(s).

  • They will understand, experience and apply fundamental AI concepts.
  • They will appreciate the workings behind real life AI research and applications.
  • They will conceptualise, design and implement a social/ technological innovation project with expert inputs from faculty from NUS and professionals from Amazon.
  • They will get insights on higher education and the international university admission process and applications in industry.
  • They will develop a strong profile for international university admissions.

Our project managers will be able to guide you to the admissions department. You will need to explore opportunities on your own with the admissions department based on your area of passion and interest for higher studies.

How do I apply?

You may download the Factsheet below or fill in the interest form. One of our counsellors will be in touch with you to take it forward and assist you with the enrollment. You may also enroll through our website and make the payment via a Debit/Credit card or Online Banking.

This program is open to all students from middle school and high school . This is an open programme that focuses on providing a better understanding of Artificial Intelligence, how it is changing our world, its benefits and usage in our everyday lives.

No, our course is specially designed to ensure that students from all streams and areas of study can easily understand the concepts taught at this internship.

Yes, students studying the ICSE, CBSE, IB MYP, IB DP, IGCSE curriculum can apply for the programme. Students can be of any nationality, ethnicity and belong to any country!

This is a fully-residential program. All students and visiting faculty will stay at the Hostel/ Hotel Accommodation in Singapore. NUS also has a large food court which serves food from 16 countries. Vegetarian and non-vegetarian both options are available.

Please refer the programme fee on the website. In addition to that, you should factor in the cost of flights, insurance and visa applications . Also, food expenses are an additional cost of approximately S$ 20 per day.

The academic internship schedule, date and time are subject to change based on the availability of professor(s) from NUS. Prior notice will be given to all concerned parties on the change of date and the best efforts will be made by Corporate Gurukul to accommodate all parties when deciding the revised schedule, date and time.

Is it a selection-based programme?

Yes

The selection criteria are as follows:

  1. Overall academic performance (grades) in current year or previous year
  2. Resume and Essays in Application Form

What is the last date to pay for the registration fees?

Please refer the Offer Letter issued to you.

  • Certificate of Completion from National University of Singapore, School of Computing (NUS)
  • Certificate of Completion from Amazon (AWS)
  • Individual Letter of Recommendations for top project group by NUS
  • Flights/Visa/Travel Insurance
  • Local Transport in India
  • International SIM Card
  • Local Transport in Singapore for personal visits/sightseeing
  • Entrance Fee to various tourist attractions in Singapore
  • Medical expenses in Singapore
  • Gym, laundry or any other hostel facility charges
  • Stay in Singapore before or after academic internship dates

Which certificate(s) will I get after completion of this academic internship?
  • Certificate of Completion by National University of Singapore (NUS)
  • Individual Letter of Recommendations for top project group from NUS

Issuance of all certificates and the LoR will be at Corporate Gurukul’s discretion subject to:

  1. the participant’s performance
  2. completion of assignments and
  3. 90% attendance during the academic internship

The certificates will be awarded during the Valedictory Ceremony at the end of the programme.

The LOR(s) will be posted to your respective institutes 60 days post the programme completion.

The hands-on sessions are conducted in the Lecture Theatre/Seminar Rooms. You will not visit any laboratories for the same unless requested by the professor(s).

Laptop with the specifications mentioned in ACADEMIC INTERNSHIP PREREQUISITES is MANDATORY for all participants.

No. The entire academic internship is delivered at NUS, Singapore.

Yes, you will be assigned teams for project work and assignments. These teams typically consist of 4-5 members.

No, the team members are assigned by Corporate Gurukul team in consultation with the professor(s). They are created keeping in mind that the prerequisite readiness and a proper mix of students from various institutions. Any request for choice of team member will not be entertained.

All assignments and projects will be submitted on the Learning Management System, Acadly.

Assignments will be given daily and need to be submitted as per specified deadline. You may be required to work on the assignments in teams or individually as per the nature of assignment.

You are required to complete one project during the duration of the programme. This will be done in teams.

The project presentations will be conducted post the NUS and Amazon sessions.

Yes, both NUS and Amazon (AWS) sessions are graded.

The MCQ Quiz will be conducted every day during the afternoon session.

Any late submissions of project, assignments or quizzes beyond specified deadline will carry negative marks.

About AIYA

What is the AIYA?

The A.I. Internship is an On Campus – 10 Days full of learning, It is conducted by National University of Singapore (NUS).

It is conducted by:

  • National University of Singapore (NUS) School of Computing Singapore
  • Amazon (AWS)

It is 15 days On Campus - internship programme, conducted at National University of Singapore, (NUS) School of Computing, Singapore.

