For individuals
- Build practical AI fluency, without needing a coding or engineering background
- Stay relevant as AI becomes a core professional capability
- Earn MIT Open Learning credentials as they progress
Delivered in India in cooperation with Masai.
Universal AI Program by
MIT Open Learning
Learn from MIT faculty and domain experts across 20+ modules built around real-world AI applications. No coding background required.
- Self-paced
- Start anytime
- Begin with one module
- MIT Open Learning credentials
For students, professionals, managers, entrepreneurs, and
decision-makers navigating an AI-first world.
AI fluency is becoming a core professional capability.
AI is already transforming how organizations operate, compete, and hire. The advantage increasingly belongs to professionals who understand how to apply it.
- 90%
of organizations will grapple with AI
skills shortages by 2026McKinsey, State of AI, November 2025
- 56%
wage premium for AI skills, comparing workers in the same job
PwC, Global AI Jobs Barometer, June 2025
- 88%
of organizations reported using AI in some capacity within their business
McKinsey, State of AI, November 2025
- 39%
of enterprise leaders report a lack
of appropriate AI educationIDC Analyst Brief, February 2025
Designed by MIT.
Built for the way professionals learn.
Learn to understand AI, evaluate its possibilities, apply it in real-world contexts, and make better decisions with it without needing a coding or engineering background.
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From foundations to applications
Build core AI understanding first, then explore how AI is applied across business, climate, energy, biology and other domains.
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Learn through real-world cases
Understand complex AI concepts through practical examples and recognizable real-world situations.
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Built for diverse backgrounds
Designed for professionals across functions and industries. No coding or engineering background required.
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AI-powered support when you need it
AskTIM, integrated into MIT Learn, helps answer questions, clarify concepts and guide you through challenging topics.
Learn the technology shaping tomorrow. Become the person shaping what comes next.
Build the fluency to evaluate AI, make informed decisions and apply it across real-world contexts, across functions, industries and career paths. No prior coding experience required.
Who is Universal AI for?
Universal AI is a flexible curriculum designed for individual learners, universities, and companies.
For universities
- Explore cutting-edge AI research and insights to stay ahead in the field
- Enhance your curriculum by addressing gaps and providing comprehensive learning
- Offer elective courses for deeper exploration of specialized topics
How You Learn on the
MIT Learn Platform
Learn at your own pace through short lectures, guided exercises, assessments, and AI-powered support designed to help you understand and apply concepts.
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AI-powered learning support
AskTIM helps clarify concepts, answer questions, and guide you through challenging topics while encouraging independent thinking.
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Assessments that reinforce learning
Built-in knowledge checks and assignments help you test your understanding and track your progress.
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Guided real-world exercises
Apply concepts through exercises developed by MIT teaching teams, with supporting code provided where required.
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Structured modules
Each module combines lectures, knowledge checks, guided exercises, and assignments into a clear learning journey.
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Learn on your schedule
Progress at your own pace, revisit concepts when needed, and fit learning around your work and other commitments.
Modular, stackable learning
16 foundational modules build core AI understanding. Industry specific modules show how AI is applied in specific domains. Complete individual modules to earn credentials and stack your learning toward a broader MIT Open Learning certificate.
Module 01 4–8 lectures 1–2 guided exercises
Python Coding Part 1
- How software logic behaves, built through small Python programs traced step by step.
- Enough fluency to read, interpret, and debug code at a high level, including code an AI wrote for you.
Module 02 4–8 lectures 1–2 guided exercises
Python Coding Part 2
- Structured data with CSV and Pandas, and visualisation that turns it into a decision.
- Intuition for entry-level models like decision trees, and how overfitting quietly destroys reliability.
Module 03 4–8 lectures 1–2 guided exercises
Foundations of Data Analytics and Machine Learning
- The analytics workflow end to end, from collection to communicating the result.
- Foundational statistics and their role in evidence-based decisioning.
Module 04 4–8 lectures 1–2 guided exercises
Supervised and Unsupervised Learning
- How regression, classification, and clustering map onto problems you already recognise.
- Reading model quality through accuracy and AUC, and explaining it to someone who didn't build the model.
Module 05 4–8 lectures 1–2 guided exercises
Foundations of Neural Networks
- What a neural network is, why it performs on unstructured text and images, and the conditions under which it fails.
