The Evolution of Classroom Observation: From Human Eyes to Artificial Intelligence | Circles Learning
Analysis
The Evolution of Classroom Observation: From Human Eyes to Artificial Intelligence
Publicado por Sebastián Marambio••5 minutos de lectura
TLDR: Traditional classroom observation is infrequent and often anxiety-inducing for teachers. Technology has evolved from basic video recording to AI-powered systems that can now analyze both audio and visual aspects of teaching. This evolution includes systems like Swivl for automated recording and TeachFX for analyzing classroom talk patterns. Recent advances in AI have democratized these technologies, making high-quality feedback accessible to all teachers regardless of school resources. The ultimate goal is transforming observation from an evaluative process to a supportive one that fosters continuous professional growth.
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Teaching is, paradoxically, both a deeply collaborative profession and an isolating one. Once that classroom door closes, many teachers find themselves essentially alone with their students, day after day, with minimal external feedback on their practice.
Research consistently shows that teachers (like all people) learn through meaningful and frequent feedback. We know this as educators. Feedback is essential for learning, regardless of age or profession. Yet the traditional model of classroom observation (an administrator or coach visiting perhaps once or twice a year) creates a scarcity mindset around this vital professional resource.
This scarcity transforms what should be routine professional development into high-stakes evaluation. When feedback is rare, it becomes threatening rather than nurturing. Teachers naturally develop resistance to observation, viewing it as assessment rather than support. This dynamic undermines the very purpose of classroom observation and creates a climate where innovation and risk-taking are stifled rather than encouraged.
The Evolution of Observation Technology
Over the past decade, technology has begun to reshape this landscape through several waves of innovation, each addressing different aspects of the observation challenge:
The initial technological approach to expanding classroom observation was straightforward: record teaching sessions. This seemingly simple innovation offered powerful benefits. For the first time, teachers could observe themselves from the student perspective, creating opportunities for self-reflection impossible in the moment of teaching. Coaches and mentors gained the ability to review specific teaching moments, providing more concrete, evidence-based feedback.
However, these early systems were often cumbersome, requiring significant setup and technical knowledge. The recording equipment itself could be intrusive in the classroom, creating an unnatural environment. And perhaps most significantly, the mountain of resulting video footage required hours of human review—creating new bottlenecks in the process.
Around the mid-2010s, a more sophisticated approach emerged with systems like Swivl, a robotic mount that transformed an ordinary tablet or smartphone into an automated recording system. Swivl represented a significant step forward in lowering the technical barriers to classroom recording.
The Swivl system includes a robotic base that holds a recording device and tracks teacher movement via a small marker worn by the instructor. This innovation addressed several key limitations of earlier recording approaches: it required minimal setup, followed the teacher automatically throughout the classroom, and even captured high-quality audio through microphones in the tracking marker.
Swivl robot automatically tracking a teacher during a classroom session.
The impact was substantial: teachers could now easily record their own classes without assistance, review the footage at their convenience, and share it selectively with trusted colleagues or coaches. Rather than a special event requiring external support, classroom recording became an everyday tool for reflective practice.
Yet even with these improvements, the fundamental challenge remained: analyzing teaching practice from hours of video still required significant human time and expertise.
In 2017, a significant innovation emerged to address the analysis bottleneck. TeachFX, founded by former teacher Jamie Poskin, introduced an AI approach that focused specifically on classroom audio. By analyzing teacher and student talk patterns, TeachFX could automatically generate insights about classroom dynamics that previously required hours of human observation.
The approach was elegant in its simplicity. Teachers record their classroom audio through a smartphone app, and TeachFX's AI analyzes the recording to provide metrics on teacher talk time versus student talk time, types of questions asked, wait time after questions, and other critical pedagogical patterns.
This innovation represented a fundamental shift in the classroom observation paradigm, from sporadic, human-intensive evaluation to consistent, automated feedback. For many teachers, this was the first time they had regular access to objective data about their teaching practice. The insights could be surprising; many discovered they dominated classroom discussion far more than they realized, or that they called disproportionately on certain students while unconsciously overlooking others.
