A practical path for teachers, student learners, student entrepreneurs, adult up-skillers, and solo pros—focused on quick wins, ethical use, and portfolio-ready projects.

Below is a structured roadmap, tool stack, and starter projects—with links, “Getting Started” steps, and copy-ready prompts you can paste into your favorite AI assistant.

1) The Basic Roadmap

1. Mathematics for AI 

• Focus: stats & probability, linear algebra, calculus, optimization intuition.

• Great primers or quick refreshers:

  • Khan Academy:

    • Statistics,

    • Linear Algebra

  • 3Blue1Brown:

    • Essence of Linear Algebra

• Do this first (2–4 hrs): Review mean/variance, vectors/matrices, gradients; practice in a notebook.

COPY this Prompt — for study plan

Create a 2-week micro-syllabus to review stats/probability and linear algebra for machine learning, with 30-minute daily exercises and one small project each week.

2. Programming Fundamentals

• Languages/Libraries:

  • Python: python.org

  • NumPy: numpy.org · Pandas: pandas.pydata.org · Matplotlib: matplotlib.org

  • R (optional): r-project.org

  • SQL basics: Mode SQL Tutorial

  • Version control (Git): git-scm.com

• Getting started: Set up a notebook workflow with Jupyter (jupyter.org) or Google Colab (colab.research.google.com).

Copy Prompt — for your First Notebook Setup

Outline step-by-step instructions to set up a Python + Jupyter/Colab workflow for data analysis and ML on a new laptop, including package list and test cells.

3. Big Data Tools (optional, choose what fits)

• Ecosystem:

  • Hadoop (hadoop.apache.org)

  • Spark (spark.apache.org)

  • Cassandra (cassandra.apache.org)

  • MongoDB (mongodb.com)

  • Kafka (kafka.apache.org).

• Tip: If you’re teaching or prototyping, you can skip Hadoop and start with small datasets + Pandas or DuckDB (duckdb.org).

4. Data Engineering Essentials

• ETL/ELT & Warehouses:

  • Snowflake (snowflake.com)

  • BigQuery (cloud.google.com/bigquery)

  • PostgreSQL (postgresql.org).

• Pipelines & APIs:

  • FastAPI (fastapi.tiangolo.com)

  • Airbyte (airbyte.com).

• Cloud basics:

  • AWS (aws.amazon.com)

  • GCP (cloud.google.com)

  • Azure (azure.microsoft.com)

5. Data Science (turn data into insight)

• Workflow:

  • cleaning → EDA → feature engineering → modeling → evaluation → visualization

• Viz:

  • Plotly (plotly.com/python)

  • Power BI (powerbi.microsoft.com)

  • Tableau (tableau.com)

Copy Prompt — for your First Mini Project

Give me a beginner mini-project using a public dataset: steps for EDA in Pandas, a baseline scikit-learn model, simple evaluation metrics, and a short report template.

2) Core AI Skills (what you’ll actually build)

Machine Learning (ML)

• What: learn from historical data (classification, regression, clustering).

• Tools:

  • scikit-learn (scikit-learn.org)

  • XGBoost (xgboost.ai)

  • LightGBM (lightgbm.readthedocs.io).

• Starter project: Predict student outcomes or event attendance with a simple tabular dataset.

Deep Learning (DL) & Neural Networks

• What: multilayer neural nets for text, images, audio, tabular.

• Tools:

  • PyTorch (pytorch.org)

  • TensorFlow (tensorflow.org)

  • Keras (keras.io).

• Starter project: Image classifier on CIFAR-10 or flowers.

NLP (Natural Language Processing)

• What: text classification, summarization, Q&A, chat.

• Tools: HuggingFace Transformers (huggingface.co/transformers).

• Starter project: FAQ chatbot for your class, club, or small business.

Computer Vision

• What: classification, detection, segmentation.

• Data:

  • COCO (cocodataset.org)

  • ImageNet (image-net.org).

• Starter project: Detect equipment in lab photos or count inventory items.

Reinforcement Learning (RL)

• What: learn via actions & rewards.

• Tools:

  • Gymnasium (OpenAI Gym successor) (gymlibrary.dev)

  • Ray RLlib (docs.ray.io/en/latest/rllib).

• Starter project: Teach an agent to balance a cart-pole.

