Understanding AI:

History and Foundations

A Brief History of AI

AI did not begin with ChatGPT. Today’s tools grew from decades of research in computing, statistics, language, and learning. This timeline highlights a few turning points—and why they matter for educators.

Updated October 2026. This is a selective history, not a ranking of products.

1950–1960s: Asking whether machines can think

Alan Turing’s 1950 paper proposed the “imitation game,” now associated with the Turing test. The 1956 Dartmouth summer research project helped establish artificial intelligence as a field. Early systems explored symbolic rules, problem solving, and conversation. ELIZA, developed in the 1960s, showed how simple language patterns could create an impression of understanding. [1, 2]

1970s–1990s: Rules, expert systems, and setbacks

Researchers built systems that used rules and specialist knowledge to solve narrow problems. Progress was uneven: ambitious promises, technical limits, and funding cuts contributed to periods called “AI winters.” In 1997, IBM’s Deep Blue defeated world chess champion Garry Kasparov in a match—a milestone in a specific task, not evidence of general human intelligence. [2]

2010s: Learning from data

Greater computing power, larger datasets, and advances in neural networks accelerated deep learning. Systems improved at recognizing images, processing speech, and finding patterns. In 2016, DeepMind’s AlphaGo defeated Lee Sedol, demonstrating a powerful combination of learning and search. [2, 3]

2017: The transformer

Researchers introduced the transformer architecture in “Attention Is All You Need.” Its attention mechanisms helped models use context within sequences and enabled more parallel training. Transformers became a foundation for many later language models. [4]

2022: ChatGPT brings conversational AI to a wider public

OpenAI released ChatGPT as a research preview on November 30, 2022. The conversational interface made generating, revising, and discussing text accessible to people without programming skills. The release also highlighted persistent problems, including plausible but incorrect answers. [5]

2023–2024: Beyond text-only chat

AI assistants increasingly combined text, images, and audio. Researchers also developed approaches that devote more computation to reasoning before producing an answer. For educators, these changes expanded the kinds of material students could create and analyze, while verification remained essential. [6, 7]

2025–2026: From answering to taking action

Agentic systems combine models with tools to carry out tasks across multiple steps. An agent might search sources, work with files, or use software, depending on its permissions. Greater capability makes clear permissions, verification, and human oversight more important. [8]

Understanding AI Today: Implications for Teaching

What Faculty Need to Understand Now

The practical shift is from generating an answer to working across media, documents, and software. Evaluate the whole system and the learning task—not simply the product name. Capabilities vary by model, subscription, tools, and institutional settings.

Multimodal AI: More Than a Writing Tool

Some systems can work with text, images, audio, video, and code in the same task. Google’s February 2026 Gemini 3.1 Pro model card documents this combination of modalities and reasoning. A different submission format alone therefore does not establish that a student completed the thinking independently.

Reasoning Models: Evaluate the Explanation

Some models devote additional computation to difficult problems. Strong performance does not make every answer dependable. Ask students to test an explanation against disciplinary evidence, identify assumptions, and explain why a method fits the problem. Treat a generated explanation as something to examine.

Agents: From Suggestions to Actions

An agent can combine a model with tools to work through multiple steps. Reusable skills can package procedures and resources for recurring tasks; Anthropic documented this approach in October 2025. In education, distinguish what a system may read, what it may change, and which actions require human review. Review sources and finished work before relying on the result.

Learning Evidence: Independent and Supported Performance

Teaching application: decide when students should demonstrate a skill independently and when AI assistance supports the outcome. Use a brief independent explanation, a supported application, and a revision or transfer task when those conditions fit the goal. Assess students’ decisions and understanding alongside the final product.

Access and Human Agency

UNESCO’s 2025 report on learners’ rights calls for a human-centred, rights-based approach. Provide equivalent ways to participate when cost, disability access, account requirements, or privacy prevent use of a particular tool. Make permitted assistance clear before the assignment begins.

Faculty decision: What must students know and be able to judge for themselves, and what evidence will show it?

Sources for This Update

Google DeepMind: Gemini 3.1 Pro model card (February 2026) — a vendor description of capabilities, not evidence of learning outcomes.
Anthropic: Equipping agents with Agent Skills (October 2025) — technical documentation of reusable agent procedures.
UNESCO: AI and education—protecting the rights of learners (2025) — educational and rights-based guidance.

Foundations: Models, Assistants, and Agents

Artificial intelligence is a broad field. Generative AI creates content such as text, images, audio, or code. A large language model (LLM) is one kind of model used in these systems. An assistant is the application around a model: it may add search, file access, memory, or other tools. Its abilities depend on that whole system, not just the model.

How language models work

During training, a language model learns patterns from data. When generating text, it produces tokens—pieces of words or other symbols—in sequence, using the available context. Further training can improve instruction following and performance on reasoning tasks. Fluent writing and successful problem solving do not establish consciousness or guarantee that an answer is true. [4, 7, 11]

Why answers can still be wrong

Models can generate convincing errors, invented citations, or unsupported explanations. This is often called hallucination. Search and document retrieval can supply useful evidence, but an assistant can still misunderstand or misrepresent it. Check important claims against the original source. [5, 11]

Why This Matters for Teaching

The following are teaching recommendations, informed by UNESCO’s human-centered guidance: [10]

Keep the learning goal visible. Decide which thinking students need to practice themselves and where AI could support that practice.
Make the process visible. Use drafts, reflection, discussion, and explanations of decisions to understand student learning.
Teach verification. Ask students to check sources, identify assumptions, and explain what they accepted or rejected from an AI response.
Protect access and privacy. Check institutional requirements and the specific service’s terms before uploading student work or personal information. Provide an accessible alternative when a tool creates barriers.
Set boundaries for agents. Review what a system can access and do. Require human review before consequential actions.

A question to take back to your course: What should students be able to explain or do independently—even when AI can produce a polished answer?

Sources and further reading

Numbers in the text refer to these sources. Product announcements document historical releases; they are not independent evaluations or current feature guides.