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The research behind AI Ready

How we built AI Ready.

Before writing a single lesson, the WAC Centre for AI-Powered Learning compared 17 free AI courses and 10 free video series. This page shows what we compared, how we judged it, what we found, and the editorial standard every lesson now follows.

Curriculum v1.0Published 23 October 2026Change log

Step 1 · What we compared

17 free courses, side by side.

From no-code literacy courses to builder courses that need Python.

CourseProviderLevel and prerequisiteHands-onCost and certificateGroup
Elements of AI: Introduction to AIUniversity of Helsinki and MinnaLearnBeginner, no maths or codeLow: quizzes and written exercisesFree, free certificateConcepts
Elements of AI: Building AIUniversity of Helsinki and MinnaLearnBeginner, basic Python recommendedMedium: algorithm exercisesFreeTheory + code
Ethics of AIUniversity of HelsinkiBeginnerLow: reading and essaysFreeConcepts
YUVA AI for ALLIndiaAI Mission, MeitYBeginner, anyone (about 4.5 hours)LowFree, Government of India certificateConcepts
Google AI EssentialsGoogle (Coursera)Beginner, professionals (about 5 hours)Medium: prompting at workFree to audit, certificate paidConcepts
Introduction to Generative AI pathGoogle Cloud Skills BoostBeginnerLow: video and quizFree, completion badgesConcepts
AI Foundations, Applied AI Foundations, Agents and WorkflowsOpenAI AcademyBeginner to advancedHigh: build real workflowsFree with a ChatGPT accountSingle vendor
AI Fluency, AI Capabilities and Limitations, Claude 101Anthropic AcademyBeginnerMediumFree, email sign-upSingle vendor
Intro to MCP, Building with the Claude API, Agent SkillsAnthropic AcademyIntermediate, some codeHighFreeCode-first
AI for Everyone, Generative AI for EveryoneDeepLearning.AI (Coursera)Beginner, non-technicalLowFree to audit, certificate paidConcepts
DeepLearning.AI short coursesDeepLearning.AI with industry partnersBeginner to intermediate, light PythonHigh: in-browser notebooksFreeCode-first
Generative AI for BeginnersMicrosoft (GitHub)Beginner coder (21 lessons)High: build lessons in Python or TypeScriptFree, open sourceCode-first
AI for Beginners, ML for Beginners, AI Agents and MCP for BeginnersMicrosoft (GitHub)Beginner to intermediate coderHighFree, open sourceCode-first
AI Agents Course, LLM Course, MCP CourseHugging FaceIntermediate, PythonHigh: Spaces and challengesFree, free certificates (Agents)Code-first
Kaggle LearnKaggle (Google)Beginner coderHigh: in-browser notebooksFree, free certificatesCode-first
Practical Deep Learning for Codersfast.aiIntermediate, PythonHigh: train and deploy modelsFreeCode-first
CS50's Introduction to AI with PythonHarvard (edX, YouTube)Intermediate, PythonHigh: graded projectsFree, verified certificate paidCode-first

Hours, prerequisites and certificate terms are taken from each provider’s own pages as of October 2026 and change often. “Free to audit” means the content is free and the certificate is paid.

And 10 free video series

Andrej Karpathy: Intro to Large Language ModelsWhat an LLM is, training, limits and security, in plain language
Andrej Karpathy: How I use LLMsA practitioner’s tour of chat, search, research, files, code, voice and images
Andrej Karpathy: Deep Dive into LLMs like ChatGPTPre-training, fine-tuning, human feedback, hallucinations and reasoning
3Blue1Brown: Neural Networks seriesVisual intuition for neural networks, GPT and attention
StatQuest with Josh StarmerStatistics and machine learning basics without heavy maths
Google Cloud Tech: Introduction to Generative AIA 20-minute overview of generative AI and LLMs
CS50 AI lectures (Harvard)Search, knowledge, uncertainty, ML, neural networks and language
MIT 6.S191 Introduction to Deep LearningUpdated every year, with labs
CampusX and Krish NaikHindi and Indian-English project tutorials for Indian students
fast.ai lecturesBuild working models first, theory later
Step 2 · How we judged them

Six questions for every course.

Code required

Can a non-programmer complete it, or does it start with Python?

Hands-on output

Does the learner finish with something they built, or only a quiz score?

Vendor neutrality

Does it teach how to choose between tools, or one company’s product?

Currency

Does it cover agents, MCP, deep research, reasoning and on-device models?

What the credential proves

Skill, assessed on real work, or simply completion?

