A role for AI in evaluation?
It’s one thing to use AI to provide constructive, no-stakes feedback to teachers about their instructional practice. But what about incorporating it into formal performance evaluations?
Nobody I talked to liked that idea.
Thomas Kane of the Harvard Graduate School of Education, who ran the MET project, said, “AI could make it easier for teachers to get more frequent feedback, without the taint of a supervisory relationship.” But introduce that “supervisory relationship,” and you lose teachers’ willingness to give these technologies a try.
Indeed, neither company founder I spoke with was eager to see their tech used for teacher evaluations. As TeachFX’s Poskin told me, “You want teachers to learn and grow.” The more often teachers upload recordings to the platform, the better. Yet formal evaluations usually only happen every few years. They are the antithesis of constructive feedback.
That said, leaders of both companies welcome teachers’ deciding to use their recordings, or the data and “reflection logs” derived from them, in coaching sessions or formal evaluations. In all cases, the key is leaving those decisions to teachers and letting them keep control of the process and data.
To me, these apps sound like great tools for conscientious teachers eager to improve—as Geller and Poskin no doubt were. But it strikes me that teacher motivation to use them as intended must be an issue, just as it is for students. Teachers are crazy-busy, and apps like these are, ultimately, extra work.
To their credit, some districts provide incentives, such as counting the time teachers spend using the apps against professional learning requirements or allowing recordings to stand in for weekly classroom walkthroughs. Those are steps in the right direction—but we shouldn’t expect uptake to be universal. To me, it seems likely that the worst teachers, who arguably would have the most to gain, are the least likely to engage with these sorts of technologies.
