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Warren McCulloch and Walter Pitts created the first mathematical model of a neural network. Their groundbreaking paper 'A Logical Calculus of Ideas Immanent in Nervous Activity' introduced the concept of artificial neurons, laying the foundation for both artificial neural networks and computational neuroscience.

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Alan Turing published 'Computing Machinery and Intelligence,' introducing the famous Turing Test. This test proposed a method for determining if a machine can exhibit intelligent behavior indistinguishable from a human, becoming a fundamental concept in AI development.

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1956: Dartmouth Conference - Birth of AI

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1956: Dartmouth Conference - Birth of AI

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The term 'Artificial Intelligence' was officially coined at the Dartmouth Conference. This historic meeting, organized by John McCarthy, Marvin Minsky, and others, marked the birth of AI as a field. The conference established AI as an academic discipline and set the stage for future research.

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Joseph Weizenbaum created ELIZA at MIT, the first natural language processing computer program. ELIZA could simulate conversation by pattern matching and substitution, primarily mimicking a psychotherapist. This was a milestone in human-computer interaction.

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Marvin Minsky and Seymour Papert published 'Perceptrons,' analyzing artificial neural networks. While their critique temporarily slowed neural network research, it ultimately led to more sophisticated approaches in machine learning.

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Report triggers the first AI winter (1973–late 1970s)

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Report triggers the first AI winter (1973–late 1970s)

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In 1973, the UK commissioned the Lighthill Report, which argued AI had failed to deliver major promised breakthroughs. After the report, UK funding and institutional support for many AI efforts were sharply reduced, helping launch the first “AI winter,” when optimism, investment, and progress slowed significantly.

Why it matters:

Became a major public turning point where expectations vs. evidence shaped funding decisions.

Helped define “AI winter” as a recurring cycle: hype → disappointment → funding cuts → rebuild.

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1997: Deep Blue Defeats World Chess Champion

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1997: Deep Blue Defeats World Chess Champion

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IBM's Deep Blue became the first computer to defeat a reigning world chess champion, Garry Kasparov, in a match. This historic moment demonstrated the potential of AI in complex strategic thinking and decision-making.

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Geoffrey Hinton introduced the concept of deep learning through his research on deep belief networks. This breakthrough led to significant improvements in machine learning capabilities and sparked the modern AI revolution.

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IBM's Watson computer system competed on Jeopardy! and won against former champions Brad Rutter and Ken Jennings. This demonstrated AI's ability to understand natural language and process vast amounts of information quickly.

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2012: Deep Learning Breakthrough in Computer Vision

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2012: Deep Learning Breakthrough in Computer Vision

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The AlexNet deep learning model achieved breakthrough performance in the ImageNet competition, demonstrating the power of deep convolutional neural networks for computer vision tasks. This marked a turning point in the practical application of deep learning.

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Google's acquisition of DeepMind marked a significant milestone in AI development. DeepMind later created AlphaGo, which defeated world champion Go player Lee Sedol in 2016, demonstrating AI's capability in handling complex strategic games.

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OpenAI released ChatGPT, a large language model that demonstrated unprecedented capabilities in natural language understanding and generation. This launch marked a new era in AI accessibility and sparked widespread public interest and discussion about AI's role in society.

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Released in Dec. 2024, Sora Turbo became the first high fidelity video creation tool accessible to the general public. Videos are often indistinguishable from real life and raises questions about AI safety and guardrails.

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In 2014, Adobe began collaborating with an startup called "Spruce" which they thought might compete with the likes of Twitter. It then evolved into a product that would generate images for Twitter to make posts more interesting. The team at Adobe quickly noticed that users were not really using the image generator for Twitter, but other projects where text had to work with images. They came to realize that this was a standalone product, and Adobe Express was born. This tool makes it incredibly easy to build educational images, graphs and even videos to help with learning.

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Canva AI, launched at Canva Create 2025, is an accessible generative design partner that enables prompt-based creation of visuals, documents, and interactive content. Its AI tools help educators and students quickly produce presentations, worksheets, and data stories, enhancing creativity, streamlining lesson design, and supporting diverse learning experiences in classrooms today worldwide.

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Google Labs introduced Notebook LM in 2023 as a private, interactive research assistant, which enables individuals to interact with user-developed documents. This tool is a game-changer, as it utilizes advanced natural language processing to interpret and respond to self-generated content, without relying on random information from the internet. Notebook LM is shifting learning from content delivery to content interaction, supporting different learning speeds and styles, while maintaining academic integrity.

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A group of researchers at Google published the "Attention is All You Need" manuscript in 2017 and dramatically re-conceptualized machine learning with the introduction of the transformer architecture. The manuscript's authors proposed a transformer model with a "self-attention" mechanism that uses parallel processing to examine the morpho-syntactic and semantic relationships in natural language. This approach for training AI results in models that have richer contextual understanding of natural language data. In educational technology, AI-generated content from transformer models can support personalized learning and automated feedback.

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2024: Emergence of Agentic AI in Microsoft Copilot Studio

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2024: Emergence of Agentic AI in Microsoft Copilot Studio

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Microsoft added new agent‑building features to Copilot Studio, making AI much more capable than just responding to prompts. These agents can plan tasks, take action, and use tools, which makes it easier to build powerful AI workflows without heavy coding.

Educators and IDs can create custom agents to help with lesson planning, feedback on assessments, learner support, and data analysis. The result is more personalized, on‑demand support for adult learners, right when they need it.

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Google introduced AutoML in 2017. This technology made AI accessible to a broader audience with increased automation of the ML development process. AutoML was relatively easy to employ, allowing users to upload data and specify desired results without needing a lot of specialized knowledge. In education, AutoML empowered teachers and administrators with the ability to build predictive models for the success of their learners.

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	Generative Adversarial Networks (GAN) 2014

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	Generative Adversarial Networks (GAN) 2014

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Introduced by Ian Goodfellow, adversarial networks are two neural networks, a generator and a discriminator. The two networks compete to create realistic synthetic data. The two work in a feedback loop to create new images, text, videos, or augment data sets until the discriminator can’t distinguish the real from the fake.

Positive uses in education include personalizing learning, making learning more immersive, or augmenting data sets.

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ImageNet is a large visual database built off of WordNet. It is a compilation of 14 million images that were hand annotated by project and consists of images in 20,000 categories. Images were selected from online image searches and in multiple languages. This had an impact on deep learning as object localization could happen with a minimal error rate compared to humans.

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Between 2012 and 2013, Massive Open Online Courses (MOOCs) gained widespread adoption through platforms like Coursera, edX, and Udacity. These platforms made high-quality education accessible at global scale, reaching millions of learners and fundamentally changing how learning content was designed and delivered online.

Educational Connection

MOOCs pushed instructional designers to rethink engagement, assessment, and feedback in large-scale learning environments, laying the groundwork for data-driven and AI-supported learning design.

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AlexNet Wins ImageNet — The Deep Learning Breakthrough (2012)

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AlexNet Wins ImageNet — The Deep Learning Breakthrough (2012)

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In 2012, a deep neural network called AlexNet dramatically outperformed all competitors in the ImageNet visual recognition challenge. Its success proved that deep learning and GPU computing could solve complex perception tasks, triggering today’s AI boom and accelerating breakthroughs in vision, speech recognition, and natural language processing.

Significance

This moment marked the modern deep learning revolution, shifting AI research toward neural networks and data-driven learning. It unlocked rapid advances across healthcare, robotics, autonomous vehicles, and large language models.