- Embeddings as intuition: how these systems represent meaning.
Module 06 4–8 lectures 1–2 guided exercises
Hands-on Deep Learning
- The training mechanics: forward and backward propagation, gradient descent, and what actually moves outcomes.
- A guided end-to-end pass in a current framework.
Module 07 4–8 lectures 1–2 guided exercises
Deep Learning and Computer Vision
- Vision in production: detection, classification, automated visual inspection.
- Why CNNs and transfer learning compress delivery timelines.
Module 08 4–8 lectures 1–2 guided exercises
Data-Driven Prescriptive AI
- Prediction (“what will happen”) versus prescription (“what should we do”), and why the distinction reshapes operations.
- Wiring ML predictions into decision logic at scale.
Module 09 4–8 lectures 1–2 guided exercises
Model-Driven Prescriptive AI Part 1
- How optimisation models are built: objectives, constraints, feasibility.
- Applying that to routing, staffing, allocation, and resource planning.
Module 10 4–8 lectures 1–2 guided exercises
Model-Driven Prescriptive AI Part 2
- Multi-objective trade-offs in practice: cost against speed against quality.
- Why real optimisation gets messy, and how teams handle it pragmatically.
Module 11 4–8 lectures 1–2 guided exercises
Large Language Models
- How LLMs work at a usable level: tokens, transformers, attention, and what drives output quality.
- Prompting strategy alongside the real risks of hallucination, bias, and cost.
Module 12 4–8 lectures 1–2 guided exercises
Generative AI, the Future of Work, and Human Creativity
- Where generative systems add value in knowledge work, and where human judgment stays non-negotiable.
- The machinery underneath, and how to assess business fit.
Module 13 4–8 lectures 1–2 guided exercises
Multimodal AI
- How systems reading text, images, and other signals together outperform any single channel.
- The hard parts at scale: alignment, fusion, interpretability, feasibility.
Module 14 4–8 lectures 1–2 guided exercises
Explanation, Reasoning, and AI Ethics
- How to explain AI outcomes to stakeholders using practical interpretability approaches: global, local, and counterfactual.
- Responsible-AI fundamentals: bias, fairness trade-offs, and mitigation strategies aligned with governance.
Module 15 4–8 lectures 1–2 guided exercises
Explainability and Fairness
- Understanding, interpreting, and clearly communicating how an AI system reached a decision.
- Where bias enters, what fairness trade-offs cost, and mitigation that fits governance.
Module 16 4–8 lectures 1–2 guided exercises
AI & Ethics
- The principles that keep AI systems designed, developed, and deployed responsibly, fairly, and transparently.
- Alignment with human values as a design constraint rather than an afterthought.
Choose your industry specific module
Select the field most relevant to your work, interests, or career goals. Complete an industry specific vertical module to earn a certificate.
Healthcare
AI and Precision Medicine
How AI reads imaging and patient data to tailor diagnosis and treatment to the individual.
Healthcare
Holistic AI in Medicine
AI across the whole care pathway — where it helps, where it must not decide alone.
Transportation and Logistics
AI for Transportation: From Concepts to Implementation
Routing, demand forecasting, and network design for moving people and goods.
Sustainability
AI and Sustainability: Transportation
Cutting emissions and waste across freight, transit, and last-mile networks.
Sustainability
AI and Sustainability: Energy
Forecasting demand, balancing grids, and integrating renewables at scale.
Business and Entrepreneurship
AI and Entrepreneurship
Using AI to find the opportunity, test it fast, and build a venture around it.
Business and Entrepreneurship
AI and Finance
Risk, pricing, fraud, and portfolio decisions — and where models mislead.
Governance
Ethical AI for Decisions in Today's World
Governance, accountability, and the judgment calls AI cannot make for you.