It is an academic internship, which is a hands-on guided experience in an academic environment to hone industry-relevant skills and knowledge in a chosen domain with mentorship and training by university professor(s) and industry professional(s).

  • They will understand, experience and apply fundamental AI concepts.
  • They will appreciate the workings behind real life AI research and applications.
  • They will conceptualise, design and implement a social/ technological innovation project with expert inputs from faculty from NUS and professionals from Amazon.
  • They will get insights on higher education and the international university admission process and applications in industry.
  • They will develop a strong profile for international university admissions.

Our project managers will be able to guide you to the admissions department. You will need to explore opportunities on your own with the admissions department based on your area of passion and interest for higher studies.

Applying for AIYA

How do I apply?

You may download the Factsheet below or fill in the interest form. One of our counsellors will be in touch with you to take it forward and assist you with the enrollment. You may also enroll through our website and make the payment via a Debit/Credit card or Online Banking.

This program is open to all students from middle school and high school . This is an open programme that focuses on providing a better understanding of Artificial Intelligence, how it is changing our world, its benefits and usage in our everyday lives.

No, our course is specially designed to ensure that students from all streams and areas of study can easily understand the concepts taught at this internship.

Yes, students studying the ICSE, CBSE, IB MYP, IB DP, IGCSE curriculum can apply for the programme. Students can be of any nationality, ethnicity and belong to any country!

This is a fully-residential program. All students and visiting faculty will stay at the Hostel/ Hotel Accommodation in Singapore. NUS also has a large food court which serves food from 16 countries. Vegetarian and non-vegetarian both options are available.

Please refer the programme fee on the website. In addition to that, you should factor in the cost of flights, insurance and visa applications . Also, food expenses are an additional cost of approximately S$ 20 per day.

The academic internship schedule, date and time are subject to change based on the availability of professor(s) from NUS. Prior notice will be given to all concerned parties on the change of date and the best efforts will be made by Corporate Gurukul to accommodate all parties when deciding the revised schedule, date and time.

Academics

Is it a selection-based programme?

Yes

The selection criteria are as follows:

  1. Overall academic performance (grades) in current year or previous year
  2. Resume and Essays in Application Form

Payment

What is the last date to pay for the registration fees?

Please refer the Offer Letter issued to you.

  • Certificate of Completion from National University of Singapore, School of Computing (NUS)
  • Certificate of Completion from Amazon (AWS)
  • Individual Letter of Recommendations for top project group by NUS
  • Flights/Visa/Travel Insurance
  • Local Transport in India
  • International SIM Card
  • Local Transport in Singapore for personal visits/sightseeing
  • Entrance Fee to various tourist attractions in Singapore
  • Medical expenses in Singapore
  • Gym, laundry or any other hostel facility charges
  • Stay in Singapore before or after academic internship dates

Programme Completion Documents

Which certificate(s) will I get after completion of this academic internship?
  • Certificate of Completion by National University of Singapore (NUS)
  • Individual Letter of Recommendations for top project group from NUS

Issuance of all certificates and the LoR will be at Corporate Gurukul’s discretion subject to:

  1. the participant’s performance
  2. completion of assignments and
  3. 90% attendance during the academic internship

The certificates will be awarded during the Valedictory Ceremony at the end of the programme.

The LOR(s) will be posted to your respective institutes 60 days post the programme completion.

The hands-on sessions are conducted in the Lecture Theatre/Seminar Rooms. You will not visit any laboratories for the same unless requested by the professor(s).

Laptop with the specifications mentioned in ACADEMIC INTERNSHIP PREREQUISITES is MANDATORY for all participants.

No. The entire academic internship is delivered at NUS, Singapore.

Yes, you will be assigned teams for project work and assignments. These teams typically consist of 4-5 members.

No, the team members are assigned by Corporate Gurukul team in consultation with the professor(s). They are created keeping in mind that the prerequisite readiness and a proper mix of students from various institutions. Any request for choice of team member will not be entertained.

All assignments and projects will be submitted on the Learning Management System, Acadly.

Assignments will be given daily and need to be submitted as per specified deadline. You may be required to work on the assignments in teams or individually as per the nature of assignment.

You are required to complete one project during the duration of the programme. This will be done in teams.

The project presentations will be conducted post the NUS and Amazon sessions.

Yes, both NUS and Amazon (AWS) sessions are graded.

The MCQ Quiz will be conducted every day during the afternoon session.

Any late submissions of project, assignments or quizzes beyond specified deadline will carry negative marks.

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