Generative AI (GenAI)

• Text LLMs:

  • ChatGPT/GPT-4 (openai.com)

  • Claude (claude.ai)

  • Gemini (gemini.google.com)

  • Llama (ai.meta.com/llama).

• Images/Audio/Video:

  • DALL·E (openai.com/dall-e-3)

  • Midjourney (midjourney.com)

  • Stable Diffusion (stability.ai).

• Starter project: Auto-draft lesson plans, marketing copy, or study notes.

Deployment, MLOps, & Explainability

• Ship it:

  • Streamlit (streamlit.io)

  • Gradio (gradio.app)

  • Docker (docker.com)

  • Kubernetes (kubernetes.io).

• Track it:

  • MLflow (mlflow.org)

  • Weights & Biases (wandb.ai)

  • DVC (dvc.org).

• Explain it:

  • SHAP (shap.readthedocs.io)

  • LIME (github.com/marcotcr/lime).

• Starter project: Deploy a sentiment app that explains predictions with SHAP.

Generative AI, Deployment, & Explainability

• LLMs:

  • ChatGPT/GPT-4

  • Claude

  • Gemini

  • Llama

• Apps:

  • Streamlit

  • Gradio

• MLOps:

  • MLflow

  • Weights & Biases

• XAI:

  • SHAP

  • LIME

Copy Prompt — Deploy a Simple App

Create a Streamlit plan for a sentiment-analysis demo using scikit-learn, with upload box for CSVs, prediction display, and SHAP explanations. Include deployment steps.

3) AI in a Nutshell (super-short glossary)

• AI: broad field of making machines “smart” (NLP, CV, robotics).

• Machine Learning: algorithms that learn from data (supervised/unsupervised).

• Deep Learning: neural networks (CNNs, RNNs, Transformers).

• Neural Networks: layers of “neurons” that learn representations.

• Generative AI: models that create text, images, audio, code.

4) Core Concepts—Explained Quickly

• Transfer Learning
  • Definition: Reuse a pretrained model’s learned features and fine-tune it on your (usually smaller) dataset.
  • Usage: Text classification with BERT/DistilBERT; image tasks with MobileNet/ResNet; audio with wav2vec; adapters/LoRA for low-compute fine-tuning.
  • Why it matters: Much faster training, fewer labels needed, often higher accuracy than training from scratch.
  • Starter idea: Fine-tune a small transformer to tag forum posts from your Discussions (e.g., “question,” “resource,” “project”).
  • Watch-outs: Domain shift, overfitting during fine-tune, and license/usage terms of the base model.
• Supervised vs. Unsupervised Learning
  • Definition: Supervised learns from labeled inputs→outputs (predict y from X). Unsupervised finds structure in unlabeled data (clusters, embeddings).
  • Usage: Supervised for grading assistance, risk/lead scoring, image/text classification; Unsupervised for segmentation, anomaly detection, topic discovery.
  • Choosing: If you have labels tied to an outcome, start supervised; if not, use unsupervised to explore and label later.
  • Starter idea: Cluster discussion posts to propose categories; later, convert to a supervised classifier.
  • Metrics: Supervised uses accuracy/F1/AUC; Unsupervised uses silhouette score, Davies–Bouldin, or qualitative inspection.
• Reinforcement Learning (RL)
  • Definition: An agent learns actions by trial-and-error to maximize reward in an environment.
  • Usage: Robotics/control, recommendation sequencing, tutoring policies, operations optimization.
  • Why it matters: Trains behavior where labeled examples are scarce but feedback (reward) exists.
  • Starter idea: Use Gymnasium’s CartPole to understand states, actions, reward, and exploration vs. exploitation.
  • Watch-outs: Reward shaping pitfalls, sample inefficiency, and safety constraints in real systems.
• GANs (Generative Adversarial Networks)
  • Definition: Two neural nets—generator creates samples and discriminator judges them—train in competition.
  • Usage: Data augmentation, image synthesis, style transfer, super-resolution.
  • Why it matters: Powerful for realistic media and boosting small datasets.
  • Starter idea: Train a tiny GAN on simple images (e.g., digits) to visualize generator progress.
  • Watch-outs: Training instability, mode collapse; consider newer alternatives (e.g., diffusion models) depending on task.
• Expert Systems
  • Definition: Rule-based systems that encode human expertise as IF–THEN logic with an inference engine.
  • Usage: Compliance checks, eligibility screening, classroom rubrics, step-by-step triage.
  • Why it matters: Transparent, auditable decisions; great baseline before ML.
  • Starter idea: Build a rubric-based grader or eligibility screener using YAML/JSON rules + a small UI.
  • Watch-outs: Brittle outside the rule set; maintenance required as policies change. Consider hybrid with ML.
• Fuzzy Logic
  • Definition: Reasoning with degrees of truth via membership functions (not just true/false).
  • Usage: Control systems (“slightly warm,” “very noisy”), recommendation heuristics, grading with soft thresholds.
  • Why it matters: Encodes human-like nuance and is interpretable.
  • Starter idea: Fuzzy rules for late/partial assignment credit or equipment safety thresholds in a lab.
  • Watch-outs: Designing membership functions requires domain insight; validate against real outcomes.
• Cognitive Computing
  • Definition: Systems that emulate aspects of human reasoning using NLP, knowledge graphs, search, and ML to support decisions.
  • Usage: Question-answering over documents, tutor/assistant bots, decision support dashboards.
  • Why it matters: Combines language understanding with retrieval and logic—great for “copilot” tools.
  • Starter idea: Retrieval-augmented Q&A bot over course policies or business SOPs with citations.
  • Watch-outs: The term is broad/marketed—define components (retrieval, LLM, rules) and measure accuracy + hallucinations.
• Evolutionary Algorithms
  • Definition: Population-based search (selection, crossover, mutation) that evolves better solutions over generations.
  • Usage: Hyperparameter tuning, feature selection, scheduling/layout optimization, neural architecture search.
  • Why it matters: Derivative-free optimization for messy objective functions.
  • Starter idea: Use a simple genetic algorithm to tune an ML model’s hyperparameters on a small dataset.
  • Watch-outs: Can be compute-heavy; set time/compute budgets and track overfitting to validation data.