Meet the faculty behind the program
Learn from MIT instructors bringing expertise across AI, data, technology, and industry applications.
faculty
Dimitris Bertsimas
Vice Provost for Open Learning, MIT Open Learning
Dimitris Bertsimas is the Vice Provost for Open Learning at MIT, the Associate Dean of Business Analytics, the Boeing Leaders for Global Operations Professor of Management, and a Professor of Operations Research at MIT Sloan School of Management. At MIT Open Learning, he oversees Open Learning’s product offerings, new initiatives, infrastructure, finances, and operations.
faculty
John Guttag
Vice Provost for Open Learning, MIT Open Learning
Professor Guttag is the Dugald C. Jackson Professor at MIT, leading the Data Driven Medical Research Group. They apply computational techniques to medicine, focusing on predicting medical events and patient responses. He has researched data networking, sports analytics, and software engineering. Guttag earned his bachelor's in English and master's in applied mathematics from Brown University, and his doctorate from the University of Toronto. He was Head of MIT’s Electrical Engineering and Computer Science Department from 1999 to 2004 and is a Fellow of the ACM.
faculty
Eric Grimson
Professor of Medical Engineering and Computer Science
W. Eric L. Grimson is a Professor of Computer Science and Engineering and the Bernard M. Gordon Professor of Medical Engineering. He was Chancellor of MIT from 2011 to 2014. A faculty member since 1984, he has served as head of the Electrical Engineering and Computer Science Department and as its education officer. Grimson is known for his research in computer vision, particularly in medical image analysis. He has developed techniques for activity recognition, object recognition, image indexing, and image-guided surgery. He has taught over 10,000 undergraduates and supervised nearly 50 PhDs. Grimson is from Saskatchewan, Canada, and received his BSc from the University of Regina and his PhD from MIT. He has received the Bose Award for Excellence in Teaching and is a fellow of AAAI and IEEE.
faculty
Ana Bell
Senior Lecturer, Computer Science and Electrical Engineering
I am a part of the EECS department at MIT, where I've been teaching introductory computer science since 2013. I received my Bachelor in Applied Science from the University of British Columbia in Vancouver, Canada. I received my MA and PhD from Princeton University, where I did research in computational biology. I discovered my passion for teaching after being appointed as a teaching assistant for two semesters for Introduction to Computer Science, at Princeton University. Since then, I've sought any opportunity to introduce students to the wonderful world of computer science
faculty
Ana Trisovic
Research Scientist, FutureTech Lab
Ana Trisovic is a Research Scientist at MIT's FutureTech Lab, focusing on the economic impact of computing trends, applications of graph neural networks in social sciences, and the reproducibility of research methods. She earned her Ph.D. in Computer Science from the University of Cambridge and CERN, contributing to the LHCb Experiment and Open Data initiatives. Prior to MIT, she worked as a Research Associate at Harvard School of Public Health and held postdoctoral positions at the Institute of Quantitative Social Science and the University of Chicago, collaborating with EPIC and the University Library. Her work has been published in journals like Nature Physics and Harvard Data Science Review.
faculty
Rama Ramakrishnan
Professor of the Practice, AI/ML, MIT Sloan School of Management.
Rama Ramakrishnan’s teaching, research, and advisory interests center on the application of AI/ML techniques to problems and opportunities in industry. Rama is a passionate AI/ML educator and was awarded Sloan's most prestigious teaching award, the Jamieson Prize for Excellence in Teaching, in 2025 and MIT's Teaching with Digital Technology Award in 2024. He strongly believes that AI knowledge should be accessible to everyone and shares his expository work at https://ramakrishnan.com.
faculty
Alexandre Jacquillat
Maurice F. Strong Career Development Associate Professor, Associate Professor of Operations Research and Statistics, MIT Sloan School of Management.
Alexandre Jacquillat’s research focuses on data-driven decision-making, spanning stochastic optimization, integer optimization, large-scale optimization, and machine learning. In particular, his research develops scalable optimization models and algorithms to support more efficient, equitable, and sustainable operations—with a particular interest in air traffic management, urban mobility, decarbonization, and other social good applications.
Credentials that stack
Earn recognition at every stage of your learning journey. Complete individual modules, build the full AI foundation, and add an industry specific module to earn progressively broader MIT Open Learning credentials.
Invest now. Build AI fluency.
all inclusive fee
₹9,999 (inclusive of GST)
- Certification from MIT Open Learning
- 16+ foundational and industry specific modules
- Flexible self-paced learning
- Dedicated AI support - AskTIM
On completion, here is what you gain
Frequently asked questions
What is Universal AI from MIT Open Learning? + −
Universal AI is an online, self-paced learning program designed to help learners build a strong understanding of artificial intelligence from core concepts and technologies to real-world applications across industries. The program is designed to make AI rigorous yet accessible, including for learners without a technical or coding background.
