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Blink 3 of 8 - The 5 AM Club
by Robin Sharma
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Artificial Intelligence for Learning summary
Donald Clark
Using AI and Generative AI to Support Learner Development
20 mins
Brief summary
Artificial Intelligence for Learning delves into the transformative potential of AI in educational settings. It examines how AI can enhance learning experiences, personalize education, and prepare us for future technological advancements in the educational sphere.
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Artificial Intelligence for Learning
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AI is a collection of tools, not one omnipotent mind
In 1899, Jean-Marc Côté, a French illustrator, sketched a robotic servant sweeping the floor. His idea of future automation mirrored the machines of his time – rigid, mechanical, and humanoid. That drawing still echoes how many people think about artificial intelligence today: as a futuristic figure with human-like traits. But AI doesn’t work that way.
What we call AI is actually a collection of systems, each built to handle a specific task. There’s no single core or shared intelligence behind it. Some systems use logic while others rely on statistics or neural networks, and they operate independently, not as pieces of one unified mind.
The confusion often begins when people assume that AI understands what it’s doing. It can write, speak, or identify images with impressive fluency, but there’s no awareness behind these actions. The term “competence without comprehension” captures this clearly. The system performs well but has no grasp of meaning, emotion, or intention.
This gap is easy to overlook, especially when familiar language muddies the waters. Terms like “learning” or “thinking” are often used to describe what these tools do, but they’re just shorthand. The process involves pattern recognition, optimization, and repetition – nothing like human thought.
When prompted poorly or fed distorted data, AI systems generate flawed responses. These errors reflect issues in the input, not deliberate behavior. Still, media headlines and cultural references keep encouraging people to see AI as something sentient or dangerous, when the more relevant concern is how we design and apply it.
AI systems improve by processing data through techniques like machine learning, deep learning, and reinforcement learning. These methods allow models to detect patterns, make predictions, and refine performance. Once trained, their outputs can be shared instantly across thousands of tools, forming a broad, scalable network of capability.
In education, these tools are already supporting analysis, translation, content creation, and early detection of student disengagement. Some even respond to emotional cues or simulate physical tasks. Their strength lies in precision and reach – not imagination or insight. Instead of replicating how people think, these systems help fill in where we fall short: consistency, scale, and memory.
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What is Artificial Intelligence for Learning about?
Artificial Intelligence for Learning (2024) explores how AI can be strategically and practically applied to improve learning and development in organizations. It clarifies common misconceptions, explains the learning science behind generative AI, and offers guidance in areas such as personalized content, chatbot support, and ethical considerations.
Who should read Artificial Intelligence for Learning?
- Educators and instructional designers adopting AI in learning
- Corporate training and development leaders using edtech
- Curious professionals exploring the future of education
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