{"id":558,"date":"2023-10-28T13:30:11","date_gmt":"2023-10-28T17:30:11","guid":{"rendered":"https:\/\/sites.miamioh.edu\/exemplary-teaching-practices\/?p=558"},"modified":"2023-10-28T13:30:14","modified_gmt":"2023-10-28T17:30:14","slug":"the-digital-classroom-revolution-how-ai-is-transforming-education","status":"publish","type":"post","link":"https:\/\/sites.miamioh.edu\/exemplary-teaching-practices\/2023\/10\/the-digital-classroom-revolution-how-ai-is-transforming-education\/","title":{"rendered":"The Digital Classroom Revolution: How AI is transforming Education"},"content":{"rendered":"\n

In today’s fast-paced, technology-driven world, the education landscape is undergoing a profound transformation. Artificial Intelligence (AI), once confined to the realm of science fiction, is now a tangible force reshaping the way students learn and teachers instruct. From personalized learning paths to innovative classroom tools, AI is opening up new horizons in education. In this blog post, we’ll explore the exciting intersection of AI and the classroom, shedding light on the numerous ways, both good and not good, in which this technology is revolutionizing education as we know it.<\/p>\n\n\n\n

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Here are a few different examples of biases we see in the AI world that are important our students are aware of. While AI is a great tool it needs to be used as a tool and be sure we are not reliant on it. <\/p>\n\n\n\n

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Reinforcement of Stereotypes:<\/strong> AI systems that are trained on biased or unrepresentative data can reinforce harmful stereotypes. This can happen in areas such as hiring, lending, or predictive policing.<\/p>\n<\/div>\n\n\n\n

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Lack of Accountability:<\/strong> Determining responsibility for biased AI systems can be challenging. Is it the responsibility of the data provider, the algorithm developer, or the end-user? This lack of accountability can make it difficult to address biases effectively.<\/p>\n<\/div>\n\n\n\n

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Data Biases:<\/strong> Biases often stem from the data used to train AI models. If historical data reflects existing societal biases, the AI model will learn and replicate those biases. Cleaning and curating training data is a complex task.<\/p>\n<\/div>\n<\/div>\n\n\n\n

In Miss Wilson’s class this week: <\/strong><\/p>\n\n\n\n

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I get to spend 45 minutes a day with a small group and work on intervention. This is an additional period I have to spend time planning each day. I had been overwhelmed with this additional planning. With the introduction of AI in my graduate class I have been playing around with using AI to plan different activities. This week I chose to use Magic School to create choice boards of different practice activities. This intervention time takes lots of time to plan for because it needs to be specific to each student. While it is small group it is still about 10 kids over five days a week adds up. I enjoyed Magic School. It is taking some time to learn how to use the tools. I was able to create the choice board easily but then I still had to find the resources. I wish Magic School could also help put the practices together.<\/p>\n<\/div>\n\n\n\n

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\nhttps:\/\/www.youtube.com\/watch?v=8pIlOX_V25Q\n<\/div>
Here is a video from a channel all about how to use Magic School. It is a great introduction to the different tools available!<\/figcaption><\/figure>\n\n\n\n

TCE Threshold concepts:<\/p>\n\n\n\n