Who is Universal AI for? + −
Universal AI is a flexible curriculum designed for everyone: individual learners and universities. No coding background is needed to get started.
For individuals looking to:
• Build practical AI fluency, without needing a coding or engineering background
• Stay relevant as AI becomes a core professional capability
• Earn MIT Open Learning credentials as they progress
For universities looking to:
• Access the latest AI research and knowledge
• Complement and fill curriculum gaps
• Offer elective or add-on programs to students
For individuals looking to:
• Build practical AI fluency, without needing a coding or engineering background
• Stay relevant as AI becomes a core professional capability
• Earn MIT Open Learning credentials as they progress
For universities looking to:
• Access the latest AI research and knowledge
• Complement and fill curriculum gaps
• Offer elective or add-on programs to students
What is the age requirement for Universal AI? + −
Universal AI is open to learners aged 18 and above.
Do I need a technical or coding background? + −
No. You do not need prior experience in programming, AI, or data science to enrol. The program is specifically designed to be accessible to learners from technical as well as non-technical backgrounds. You will encounter Python and coding notebooks during the program, but the objective is not to turn you into a software developer. They are used to help you interpret AI outputs and understand how AI systems work in practice.
How is Universal AI different from short AI or GenAI courses? + −
Many short AI courses concentrate primarily on a particular tool, platform, or current trend. Universal AI is designed to build broader and more durable AI fluency beginning with foundations and progressively moving into areas such as machine learning, deep learning, large language models, generative AI, ethics, and industry applications. The objective is not simply to teach learners how to use today's AI tools, but to help them understand, evaluate, and apply AI as the technology continues to evolve.
Is Universal AI relevant for working professionals? + −
Yes. The self-paced and asynchronous structure allows professionals to fit learning around their work schedule. The curriculum is also designed around real-world AI applications rather than requiring learners to approach AI purely from a computer science perspective.
Can business leaders and managers benefit from the program? + −
Yes. The program can help managers and business leaders develop the AI fluency needed to better evaluate AI opportunities, understand limitations and risks, communicate with technical teams, and make more informed decisions about the use of AI. Universal AI specifically focuses on understanding and applying AI across real-world contexts, rather than requiring every learner to become an AI developer.
Can entrepreneurs and founders benefit from Universal AI? + −
Yes. MIT Open Learning's AI and Entrepreneurship course explores how AI can be applied across the process of building a company, including idea validation, product development, team building, scaling, and strategic decision-making. It also examines how entrepreneurs can balance rapidly evolving AI capabilities with human judgement, creativity, and leadership.
Is Universal AI a degree or academic-credit program? + −
No. Universal AI is an online professional learning program from MIT Open Learning. It is not an MIT degree or admission to an MIT academic program.
What will I learn? + −
The program helps you build the ability to:
• Understand different types of AI, their concepts, use cases, and applications
• Interpret and analyse outputs from AI systems
• Evaluate AI applications with an informed and critical perspective
• Apply AI frameworks to real-world decisions and challenges
• Understand ethical implications and risks associated with AI
• Communicate confidently about AI with technical and non-technical audiences
The emphasis is on developing durable AI fluency, rather than learning only the latest AI tools.
• Understand different types of AI, their concepts, use cases, and applications
• Interpret and analyse outputs from AI systems
• Evaluate AI applications with an informed and critical perspective
• Apply AI frameworks to real-world decisions and challenges
• Understand ethical implications and risks associated with AI
• Communicate confidently about AI with technical and non-technical audiences
The emphasis is on developing durable AI fluency, rather than learning only the latest AI tools.
How is the program structured? + −
Universal AI follows a modular learning structure. The current full program consists of five required foundation courses plus one industry-specific vertical/elective course. The foundations build broad AI understanding, while the industry courses allow you to explore applications relevant to particular sectors or professional interests.
What does a typical module look like? + −
Each Universal AI module is a focused learning experience covering a specific AI topic. A module typically includes:
• 4–8 lectures
• Knowledge checks
• Guided exercises
• Assignments
• Access to AskTIM for learning support
An individual module typically requires approximately 7–10 hours of learner effort, although you can progress at your own pace.
