How Does AI Help with Instructional Coaching?

Specific Ways AI Supports the Coaching Cycle

Instructional coaching involves a recurring cycle of observation, feedback, goal-setting, and follow-up. AI can enhance nearly every stage of this cycle without changing the coach’s fundamental role.

• Real-time note capture and transcription. During classroom visits, coaches can use AI-powered tools to capture notes via text or voice. AI transcribes and organizes these observations so coaches can stay focused on what’s happening in the classroom rather than worrying about getting every detail down in writing. Raw bullet points, shorthand, and voice memos are all transformed into structured evidence after the visit.
• Pattern recognition across observations. One of the most powerful applications of AI in coaching is the ability to surface patterns that would be difficult to see manually. When a coach visits 15 or 20 teachers across multiple schools, AI can identify trends—like widespread challenges with student engagement strategies or consistent strengths in formative assessment practices—that inform both individual coaching and school-wide professional development planning.
• Feedback drafting and framework alignment. After an observation, AI can draft feedback that maps evidence to the district’s instructional framework (Danielson, Marzano, CSTPs, UDL, or custom rubrics). This saves coaches significant time on the writing process while ensuring feedback is specific, evidence-based, and connected to the language teachers already know.
• Longitudinal growth tracking. AI maintains a running profile of each teacher’s observations, goals, and feedback over time. Instead of hunting through email threads, shared docs, and spreadsheets, coaches can quickly review a teacher’s growth trajectory, see which goals have been met, and identify areas where additional support is needed.
• Goal alignment and progress monitoring. AI can connect individual observation evidence to the teacher’s stated professional goals, making it easy to see whether classroom practice is moving in the direction the teacher and coach agreed on. This keeps coaching conversations focused and forward-looking.

How Trellis Uses AI: Enhancing Judgment, Not Replacing It

A common concern about AI in education is that it might replace human judgment. Trellis takes a fundamentally different approach: AI is a drafting and analysis tool, not a decision-maker. Every piece of feedback generated by Trellis is reviewed, edited, and approved by the coach or leader before it reaches a teacher.

Here is how Trellis specifically uses AI in the coaching workflow:

• The coach captures evidence during the visit using Trellis on their phone or tablet—quick notes, tagged observations, or voice recordings. The focus is on being present in the classroom, not on producing polished documentation.
• AI transforms raw notes into structured feedback. After the visit, Trellis’s AI reviews the evidence, maps it to the relevant instructional framework domains, surfaces strengths, and suggests growth-focused next steps. The output is a draft—a starting point for the coach to refine, not a finished product.
• The coach reviews, edits, and personalizes. Before any feedback is shared, the coach reads the AI’s draft and adjusts it. They might reframe a suggestion to match the teacher’s current context, add a personal note referencing a previous conversation, or change the tone to match the relationship they’ve built.
• The teacher receives timely, specific feedback. The final report reaches the teacher while the visit is still fresh—typically the same day. Because the coach spent their energy on refining the message rather than starting from scratch, the feedback is both faster and more thoughtful.

ToneBoost: Matching the Coach’s Voice

One of the biggest risks with AI-generated text is that it can sound generic—like it was written by a machine rather than a person who knows the teacher. Trellis addresses this with ToneBoost, a feature that learns the coach’s communication style over time.

ToneBoost analyzes how a coach naturally writes feedback—their word choices, sentence structures, and framing preferences—and adjusts AI-generated drafts to match. The result is feedback that sounds like the coach wrote it, because the AI is adapting to their voice rather than imposing a generic style.

This matters for coaching because trust is relational. Teachers respond to feedback differently when it feels like it came from someone who knows them and their classroom. ToneBoost ensures that the efficiency gains of AI don’t come at the cost of the personal touch that makes coaching effective.

Connecting Evidence to Growth Goals

Effective coaching isn’t just about giving feedback on a single lesson—it’s about connecting daily classroom practice to larger professional growth goals. AI makes this connection easier by automatically linking observation evidence to the goals a teacher and coach have established together.

For example, if a teacher’s growth goal is to increase student discourse in their classroom, Trellis can flag observation evidence related to that goal—like instances of student-to-student discussion, questioning strategies, or wait time—and surface them prominently in the feedback report. Over multiple observations, the teacher and coach can see a clear picture of progress, sticking points, and next steps.

This evidence-to-goal connection also helps coaches prepare for post-observation conversations. Instead of spending 20 minutes reviewing notes before a meeting, coaches can pull up the teacher’s profile and immediately see how recent evidence connects to their ongoing development priorities.

Aligning Coaching with District and School Priorities

Instructional coaches don’t work in isolation. Their work needs to align with school improvement plans, district instructional priorities, and building-level focus areas. AI helps bridge this alignment gap in several ways:

• District priority integration. Trellis allows districts to define instructional priorities (for example, “increase use of formative assessment” or “strengthen Tier 1 differentiation”), and AI incorporates these priorities into feedback. When evidence from an observation connects to a district focus area, it’s highlighted automatically.
• School-level focus areas. Individual schools can set their own priorities within the district framework. A school focused on SEL integration will see feedback that surfaces evidence related to social-emotional learning practices, while a school focused on literacy across content areas will see different connections highlighted.
• Aggregated trend data. When coaches observe across multiple classrooms, AI can aggregate trends and surface insights at the school or district level. This data helps instructional leadership teams make informed decisions about professional development, resource allocation, and coaching focus.
• Coherent messaging across the coaching team. When multiple coaches use the same platform with shared framework configurations and priority settings, the feedback teachers receive is consistent in its framing and alignment—even when different coaches are delivering it.

What AI Cannot Replace in Coaching

It’s important to be clear about what AI does well and where human judgment remains essential. AI excels at processing information, identifying patterns, and drafting text quickly. It does not replace the coach’s ability to:

• Build trust and rapport with teachers through consistent, honest relationships.
• Read the emotional context of a classroom visit and adjust the coaching conversation accordingly.
• Make strategic decisions about when to push and when to affirm.
• Navigate the interpersonal dynamics of adult learning and professional vulnerability.
• Model instructional practices or co-teach alongside a teacher.

The most effective use of AI in coaching is when it handles the administrative work—organizing notes, drafting language, tracking goals—so that coaches can invest their limited time and energy in the relational and strategic work that only humans can do.