Types of AI Tools Explained

Whether you’re looking to enhance lessons, support independent learning, or introduce new skills, understanding the types and features of AI tools is the first step.

Therefore this section helps educators explore the landscape of AI tools available for adult learners. It introduces key categories of tools, explains their functions and practical uses, and offers real-world examples to make teaching and learning more relevant.

UNDERSTANDING AI TOOL TYPES

Different Types of AI Tools

Artificial Intelligence has its roots in ancient philosophy, but modern AI began in the 1950s with pioneers like Alan Turing and John McCarthy. The 1956 Dartmouth Conference marked the official birth of AI as a field. In the 1970s–80s, AI faced setbacks known as the “AI winters” due to limited progress and high expectations. The resurgence began in the 2000s with advances in computing power, big data, and machine learning. Today, we live in the era of generative AI, with tools like ChatGPT and DALL·E becoming part of daily life. Understanding this history helps learners appreciate how far the field has come—and where it may go next.

Transversal Tools (General-purpose AI)

These tools are flexible and widely applicable across many contexts and subjects. Learners can use them for multiple purposes—from writing and research to organizing tasks.

Transversal tools are typically built using large language models (LLMs) or foundation models trained on massive datasets across many domains (e.g., books, websites, conversations). Tools like ChatGPT or Gemini are based on transformer architecture and use unsupervised machine learning on billions of text samples, followed by instruction tuning to better respond to human input.

Examples:

Great for: Teaching foundational AI skills, digital literacy, and problem-solving.

Specific Tools (Task-focused AI)

These tools are designed for one main function and often serve a niche or practical use case. They help learners accomplish specific tasks more efficiently.
Specific AI tools are developed using narrow AI models designed and trained for one or a limited set of tasks—such as voice recognition, text-to-speech, or image generation. These models often rely on supervised machine learning, using labeled datasets relevant to their specific function, and are optimized for accuracy and performance within their domain.

Examples:

Canva AI / Adobe Firefly – Graphic design and image generation
Natural Reader – Text-to-speech for reading articles and books aloud
TripIt AI / Roam Around – Travel itinerary planning
Runway ML – Video editing and content generation
Speechify / Bookshare – Accessibility and audio learning

Great for: Subject-specific tasks (e.g., media creation, language learning, accessibility).

AI BUILDING BLOCKS

Core Concepts Behind Today’s Smart AI Tools

To use AI tools confidently, it helps to understand the powerful technologies that make them work. Three essential AI concepts—Large Language Models (LLMs), Natural Language Processing (NLP), and Generative AI represent key technologies enabling AI tools to understand, respond, and even create human-like language, images, and more.

Natural Language Processing (NLP)
The field of AI that focuses on how machines understand, interpret, and generate human language.
NLP is the method that allows machines to “read” or “listen” to human language and turn it into something an AI can work with.
Used in: chatbots, translation apps, voice assistants

Generative AI
AI that can create new content—such as text, images, music, or video—rather than just analyzing data.
Generative AI is about creativity—it uses models like LLMs to produce entirely new content based on prompts or patterns.
Examples: ChatGPT (text), DALL·E (images), Runway ML (video)

What AI can do

Common Features of AI Tools

AI tools come with a variety of features that help users accomplish tasks more efficiently, creatively, or accessibly. This section introduces the most common functions—such as generating text, finding information, creating visuals, or converting speech—and explains how each feature can be used in real-life learning and everyday situations.