• 4–8 lectures
• Knowledge checks
• Guided exercises
• Assignments
• Access to AskTIM for learning support
An individual module typically requires approximately 7–10 hours of learner effort, although you can progress at your own pace.
Is the program mainly theoretical, or will I apply what I learn? + −
The program combines conceptual understanding with practical application. Learners work through real-world cases, knowledge checks, assignments, and guided exercises designed to connect AI concepts with practical situations. MIT describes the program as deliberately bridging the gap between highly technical AI education and surface-level introductions to AI tools.
Are there hands-on exercises? + −
Yes. Guided exercises accompany the learning modules and help you apply concepts introduced in the lectures to practical examples. Where coding is involved, supporting code is provided so that learners can focus on understanding the concepts and interpreting the results rather than having to build everything from scratch.
Can I choose an AI specialization relevant to my career? + −
Yes. After building your AI foundation, you can explore industry-focused courses covering areas such as healthcare, entrepreneurship, transportation, sustainability, and other domains. These industry courses can help you understand how AI is being applied to problems and decisions relevant to particular sectors.
Can I take an industry specialization without completing the entire foundation? + −
Yes. Individual industry-specific courses can be taken independently. However, the requirements for earning the overall Universal AI Program Certificate are different from those for completing an individual course. Learners should choose their pathway based on the credential they ultimately want to earn.
How does Universal AI make complex AI concepts easier to understand? + −
The program uses real-world cases and applications to explain AI concepts instead of relying solely on mathematical derivations. This allows learners to build conceptual depth while making the material approachable to people from different academic and professional backgrounds.
Is the program self-paced? + −
Yes. Universal AI is fully online, self-paced, and asynchronous. You can study when it works for you rather than having to attend a fixed class schedule. This makes the program particularly suitable for working professionals balancing learning with their existing responsibilities.
How much time should I plan to spend on the full program? + −
MIT Learn currently estimates approximately 15–20 weeks, with around 7–10 hours of learning per week, for the full program pathway. Because the program is self-paced, your actual completion time will depend on your schedule and learning pace.
What is AskTIM? + −
AskTIM is an MIT-developed AI assistant integrated into the Universal AI learning experience. You can use AskTIM to:
• Ask questions about lectures and concepts
• Clarify topics you find challenging
• Get help with exercises and assignments
• Reinforce your understanding
• Navigate learning resources
• Identify learning pathways relevant to your goals
AskTIM is designed to support your thinking rather than simply give you answers.
• Ask questions about lectures and concepts
• Clarify topics you find challenging
• Get help with exercises and assignments
• Reinforce your understanding
• Navigate learning resources
• Identify learning pathways relevant to your goals
AskTIM is designed to support your thinking rather than simply give you answers.
How long will I have access to Universal AI? + −
Your Universal AI subscription is valid for 12 months from the date your learner account is activated. The subscription fee is payable in advance, and the subscription cannot be cancelled. The course fee is non-refundable.
Can I access Universal AI while travelling outside India? + −
Universal AI is offered to learners based in India. You should normally access the program while you are in India. MIT Open Learning may verify a learner's location or residency status where required.
What is Universal AI? + −
Universal AI (UAI) is an online AI learning experience from MIT, hosted on the MIT Learn platform. It includes:
• Foundational AI learning modules
• Industry and domain-specific modules
• GPT-powered learning support
• Assessments and knowledge checks
• Access to Universal AI learning content
The program is designed to help learners understand the underlying concepts, technologies, and real-world applications of artificial intelligence.
• Foundational AI learning modules
• Industry and domain-specific modules
• GPT-powered learning support
• Assessments and knowledge checks
• Access to Universal AI learning content
The program is designed to help learners understand the underlying concepts, technologies, and real-world applications of artificial intelligence.
Where will I access the Universal AI program? + −
Universal AI is hosted on the MIT Learn platform. Once your learner account is activated, you will receive access to the relevant Universal AI learning modules through the platform.
What happens if I do not complete the program within one year? + −
After the one-year subscription period, you retain access to the purchased learning content available to you at that point. However, you will no longer be able to complete coursework for additional Universal AI certificates, use subscription-limited features such as AskTIM, or receive newly released content and updates under that purchase.