Tip: Start with supervised learning and transfer learning; they deliver the fastest wins for real projects.

5) Tools & Ecosystem (where to learn & practice)

Top Sites to Learn

• DeepLearning.AI – courses & short programs: deeplearning.ai

• fast.ai – practical DL, free: fast.ai

• Coursera – curated tracks: coursera.org

• Great Learning – applied courses: mygreatlearning.com

• Kaggle Learn – bite-size notebooks: kaggle.com/learn

• Google AI Education – overviews & guides: ai.google/education

Best Dataset Repositories

• Kaggle: kaggle.com/datasets

• Hugging Face Datasets: huggingface.co/datasets

• UCI ML Repo: archive.ics.uci.edu/ml

• Google Dataset Search: datasetsearch.research.google.com

• OpenML: openml.org

• Data.gov: data.gov

YouTube Channels

• 3Blue1Brown
• StatQuest with Josh Starmer
• CodeBasics
• Two Minute Papers
• DeepLearningAI
• Yannic Kilcher

Blogs to Follow

• Towards Data Science
• Machine Learning Mastery
• The Gradient
• Distill
• Andrej Karpathy’s blog

6) Choose-Your-Path: tailored learning tracks

A) Track A — Teachers & Instructors

Goal: build AI-enhanced lessons, grading rubrics, and formative feedback.

• Week 1–2: Prompting + NLP basics with HuggingFace; create a rubric generator.

• Week 3–4: Build a Streamlit app that auto-summarizes student drafts and suggests resources.

• Deliverables: Responsible-use policy + consent workflow for your class.

Join the Teacher Discussion

B) Track B — Student Learners

Goal: pass courses and build a portfolio.

• Week 1–2: Python + Pandas; EDA on a Kaggle dataset.

• Week 3–4: Train a scikit-learn model; document results; publish to GitHub Pages.

• Deliverable: 2-page project readme with charts & model card.

C) Track C — Student Entrepreneurs

Goal: validate an AI-assisted product idea fast.

• Week 1: Customer discovery + LLM prototyping (ChatGPT/Claude/Gemini).

• Week 2: Build a Streamlit MVP (copywriter, tutor bot, or research assistant).

• Week 3: Collect 5 tester interviews; iterate features from feedback.

• Week 4: Add analytics + Stripe test mode; write landing page.

7) Mini Projects (portfolio-ready)

1. 1. Data-to-Decision Dashboard

      • Pull a public dataset; clean with Pandas; model in scikit-learn; visualize in Plotly; publish on Streamlit Cloud.

