IPersonal LearningExperience with AI
In my capstone class, our professor had us pull out the main ideas from our research and use an AI application to find similar topics. The themes generative AI tools identified were pretty shallow and did not really connect to our sources all that well. While the generative AI tools could identify patterns and repeated words, they overlooked the important ideas we found by closely reading the articles. This highlighted the AI's weaknesses: it can see patterns, but it lacks the judgment inherent to us humans.
Given the rapid developments in AI, educators today need to develop critical skills, including good judgment, data and ethical literacy, and the ability to facilitate learning with curiosity. With these skills, educators will be able to teach kids about and work with artificial intelligence and prepare them for a digital world.
Strong Professional Judgment
In her opinion article, Tanishia Lavette Williams highlights that AI is always behind the times and only uses old information. Teachers have to carefully look at AI's answers and teach students to do the same. AI systems are often biased, and they look to the past. She notes that we need judgment, care, and cultural knowledge to teach with a human touch, which computers, though sophisticated, cannot do. To make good judgments, teachers need to think about what they are doing, carefully analyze what AI provides them, and identify bias. Teachers who lack these skills may unknowingly continue practicing unfair systems and rely too much on automated solutions. But with these skills, teachers can ensure that learning is meaningful for everyone.
Ethical Awareness and Data Literacy
In the podcast with Tom Vander Ark, Doug Fisher emphasized the importance of teaching children about AI from elementary school onward, so they can use the tool critically and responsibly. Many kids start checking out AI on their own by high school, so he thinks we should introduce it as early as second grade. Teachers should get kids curious, help them understand data, and teach them how to talk to AI. If teachers and students know how to understand data, they can check information, question facts, and make good choices. Fisher believes that to make good decisions, everyone needs to know more about data. To help teachers grow in this area, they can work together and set clear classroom rules for using AI responsibly. Without these things, students might not use AI the right way or might just believe whatever it tells them. But if these skills are taught well, they can encourage responsibility, respect for privacy, and good moral judgment.
Nurturing Curiosity and Professional Agency
Fisher also points out that some students think there's just one right answer. We need to pair curiosity with good judgment in this AI age - the ability to look at results, improve them, and make them better. Teachers can show how this is done by asking questions, creating activities where AI helps solve problems, and helping students become skilled with AI instead of just using it passively. As students learn to think critically about AI, they start to see themselves as professionals in training instead of just people trying to finish tasks.
Reflective Question:
What other skills do you think teachers need to keep up with AI and guide students as they use it?
Hi Kris, Your post brought back my memories of being in the same capstone class with you. I did start evaluating AI work then and understood its various loopholes. Now if I ponder over the use of AI then I figure out that maybe the real boundary with AI is not about how much we use it, but when we bring it to use. If AI enters after we have already thought-probed, reflected and formed our own position, it feels like enhancement. But if it takes that active stance before the productive struggle, it risks replacing our expertise. So for me, that timing is something that is much needed alongside integrity and authorship. Anyways thanks for your post. Good Luck!!!
ReplyDeleteKritika, thanks for pointing out the timing and the manner in which we use AI. I’m realizing more and more that the “when” matters more than the “how.” If we bring AI in after we’ve already wrestled with an idea, formed a stance, or done some of the productive struggle, it really does feel like support rather than substitution. But when we let it jump in too early, it can quietly take over the thinking we’re supposed to be doing ourselves. That balance between timing, integrity, and authorship is exactly what I’m trying to hold onto as well. Good luck completing our projects.
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