Key points:
- The moment to shape AI’s role in teacher preparation is now
- From pen to prompt: Navigating AI in the classroom
- As higher ed embraces AI, where is learning headed?
- For more news on AI, visit eCN’s AI in Education hub
In education, we often talk about “meeting the moment.” Our current moment presents us with both a challenge and an opportunity: How can we best prepare and support our teachers as they navigate increasingly complex classrooms while also dealing with unprecedented burnout and shortages within the profession?
One answer could lie in the thoughtful integration of artificial intelligence to help share feedback with educators during training. Timely, actionable feedback can support teacher development and self-efficacy, which is an educator’s belief that they will make a positive impact on student learning. Research shows that self-efficacy, in turn, reduces burnout, increases job satisfaction, and supports student achievement.
As someone who has spent nearly two decades supporting new teachers, I’ve witnessed firsthand how practical feedback delivered quickly and efficiently can transform teaching practice, improve self-efficacy, and support teacher retention and student learning.
AI gives us the chance to deliver this feedback faster and at scale.
A crisis demanding new solutions
Teacher shortages continue to reach critical levels across the country, with burnout cited as a primary factor. A recent University of Missouri study found that 78 percent of public school teachers have considered quitting their profession since the pandemic.
Many educators feel overwhelmed and under-supported, particularly in their formative years. This crisis demands innovative solutions that address both the quality and sustainability of teaching careers.
What’s often missing in teacher development and training programs is the same element that drives improvement in other high-performance fields: immediate, data-driven feedback. While surgeons review recordings of procedures and athletes get to analyze game footage, teachers often receive subjective observations weeks after teaching a lesson, if they receive feedback at all. Giving teachers the ability to efficiently reflect on AI-generated feedback–instead of examining hours of footage–will save time and potentially help reduce burnout.
The transformative potential of AI-enhanced feedback
Recently, Relay Graduate School of Education completed a pilot program with TeachFX using AI-powered feedback tools that showed remarkable promise for our teacher prep work. Our cohort of first- and second-year teachers more than doubled student response opportunities, improved their use of wait time, and asked more open-ended questions. Relay also gained access to objective data on student and teacher talk time, which enhanced our faculty’s coaching sessions.
Program participants described the experience as “transformative,” and most importantly, they found the tools both accessible and effective.
Here are four ways AI can support teacher preparation through effective feedback:
1. Improving student engagement through real-time feedback
Research reveals that teachers typically dominate classroom discourse, speaking for 70-80 percent of class time. This imbalance leaves little room for student voices and engagement. AI tools can track metrics such as student-versus-teacher talk time in real time, helping educators identify patterns and adjust their instruction to create more interactive, student-centered classrooms.
One participant in the TeachFX pilot said, “I was surprised to learn that I engage my students more than I thought. The data helped me build on what was working and identify opportunities for deeper student discourse.”
2. Freeing up faculty to focus on high-impact coaching
AI can generate detailed transcripts and visualize classroom interactions, allowing teachers to reflect independently on their practice. This continuous feedback loop accelerates growth without adding to workloads.
For faculty, the impact is equally powerful. In our recent pilot with TeachFX, grading time on formative observation assignments dropped by 60 percent, saving up to 30 hours per term. This reclaimed time was redirected to what matters most: meaningful mentoring and modeling of best practices with aspiring teachers.
With AI handling routine analysis, faculty could consider full class sessions rather than brief segments, identifying strategic moments throughout lessons for targeted coaching.
The human touch remains essential, but AI amplifies its reach and impact.
3. Scaling high-quality feedback across programs
What began as a small experiment has grown to include nearly 800 aspiring teachers. This scalability can more quickly reduce equity issues in teacher preparation.
Whether a teaching candidate is placed in a rural school or urban district, AI can ensure consistent access to meaningful, personalized feedback. This scalable approach helps reduce the geographic disparities that often plague teacher development programs.
Although AI output must be checked so that any potential biases that come through from the underlying datasets can be removed, AI tools also show promise for reducing bias when used thoughtfully. For example, AI can provide concrete analysis of classroom dynamics based on observable actions such as talk time, wait time, and types of questions asked. While human review and interpretation remains essential–to spot check for AI hallucinations or other inaccuracies and interpret patterns in context–purpose-built tools with appropriate guardrails can help deliver more equitable support.
4. Helping teachers recognize and build on their strengths
Harvard researchers found that while AI tools excel at using supportive language to appreciate classroom projects–and recognize the work that goes into each project–students who self-reported high levels of stress or low levels of enjoyment said the feedback was often unhelpful or insensitive. We must be thoughtful and intentional about the AI-powered feedback we share with students.
AI can also help teachers see what they themselves are doing well, which is something many educators struggle with. This strength-based approach builds confidence and resilience. As one TeachFX pilot participant noted, “I was surprised at the focus on my strengths as well and how to improve on them. I think it did a good job of getting good details on my conversation and the intent behind it. ”
I often tell new teachers: “You’ll never see me teach a perfect lesson because perfect lessons don’t exist. I strive to improve each time I teach, and those incremental gains add up for students.” AI helps teachers embrace this growth mindset by making improvement tangible and achievable.
The moment is now
The current teacher shortage is a crisis, but it’s also an opportunity to reimagine how we support teachers.
Every student deserves a teacher who knows how to meaningfully engage them. And every teacher deserves timely, actionable feedback. The moment to shape AI’s role in teacher preparation is now. Let’s leverage these tools to help develop confident, effective teachers who will inspire the next generation of learners.
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