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Teachers are starting to embrace the feedback from AI tools to enhance their teaching methods, according to a recent report. But will this trend continue, and how are teachers responding to this new form of feedback?
- The TeachFX AI tool provides detailed feedback on teaching styles, focusing on the performance of teachers rather than students.
- Personalized feedback from AI tools, such as TeachFX and TalkMoves, offers scalable solutions that aren’t bound by place or time.
- Teachers have expressed concerns over the data collection aspect of these tools and the potential misuse of their instructional data.
A Deep Dive into TeachFX
Julie York, a teacher from South Portland High School in Maine, discovered TeachFX, an AI tool that analyzes classroom audio to provide feedback on teaching styles. She quickly recognized its potential, noting:
“It’s not a rubric. It’s a reflection.”
Jamie Poskin, the founder of TeachFX, emphasizes that the tool aims to support teachers in reflecting on their practices, particularly on balancing classroom talk between teachers and students.
Teachers have observed quick improvements after using TeachFX. Keara Phipps, an elementary school teacher, adjusted her teaching method after realizing she spoke excessively during her classes. Jamie Poskin highlights the significance of this feedback, mentioning a shocking statistic: “You want to guess how much the average student spoke in one hour of class? Seven seconds, per hour.”
The Growth of AI in Professional Development
Other AI-driven tools like TalkMoves, developed at the University of Colorado Boulder, also use audio data to provide insights into classroom practices. This application is now employed by Saga Education for tutor training.
Dora Demszky from Stanford University comments on the computational capabilities that allow for large-scale classroom analysis. These tools focus on determining good teaching practices and providing neutral feedback. After the release of M-Powering Teachers, another AI tool, tutors reduced their talk time and increased their uptake of student contributions, which emphasizes giving students a voice in the learning process.
The COVID-19 pandemic and the resulting shift to online teaching presented challenges. However, Jennifer Jacobs, who developed TalkMoves, noticed similar improvements in online and offline teaching methods after teachers received feedback.
While these tools generate extensive data, it’s the personal aspect of the feedback that’s game-changing. Yasemin Copur-Gencturk, a researcher, has designed an AI mentor for math teachers that operates similar to student AI assistants. It offers detailed insights into student misconceptions in math and assists teachers in crafting appropriate questions.
Privacy Concerns Come into the Picture
Despite the positive feedback, concerns remain. Julie York, who praises TeachFX, struggled to get fellow teachers on board. The apprehension lies in data collection and potential misuse. Dora Demszky acknowledges that while many tools already collect teacher data, there’s hesitancy in sharing it in this particular context.
Keara Phipps points out that teachers need to be prepared for the feedback:
“You’re going to have to change something when you look at this data.”
In conclusion, as AI-driven tools become increasingly prevalent in professional development, the teaching community is faced with the decision of whether to embrace or resist these advancements. It remains to be seen how these tools will shape the future of education.
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