   2. FAQ Chatbot for Your Program/Business

      • Curate FAQs; embed with sentence transformers; build retrieval-augmented Q&A in Python; add guardrails & a usage log.

   3. Image Classifier for Local Needs

      • Collect 200–500 images (ethically); fine-tune a pretrained CNN in PyTorch; deploy with Gradio.

   4. Explainable Risk Scoring

      • Train a tree-based model; add SHAP explanations; write a one-page “model card” explaining data, bias checks, and limits.

8) Responsible & Ethical Use (non-optional)

• • Data privacy: use consent forms; anonymize where possible.

  • Bias & fairness: test on subgroups; document harms/mitigations.

  • Transparency: provide model cards & disclaimers for limitations.

  • Classroom & workplace: follow your institution or client policy.

  • Helpful resources:

    • Model Cards: modelcards.withgoogle.com

    • AI Fairness 360: ai-fairness-360.org

9) Quick Start: your first 48 hours

1. 1. Open Colab and complete a Pandas + scikit-learn tutorial (Kaggle Learn).

   2. Fork a Streamlit starter and deploy a toy app.

   3. Join one dataset community (Kaggle or HF Datasets) and post one question/answer.

   4. Pick one mini project and write your success criteria before you code.

10) Handy Link Pack (bookmark these)

• • Python: python.org 

  • Pandas: pandas.pydata.org

  • scikit-learn: scikit-learn.org

  • PyTorch: pytorch.org

  • TensorFlow/Keras: tensorflow.org / keras.io

  • Streamlit: streamlit.io

  • Gradio: gradio.app

  • MLflow: mlflow.org

  • W&B: wandb.ai

  • Hugging Face: huggingface.co

  • Kaggle: kaggle.com

  • Docker: docker.com

  • Git: git-scm.com

Discuss, Compare, Improve

Use the threads below to share lesson links, notebooks, model cards, and mini-project screenshots.

EU AI Act: GPAI rules start (Aug 2, 2025)

The EU’s AI Act has moved from headlines to homework. As of August 2, 2025, providers placing general-purpose AI (GPAI) models on the EU market must meet new transparency and copyright-related obligations—and “systemic-risk” models face additional safety duties. Models that were already on the market before that date get until August 2, 2027 to comply.

Even if you’re US-based, these rules shape vendor roadmaps, product features, research disclosures, and the documentation your classrooms and nonprofits will rely on.

Why it matters for Incubator.org: this is the way forwards to teach AI Literacy + Safety & Well-Being with real artifacts (model specs, training disclosures, eval notes) and to prep Arizona teams for cross-border projects. We’ll translate the law into checklists teachers, program leads, and student teams can actually use.

EU AI Act — GPAI Duties & Timeline (Educator/Nonprofit Guide)

• What’s new: GPAI transparency & copyright duties began Aug 2, 2025; pre-existing models must comply by Aug 2, 2027; systemic-risk models have additional obligations.

• Use this for: syllabus policies, vendor due-diligence, grant compliance, student research ethics.

• Getting started:

  1. List all AI tools in your course/program.

  2. Request vendor transparency notes + copyright statements.

  3. Add a “model facts” appendix to assignments.

• Authoritative references: EU AI Act implementation timeline; EU news on GPAI obligations.

Digital Strategy

Artificial Intelligence Act - Implementation Timeline

→Jump to the Forum Discussion about Are we EU AI Act-ready?

Learning AI isn't optional anymore. We need to learn how to optimize our use. We need to prepare communities, students, and families not just how to keep up with technology, but to lead with it.

Digital literacy is the new reading and writing. It is important to engage with AI thoughtfully, creatively and safely. We already use AI when we use Siri, watch a show that Netflix recommends, or use a chatbot to get help online. 

There's a lot of concern about the potential job loss with AI, but the truth is more nuanced. AI is transforming work, not replacing it. But transforming it is and at a rapid pace. The people who will thrive in this new era will know how to use AI; how to prompt, question and work alongside it.

Knowing how to use AI tools builds skills in critical thinking, communication, and digital collaboration.

Of course it isn't all upside. It can also spread misinformation, reinforce bias or trick people into sharing their personal data. That's why AI education must include cyber-security; the ability to tell what's real what's generated, and how to stay secure online. Teaching digital wisdom is as important as teaching digital skills.

