Streamlining Instructional Coaching With Tech for Systemwide Change

As an education consultant, I work with several school communities committed to providing additional coaching support to their teachers. These schools have discovered that one way to do this, without breaking the bank, is to provide stipends to teachers interested in growing their leadership skills as non-evaluative teacher mentors.

Yet, although the desire is there, I’ve seen in-house instructional coaching quickly devolve to mentor teachers providing ad-hoc advice to teachers, catch as catch can, with fidelity being tied to their individual habits, systems, or documentation practices. The unfortunate result is that coaching often becomes an activity rather than a strategy.

So how can schools increase visibility across coaching teams so that recurring teacher needs and common instructional trends can be surfaced and school improvement priorities set? And when multiple coaches are working within a school or district, how can we know the system itself is moving in the right direction, based on what we’re learning?

In my experience, careful and targeted use of technology can turn instructional coaching into a systemic school improvement engine.

Coaching Data Is Organizational Intelligence

Coaching data should not live in isolated notebooks. Walk-through notes, coaching conversations, teacher goals, implementation evidence, classroom observation trends, teacher requests for support, and student work samples together define school improvement intelligence. To ensure that coaching data leads to instructional impact, coaching teams must first synthesize what their role as a team is, what effective instruction looks like, how they will engage in providing teachers with support (e.g., through observation and feedback or by modeling best practices), and how they will engage in collective reflection.

Tools That Make Organizational Intelligence Possible

In my ongoing work with coaching teams, I recommend the use of various Google tools to centralize the work of individual coaches. Google Docs, for instance, can be used as an online hub for shared coaching notes, teacher action plans, and collaborative reflection and notes from team meetings.

Meanwhile, Google Sheets can be used to track trends in instruction, such as questioning, differentiation, student discourse, and formative assessment, as well as coaching cycles. Google Sheets can also help teams tag instructional focus areas and monitor implementation patterns.

Google Forms can help teams to standardize walk-through data collection, coaching intake information, and teacher self-reflection. My mentor teams usually have an assigned data/technology coordinator who can pull up reports during meetings, a role that rotates among them, so that when coaching impact is discussed, it is based on instructional data and not perception.

Further, through shared visibility, coaches more easily learn from the successes and challenges of other mentors and can take their colleagues’ best practices and employ them in their own coaching. When data indicates that teacher coaching practices are positively correlated to student growth and achievement, teams can standardize those practices. It’s important to ensure that similar data points are collected in similar ways for all mentors.

AI as the Next Layer: Synthesizing the Data

Practical tools can help teacher coaching teams centralize and share coaching data; AI can help teams analyze that data quickly. This allows teacher coaching teams to spend their limited time together in defining next steps based on this data. I often use my own AI coaching agent with the mentor teams I support. To help teams determine instructional themes and implementation gaps that are emerging across grade levels and content areas, AI also can plan out what professional development topics should be prioritized and modeled by teacher mentors.

NotebookLM, Google’s AI-powered research and note-taking tool, allows teams to work with their own sources rather than just the open web. In addition to summarizing content and answering questions grounded in the resources provided, it can generate briefing notes and audio podcast overviews of a team’s school improvement artifacts. (Although Google states that sources and prompts provided by individual NotebookLM users are not used to train its AI model, coaching teams should be careful, as with any AI platform, not to provide AI agents with student names or other private student data.)

Calibration Through Video and Shared Analysis

Calibration improves coaching quality. However, one challenge I routinely face when developing teacher mentors is finding the time to conduct inter-visitations together given class coverage gaps and other demands on teacher mentors’ time. Even when teacher mentors can collectively observe instruction, it can be difficult to fully analyze instruction in real time, particularly if mentors are also completing walk-through forms. Watching classroom videos collaboratively can therefore improve shared analysis, especially if team discussions include the use of a common observation protocol that allows each coach to compare notices. This, in turn, can assist teacher mentors in aligning feedback language and reducing inconsistencies in coaching practice from one coach to another.

I recommend that teacher mentor teams record themselves modeling best practice. When coaches create norms around what the best practices are—strong questioning, checks on student understanding, and student discourse, for example—they become less idiosyncratic. The videos then serve as a coaching tool that mentored teachers can access asynchronously.

Further, using video in feedback conversations with teachers allows them to be fully present when reviewing their own instruction. This turns feedback from something that is provided to teachers into a professional development practice that is done with them, collaboratively.

Leadership Implications

Teacher mentorship should be about transformation of teacher practice, not surveillance. Using technology to make the work of teacher coaching teams organized and transparent gives school leaders the opportunity to align coaching with school goals and pinpoint shared instructional needs without micromanaging.

Too often, school leaders both evaluate and mentor teachers, stretching their capabilities thin, muddying what truly makes coaching impactful—trust and agency. In schools with coaching that is embedded within coherent learning systems, evaluation of teaching practice can be separated, cleanly, from that of teacher professional development. Technology should never replace one-on-one coaching relationships. What it should do is capture what is and is not working within coaching, so that the school can learn alongside the individual teacher being coached.