Will I earn a certificate? + −
Yes, eligible paid learning pathways offer stackable MIT Open Learning credentials. Depending on the pathway completed, learners can earn credentials at different milestones, including module-, course-, and program-level certificates.
How do the credentials stack? + −
Think of the learning journey as progressive:
• Module Credential — Complete an eligible individual module.
• Course / Foundation Credential — Complete the required modules within the relevant course or foundation pathway.
• Universal AI Program Certificate — Complete the requirements of the full Universal AI Program pathway.
• Module Credential — Complete an eligible individual module.
• Course / Foundation Credential — Complete the required modules within the relevant course or foundation pathway.
• Universal AI Program Certificate — Complete the requirements of the full Universal AI Program pathway.
Who issues my certificate: MIT Open Learning or Masai? + −
MIT Open Learning issues the certificate. Masai supports the delivery and learner experience in India, while the credential is issued by MIT Open Learning upon successful completion of the applicable requirements.
Will I become an MIT alumnus after completing Universal AI? + −
No. Completing the Universal AI program does not confer MIT alumni status. Learners receive the applicable MIT Open Learning credential based on the learning pathway they successfully complete.
Who will deliver and support Universal AI in India? + −
Masai is the reseller of Universal AI in India. Masai supports the delivery and learner experience for Indian learners, while the program content and credentials are provided by MIT Open Learning.
Will Masai provide learner support in addition to the MIT Learn platform? + −
Yes. Masai provides learner support for programme, enrolment, certificate, and general learner assistance reachable via WhatsApp or email (uai-support@mit.edu). For institutions and partnership enquiries, Masai can be reached at partnerships@masaischool.com.
What is included in the program fee? + −
Program access, credentials, AskTIM access, Masai support, applicable taxes, etc.
Can I pay the program fee in instalments or through EMI? + −
No. EMI or instalment payment options are currently not available. The full program fee must be paid at the time of enrolment.
Is the Universal AI subscription refundable? + −
The subscription fee is payable in advance, and the subscription can be revoked but will not be refunded. The course fee is non-refundable.
When can I start learning after I enrol? + −
You can start learning once your enrolment and payment are successfully completed. Universal AI is self-paced, so there is no cohort or batch start date to wait for. You can begin learning as soon as access is activated and progress at your own pace.
Who should I contact if I face a technical issue on the MIT Learn platform? + −
MIT provides technical and content-related support for the Universal AI learning platform. For issues related to:
• Accessing the MIT Learn platform
• Universal AI course content
• System or technical issues
• Content-related questions
You can contact masai-uai-support@mit.edu. MIT Open Learning support is available on weekdays from 9:00 AM to 5:00 PM Eastern Time, excluding U.S. Federal Holidays. MIT will make reasonable efforts to respond to support requests within 3 business days.
• Accessing the MIT Learn platform
• Universal AI course content
• System or technical issues
• Content-related questions
You can contact masai-uai-support@mit.edu. MIT Open Learning support is available on weekdays from 9:00 AM to 5:00 PM Eastern Time, excluding U.S. Federal Holidays. MIT will make reasonable efforts to respond to support requests within 3 business days.
Who should I contact for other learner-related queries? + −
For queries not related to the MIT Learn platform or Universal AI course content, the Masai learner support team will assist you. This can include areas such as enrolment, payments, onboarding, and other learner-support requirements.
What kind of platform availability can I expect? + −
MIT Learn is designed to provide reliable access to Universal AI, with a target platform availability of 99% measured on a monthly basis. Occasional interruptions may occur for maintenance, upgrades, or technical reasons.
Does the program provide placement or career-assistance services? + −
No. Universal AI is a professional learning program focused on building AI fluency and practical understanding. It does not include placement assistance, job guarantees, or career-placement services.
Contact us
We're here to help you make the most
of your Universal AI journey.
For individual learners
For inquiries about enrolment and general assistance.
For colleges & universities
For colleges looking to enrol their students for the program.
MIT Learn Platform Support
uai-support@mit.edu
For MIT Learn platform-specific technical or content issues only
- Monday - Friday
- 9:00 AM - 5:00 PM Eastern Time (USA)
- Response within 3 business days