When a student helps a grandparent understand a new AI-powered app, or a parent uses AI to help their child with homework, a multi-generational learning community is born - one that values curiosity, collaboration, and mutual respect. And when users know how to stay safe, they don't just adapt to the digital world, they shape it.

further reading:

OECD, Holmes et al. (2019) educational case studies on critical thinking skills involved with prompt crafting, evaluating outputs, questioning AI generated content

MIT RAISE, British Journal of Educational Technology; case studies on communication skills involved with prompt writing, receiving feedback, refining language/tone

WEF, Journal of Computer Assisted Learning, studies of digital collaboration skills such as Group use of AI tools, co-creation, peer review in tech environments

Developmental psychologist Howard Gardner proposed a theory that challenged the traditional notion of intelligence as a single IQ score. Instead, he identified nine distinct types of intelligence, each representing a different way of understanding and interacting with the world. This theory, called Multiple Intelligences, helps explain the diversity in learning styles, talents, and personal growth.

🌿 1. Naturalist Intelligence (“Nature Smart”)

This intelligence reflects sensitivity to the natural world—plants, animals, weather patterns, and ecosystems. It is especially strong in botanists, ecologists, and even chefs who work with natural ingredients.

• Apps/Tools: 
  • iNaturalist – A community-powered biodiversity observation app that helps users identify and document plant and animal species worldwide.
  • Seek by iNaturalist – A beginner-friendly nature ID app that uses real-time image recognition to identify species without requiring user login.
  • PlantSnap – A visual plant identifier app that instantly recognizes thousands of plant species using your phone’s camera.
  • PictureThis – A smart plant identification app that also provides care guides and plant health insights.
  • Nature’s Notebook – A citizen science platform for phenology tracking—observing seasonal changes in plants and animals over time.
• Related Activities: Gardening, foraging, nature journaling

🎵 2. Musical Intelligence (“Sound Smart”)

The capacity to understand pitch, rhythm, tone, and musical patterns. It is evident in musicians, conductors, composers, and even sensitive listeners.

• Apps/Tools:
  • GarageBand – A user-friendly digital audio workstation (DAW) from Apple that lets users create music using loops, software instruments, and audio recording.
  • Ableton Live – A professional-grade DAW designed for music production, live performance, and experimental sound design.
  • EarMaster – A comprehensive ear training and music theory app that develops listening skills, sight-singing, and rhythm accuracy.
  • Soundtrap – A collaborative, browser-based music studio by Spotify that enables online music production and sharing.
  • Melodics – A gamified learning app that helps users build rhythm, finger drumming, and keyboard skills through engaging lessons.
• Connections: Emotion regulation, auditory learning, math-music cognition

🔢 3. Logical-Mathematical Intelligence (“Number/Reasoning Smart”)

This involves abstract reasoning, numerical logic, and pattern recognition. It’s often well-developed in scientists, engineers, and problem-solvers.

• Apps/Tools: 
  • Khan Academy – A free online learning platform with structured lessons in math, logic, science, and more, ideal for building foundational reasoning skills.
  • Wolfram Alpha – A computational search engine that solves equations and queries using symbolic reasoning, data analysis, and real-world facts.
  • GeoGebra – An interactive mathematics tool for visualizing geometry, algebra, calculus, and statistics through dynamic graphs and models.
  • Brilliant – A problem-solving platform that teaches math, science, and logic through interactive courses and real-world scenarios.
  • Desmos – A graphing calculator and visual learning tool for algebra, calculus, and mathematical modeling.
• Activities: Puzzles, logic, reasoning, problem solving, programming, data analysis

🧠 4. Existential Intelligence (“Life Smart”)

This is the sensitivity to deep questions about existence—life, death, spirituality, and the universe. It often manifests in philosophers, theologians, and spiritual leaders.

• Apps/Tools: 
  • Day One – A beautifully designed digital journaling app for recording thoughts, emotions, spiritual insights, and life events.
  • Insight Timer – A vast library of free guided meditations, spiritual talks, and music that support mindfulness and contemplation.
  • Waking Up – A meditation app that blends mindfulness with philosophical teachings, helping users explore consciousness and the nature of the self.
  • Stoic – A reflective journaling and mood-tracking app inspired by Stoic and philosophical principles to guide personal growth.
  • Reflectly – An AI-powered journaling app that prompts self-reflection and emotional processing through a conversation-like interface.
• Books: Man’s Search for Meaning by Viktor Frankl
• Activities: Journaling, reflection, meditation, finding meaning

🤝 5. Interpersonal Intelligence (“People Smart”)

The ability to understand and connect with others through empathy, communication, and collaboration. Strong in teachers, therapists, and leaders.

