Coaching, at its best, is an act of hope: It’s the belief that change is possible, that people can get better, that something sacred happens when we sit down and say, “Let’s figure this out together.”
AI can support us in this very human endeavor, but let’s not forget where the meaning lives. We find it in listening, in pausing, in that slightly awkward silence when a powerful question causes someone to really think. The role of AI in coaching should be determined by the values of the people who use it, not by the technology itself.
We have worked with thousands of teachers, leaders, and coaches around the world—Mary through the Instructional Coaching Group and Dan through Growth Coaching International. Between our two organizations, we have experienced AI’s coming of age, both within our own coaching engagements and through the everyday experiences of educators and leaders across North America and Australia. We believe that thoughtful educators, leaders, coaches, and technologists will shape the coaching systems to be more humanely effective—if those systems are rooted in consent, ethics, clarity of intent, partnership, and meaningful connection.
In the era of AI, we can easily be pulled into the world of efficiency. Convenience and speed are helpful, but not if we’re missing or losing connection. Before we use AI in our coaching work, we need to identify our purpose. Is it simply to be quicker and more efficient? Is it to connect with and support others in more accessible ways? Or is it a mixture of both?
Tools don’t create cultures or transfer learning into classrooms; people do. No matter how sophisticated the AI platform, it can’t generate a culture of trust or reduce the transition time between lessons on a Tuesday morning in school. Although it will support these objectives, the ultimate drivers of improvement are the very human coach and teacher working in partnership, side by side.
Looking Outward to the Classroom
AI can illuminate what’s happening in the classroom, but it’s crucial to have a clear framework to guide the process. In the absence of one, coaching can turn into fragmented and never-completed coaching cycles. Jim Knight’s (2018) Impact Cycle provides just such a framework. The Impact Cycle guides both the teacher and coach through a structured partnership approach to student improvement. AI can help coaches efficiently complete coaching cycles by enabling them to become more strategic in establishing and maintaining the three stages of the cycle: Identify, Learn, and Improve.
Identify: See Patterns
The first phase of the Impact Cycle, Identify, begins with building a clear picture of the reality of the classroom. For example, a 5th-grade math teacher and coach are reviewing a video of a lesson the teacher taught; initially, they feel that the lesson went well and that students were independently working and engaged. But when they upload the video to an AI platform, the system reveals patterns that neither the teacher nor the coach had noticed.
AI can illuminate what’s happening in the classroom, but it’s crucial to have a clear framework to guide the process.
The data show that the teacher asked 47 questions during the lesson—89 percent of which were directed to the same eight students. During the 12-minute independent practice block, only 23 percent of students stayed on task for more than 5 minutes.
This is an example of the power of AI in the Identify stage. Video transcription surfaces dialogue patterns invisible in the moment. Engagement mapping reveals which students participate, and which withdraw. Exit ticket analysis uncovers misconceptions. And student work comparison shows who progresses and who struggles.
With these clear data, the coach and teacher can create a targeted coaching goal. Using Jim Knight’s (2018) PEERS goals—powerful, easy, emotionally compelling, reachable, and student-focused—a goal might sound like this: During independent practice, 80 percent of students will remain on task completing the required assignment(s). With AI support, PEERS goals become motivating, meaningful, and achievable.
Learn: Implement Strategies Effectively
Once they’ve decided on a goal, coaches and teachers move into the Learn stage; they prepare to implement a strategy with clarity and confidence. AI streamlines this preparation in two time-efficient ways. First, it can curate research-based strategies aligned to the PEERS goal by searching instructional playbooks or research databases, giving coaches access to proven practices without them having to engage in hours of manual curation. For instance, to target a PEERS goal focused on students’ time on task, AI can curate the strategy “Act, Talk, Move” from High-Impact Instruction (Knight, 2013). Second, AI can create checklists that break down the strategy into clear, actionable steps. This helps coaches clearly explain the strategy, which, in turn, helps teachers more fully understand it.
Improve: Analyze Progress and Adjust
During the Improve stage, the coach and teacher have a conversation about student progress, the data collected, and the results of the PEERS goal. AI simplifies the data collection piece. Coaches used to spend hours manually transcribing lessons or coding their observations. Now, they can upload a video of a lesson a teacher taught and prompt the AI tool to not only transcribe it, but also measure student engagement and analyze common trends in student responses and teacher questioning. This automation frees up the coach to focus on valuable coaching conversations. Using these data, the teacher and coach can make informed decisions about progress on PEERS goals.
AI can also compare the data with the PEERS goal to identify additional trends. For example, when focusing on a time-on-task goal, AI can reveal patterns across student groups, such as gifted and talented students or English language learners, or by gender, identifying when some groups become unengaged. AI can also flag when students go off task or what types of activities fail to hold their attention, such as worksheets. The different trends can paint a clearer picture of not just whether students are on task, but who is struggling and when.
Transformative coaching is built on trust, dialogue, and support from the coach. AI enables coaches to focus on what only humans can do: listen with care, ask powerful questions, and support student achievement with high-impact strategies.