Local context

Indian languages, India’s data protection law and local job roles?

Step 3 · What we found

Plenty of free AI courses. One clear gap.

Concept courses explain AI but rarely let you use it. Builder courses are practical but start with Python. The practical no-code options each teach one company’s product. Nobody offered a vendor-neutral, no-code, hands-on course with a skills assessment.

Practical, no code
  • AI Ready (WAC)
  • OpenAI Academy
  • Anthropic AI Fluency
Until now: single-vendor only
Practical, code-first
  • Microsoft courses
  • Hugging Face
  • fast.ai
  • CS50 AI
  • Kaggle Learn
Concepts, little practice
  • Elements of AI
  • Google AI Essentials
  • AI for Everyone
  • YUVA AI for ALL
Theory with some code
  • Elements of AI: Building AI
↑ More hands-onMore code required →Positions are an editorial judgement from each course’s own description, not a measurement.
  • One tool, one vendorCompany academies teach their own assistant. We use many.
  • Theory, no practiceLiteracy courses end in a quiz. Ours end in something built.
  • The code wallAgents and automation were for coders. Not any more.
  • Courses age fastOurs is a living course: updated whenever AI changes, free for life.
  • Attendance certificatesWe assess real work, not completion.
  • Tools without judgementChecking AI output is practised in every module.
Step 4 · What we decided

Six design principles, from the evidence.

  1. 01No code first, depth later

    The core course needs no programming. The optional Builder track adds Python for those who want it.

  2. 02Every module ships real work

    Watch, try, ship, check. About 70% of the time is hands-on, and each module adds one piece to a portfolio.

  3. 03Many tools, three essentials

    Learners master one assistant, one research tool and one creation tool, and learn to judge the rest.

  4. 04Verification in every module

    Checking AI output, sources and citations is practised throughout, not left to one lesson.

  5. 05Assess skill, not attendance

    The certificate is awarded on a portfolio scored for tool choice, prompt and context quality, verification and responsible use.

  6. 06Living curriculum

    Lessons are dated, reviewed and rewritten when tools change. Every change is logged below.

Our editorial standard

How every lesson is researched and checked.

Simple teaching on the surface, a visible research process underneath.

Facts carry sources

Every ebook chapter ends with its sources. Statistics, dates, laws, research findings and named cases are cited to a primary source or a reputable publication.

Three kinds of statement

Lessons separate verified facts (cited), good practice (labelled “Good to know”, based on how the tools work) and illustrations (labelled as examples or cases, with invented names).

Screenshots

Captured from the tools’ public pages, with no personal data, and dated. They are retaken when an interface changes.

Free tiers

Every lab must work on free plans. Free-tier limits and regional availability are rechecked at every review, and each lab offers an alternative tool.

Review cycle

Each chapter carries a review record with its version and last-reviewed date. Tools and screenshots are checked every month; the agents module every quarter; every lesson at least twice a year.

Corrections

Anyone can report an error to connect@worldassessmentcouncil.org. Confirmed corrections are made in the lesson and recorded in the change log.

No undisclosed sponsorship

No tool maker pays for inclusion. Tool names belong to their owners; WAC is not affiliated with them.

Review record

Every published lesson, dated.

LessonTitleVersionLast reviewedSources
0.1What AI is, and is not, in 20261.023 October 20264
0.2The AI landscape: assistants, agents, open models1.023 October 20268
0.3Set up your free AI accounts the smart way1.023 October 20265
0.4Privacy and safety: what never to paste1.023 October 20265
0.5Lab 0: test three assistants and score them1.023 October 20262
1.1From rules to learning: how AI got here1.023 October 20266
1.2Data, models and predictions1.023 October 20264
1.3Neural networks, intuitively1.023 October 20264
1.4How large language models write: one token at a time1.023 October 20265
1.5Context windows, memory and reasoning models1.023 October 20265
1.6Why AI makes things up, and how to stop it fooling you1.023 October 20266

Modules 2 to 8 and the capstone are in production and will appear here as they are published. Each chapter’s full source list is printed at the end of the chapter. Download the sample lesson to see one.

Change log

Every change, on the record.

Newest first. Registered learners are told about updates by email.

  1. PublishedModules 0 and 1: 11 lessons, each with slides, a lecture script and an ebook chapter
  2. AddedSources and a review record at the end of every ebook chapter
  3. ExpandedModule 3: studying with AI, academic integrity and defending your own work
  4. PublishedHow we built AI Ready: the course comparison, editorial policy and this log
  5. LaunchedCurriculum v1.0 and the course page
Start free See the curriculum