Own the Shift: AI in Your Classroom
Pathway: teaching
Design a practical, skill-based course for all teachers to help them understand and adapt to their roles in the age of AI-powered education. The course should: Be divided into three clear phases Include realistic scenarios, hands-on activities, short videos, and concrete classroom examples. Use scenarios and practices Focus on immediate application, not theory. make it more practical and skill based. don't exceed three phases.
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1. Analyze the fundamental concepts and implications of AI-powered education for transforming classroom practices.
Learning Targets:
1. Define key AI terminologies (e.g., machine learning, deep learning, natural language processing) relevant to education.
2. Evaluate the impact of AI technologies on traditional teaching methods and student engagement.
3. Identify ethical, practical, and technical considerations when integrating AI into classroom settings.
4. Summarize historical evolutions and future trends in AI applications within educational contexts.
Modules
1. AI Foundations for Educators
1. 1. Introduction to AI Terminologies and Concepts
Learning Outcomes:
1. Define core AI terminologies including machine learning, deep learning, neural networks, and natural language processing.
2. Differentiate between supervised, unsupervised, and reinforcement learning methods.
3. Identify examples of AI terminology usage in various educational tools.
4. Illustrate the significance of these terms in modern pedagogical approaches.
1. 2. Mathematical and Statistical Foundations of AI
Learning Outcomes:
1. Explain basic probability and statistics principles used in AI.
2. Identify mathematical models that serve as a basis for machine learning algorithms.
3. Solve sample problems using linear algebra concepts relevant to neural networks.
4. Demonstrate application of statistical methods in analyzing educational data.
1. 3. Introduction to AI Frameworks and Architectures
Learning Outcomes:
1. Describe fundamental AI architectures such as neural networks and decision trees.
2. Compare different AI frameworks and toolkits applied in education.
3. Evaluate the benefits and limitations of various AI architectures.