How to Get Started
Learning something new—in this case, how a coach can best use AI—can be overwhelming. Psychologist Edgar Schein describes this as “learning anxiety” (Coutu, 2002); we can be afraid to try something new out of fear that it will be too difficult or because we may look deficient at whatever we’re learning. So, first, think about what may be holding you back or adding to your learning anxiety.
Revisiting your coaching beliefs and purpose can help you think about how AI can align with, rather than replace, your coaching. Identifying your strengths is a powerful place to start. Are you an effective listener? Do you build trust in partnerships? Do you show up with empathy and without judgment? AI tools cannot replace your personal strengths.
Next, identify how AI can enhance your coaching work. How might it support time management, resource gathering, or data organization? This is your starting point: understanding what only you can do and what AI might help you do more efficiently.
AI can support you as a coach by helping you:
- Organize coaching notes. Use meeting transcription tools like Otter.ai.
- Curate and create resources. Use AI to modify tools, locate materials, or adapt existing resources.
- Plan. Use AI to create or refine coaching workflows and questions, summarize notes, draft follow-up emails, create agendas, and anticipate teacher concerns.
Building competency with AI is about letting your coaching needs drive your tool choices, not letting a platform create needs you didn’t have.
Building competency with AI is about letting your coaching needs drive your tool choices, not the other way around.
Looking Inward to Our Practice
AI can help a teacher look outward to the classroom and see patterns they might not notice in the moment. It can do the same when we turn that lens on ourselves. We can record a coaching conversation we’ve had with a teacher to analyze ourselves, not the teacher. An AI tool can track the following metrics in a session:
- Your talk-to-listen ratio.
- How long you waited after asking a question before filling the silence.
- How often you offered a suggestion, solution, or strategy when you might have asked a clarifying question instead.
- The types of questions you asked during that conversation, and how that compares over time across the cycle with this teacher.
These patterns are nearly impossible to notice in the moment, yet they can shape every conversation we have. Structured reflective practice helps coaches refine their judgment and develop their craft (Hullinger et al., 2019). The challenge has always been finding the time to do this, as well as an honest mirror to do it well. When used intentionally and ethically, AI can offer both.
How to Get Started
Feed the AI tool a video of one of your coaching sessions (after obtaining permission from the teacher involved) and prompt it to analyze questions that opened possibilities, as well as those that shut them down. With Jim Knight’s (2018) Partnership Principles in mind—the first one focuses on equality—ask your AI tool to track how often you interrupted the teacher, where the teacher’s voice led the conversation, or where you took over. Given that the quality of the coaching relationship is consistently linked to better outcomes and fewer unintended negative effects (Graßmann et al., 2020), these small moments are worth examining.
Across several sessions, you may notice habits you unknowingly keep returning to. These could include using leading questions, overdoing or underdoing eye contact, responding without noticing that a coachee was about to expand on an answer, or taking up the greater part of the conversation. The point is to make reflection sharper and more honest so we’re able to refine our skills to better help the teachers we coach.
With intentional prompts and guardrails, AI can also help surface assumptions. Here’s an example of such a prompt:
Acting as an expert instructional coach with deep knowledge in dialogic coaching and Jim Knight’s Partnership Principles, analyze the transcripts from four recent coaching sessions. Focus on the principles of equality, particularly on how much of the talking I’m doing within the session. Where I do talk, help me see if it’s a question, statement, or minimal encourager. Share key insights and trends to help me reflect on my conversations. Include links to specific points in the transcript so I can check and review if needed. Don’t tell me what to do or evaluate my performance as a coach; instead, act as a reflective tool.
Sometimes a coaching relationship may feel stuck. Perhaps a coachee isn’t demonstrating progress toward their goal. Maybe they’re avoiding taking responsibility for creating new habits or failing to follow through on actions discussed in coaching sessions. Describe the situation and ask the AI tool to play devil’s advocate. What might you be missing? What are you assuming about this teacher that may not be true? How should you adapt your approach to work in partnership with that person?
A note of care runs through all this. The same contracting and confidentiality we establish with teachers must hold here, too. Be clear about what you record, what you don’t, what you put into a tool, and who has consented. Always check AI settings. For example, when using tools, turn off settings that allow your data to be used for training models, turn off activity tracking to prevent prompts from being connected to your account, and turn off “memories” so past conversations can’t be connected to new ones. Check and recheck this; trust is paramount. This is about you reflecting on your practice and growth; it’s not about surveillance.
The Unreplaceable You
AI can help us see outward to view the classroom more clearly. And it can help us see inward by holding up a mirror to our own coaching practice. In both cases, technology is the ally, never the agent that’s calling the shots. It may surface patterns, save time, and sharpen reflection, but it doesn’t build trust, ask the question that lands, or sit with a teacher in the silence where real thinking happens. That work is, and remains, ours. The most powerful thing we can do right now is protect the space for connection and conversation.
Let your coaching purpose drive your use of AI; it shouldn’t be the other way around. Growth does not happen on its own. It requires productive struggle, support, time, and genuine partnership within human moments. No algorithm can replace that.