• Apps/Tools: 
  • MBTI (Myers-Briggs Type Indicator) – A widely used personality framework that helps individuals understand their social preferences and communication styles.
  • DiSC – A behavioral assessment tool that categorizes users into four communication styles for better collaboration and conflict resolution.
  • GroupMap – A collaborative brainstorming platform that enables teams to visually map ideas, vote, and find alignment.
• Learning Strategies: Role-playing, group discussions, peer mentoring

🏃 6. Bodily-Kinesthetic Intelligence (“Body Smart”)

The skillful use of the body and coordination of movement. Found in athletes, dancers, artisans, and surgeons.

• Apps/Tools: 
  • GoNoodle – Interactive movement videos that combine physical activity with learning, designed especially for children.
  • Just Dance – A rhythm-based dance game that encourages full-body movement and musical timing.
  • Beat Saber – A VR rhythm game where players slash musical blocks, blending motion, timing, and spatial awareness.
  • Tilt Brush – A 3D painting app in virtual reality that allows users to “draw in space” using body gestures.
  • Nike Training Club – A personal training app offering bodyweight workouts, mobility routines, and coaching for physical mastery.
  • SuperBetter – A gamified resilience-building app that uses small physical and mental challenges to improve well-being.
  • FitOn – A fitness app offering guided video workouts across strength, dance, HIIT, and yoga, emphasizing movement for wellness.
• Techniques: Hands-on learning, makerspaces, kinesthetic games

📝 7. Linguistic Intelligence (“Word Smart”)

The ability to express oneself through words and understand complex meanings. Strong in writers, speakers, and readers.

• Apps/Tools: 
  • Grammarly – An AI-powered writing assistant that corrects grammar, spelling, and tone in real time across apps and platforms.
  • Scrivener – A powerful writing software built for long-form projects like novels, scripts, academic papers, and research-based writing.
  • Hemingway Editor – A style and readability checker that highlights complex sentences, passive voice, and adverb usage for tighter, clearer writing.
• Learning Activities: Debates, writing, storytelling, language games

🪞 8. Intrapersonal Intelligence (“Self Smart”)

The capacity for self-reflection, emotional awareness, and personal goal setting. Often seen in philosophers, counselors, and spiritual individuals. These tools support self-awareness, mood tracking, emotional regulation, and reflective learning, which are all central to developing intrapersonal skills.

• Apps/Tools
  • Daylio – A minimalist mood and activity tracker that lets you journal how you feel with just a few taps a day.
  • Headspace – A guided meditation and mindfulness platform with audio sessions, courses, and stress-reducing exercises.
  • Journey – A cross-platform digital journal for long-form self-reflection and life documentation.
  • Insight Timer – A free meditation app with thousands of guided meditations, music tracks, and mindfulness talks.
• Practices: Meditation, journaling, therapy

🧩 9. Spatial Intelligence (“Picture Smart”)

The ability to visualize and manipulate objects in space. Essential in fields like architecture, design, and art.

• Apps/Tools:
  • SketchUp – A user-friendly 3D modeling program used for architectural design, interior layouts, and concept visualization.
  • Tinkercad – A free, browser-based CAD tool by Autodesk designed for beginners to create 3D models using drag-and-drop shapes.
  • AutoCAD – A professional drafting and design software used by engineers, architects, and designers for precise 2D and 3D technical drawings.
  • Procreate – A high-powered digital painting app for iPad favored by illustrators, animators, and concept artists.
  • Adobe Illustrator – A vector graphic design program ideal for logos, illustrations, and scalable design assets.
  • Inkscape – A free, open-source vector graphics editor perfect for design, illustration, and technical drawing.
  • Minecraft – A block-based sandbox game that allows players to build virtual worlds, enhancing spatial reasoning and creative design thinking.
• Activities: Drawing, illustrating, modeling, 3D design, Minecraft

Sources:
1. Gardner, H. Frames of Mind: The Theory of Multiple Intelligences, 1983
2. Armstrong, T. Multiple Intelligences in the Classroom, ASCD, 3rd Ed., 2009
Visit Gardner’s official site: howardgardner.com

→Jump to the Forum Discussion about 9 Types of Intelligence.