While these systems focused primarily on audio analysis, they demonstrated the potential for artificial intelligence to transform teacher feedback by making it more accessible, regular, and data-driven.
While leading Chile's Center for Educational Innovation at the Ministry of Education from 2018 to 2022, I advocated for these emerging technologies as tools to support teacher growth. We recognized the fundamental challenge: teachers in Chilean schools, like their counterparts worldwide, received too little feedback, too infrequently.
We initiated pilot programs with schools across the country, introducing video recording systems and audio analysis tools to support teacher development. The results were promising. Teachers who initially approached the technology with skepticism often became its strongest advocates once they experienced the benefits of regular, non-judgmental feedback.
The Chilean experience highlighted a crucial insight: technology alone doesn't change practice; it requires thoughtful implementation that centers teacher agency and creates safe spaces for professional growth. When teachers saw these tools as supportive rather than evaluative, they embraced the opportunity to improve their practice.
The AI Revolution in Teacher Observation
In just the past year and a half, we've witnessed a seismic shift in the landscape of classroom observation technology. The fundamental economics of AI-powered solutions have been completely transformed by massive investments from companies like OpenAI, Anthropic, Meta, Google, and DeepMind. These organizations have developed powerful foundation models that are now accessible to anyone at remarkably low cost, creating a democratization of advanced AI capabilities that was unimaginable just a few years ago.
This transformation has profound implications for educational technology. Previously, companies like TeachFX needed to invest substantial resources in developing proprietary AI models specifically for educational applications. Today, anyone can build similar—or even more advanced—systems by leveraging these foundation models as building blocks, dramatically lowering the barriers to entry and accelerating innovation.
A teacher in front of her students. Seen through the lens of a recording camera.
What makes this revolution particularly powerful is the multimodal nature of modern AI systems. These models excel not only at transcription and audio analysis but also at understanding visual information. This multimodal capability opens entirely new dimensions for classroom observation that were previously inaccessible:
Video analysis can reveal classroom configurations. For example, whether students are arranged in collaborative groups or traditional rows
AI can evaluate nonverbal aspects of teaching, from teacher movement patterns to student engagement signals
Systems can correlate visual and audio data to provide richer context about teaching dynamics
We are now seeing an emergence of tools that build upon these foundation models to create powerful, user-friendly experiences for teachers and coaches. TeachView, developed by Circles, represents one example of this new generation of observation tools. Unlike previous systems limited to predefined metrics, TeachView allows schools to define their own observation protocols or adapt existing ones to their specific contexts.
What truly distinguishes these new AI-powered systems is their conversational nature. Rather than providing static reports, teachers and coaches can ask the AI specific questions about the teaching session in natural language: "Show me three moments when I asked higher-order thinking questions," "Identify instances where I differentiated instruction for struggling students," or "Help me find examples of effective classroom management techniques I used." The AI can instantly locate and present these moments, making feedback more personalized and actionable than ever before.
This capability transforms the observation process from one of generalized feedback to targeted, personalized professional growth aligned with each teacher's development goals. It shifts the power dynamic of observation, putting teachers in control of their own professional learning journey while still providing school leaders with valuable insights about instructional trends across their schools.
And perhaps most significantly, the psychological calculus of classroom observation begins to change. When feedback becomes abundant rather than scarce, it transforms from evaluation to support. The pressure diminishes, and openness to growth can flourish.
The trajectory of classroom observation technology follows a familiar pattern in innovation, from specialized, expensive, and complex to accessible, affordable, and simple. What began as an elite practice requiring trained observers has evolved into a democratic tool available to any teacher with a smartphone.
This democratization addresses a fundamental equity issue in professional development. Historically, the highest quality feedback was concentrated in well-resourced schools that could afford instructional coaches and extensive observation programs. Technology now makes it possible to provide meaningful feedback to every teacher, regardless of their school's resources.
AI doesn't replace human coaching but makes it more effective by focusing it on what matters most. Coaches no longer need to spend their limited time collecting basic data; instead, they can focus on helping teachers interpret patterns, develop strategies, and implement changes. The conversation elevates from "here's what happened in your classroom" to "here's what it means and how we might improve it."
We are moving from a world of feedback scarcity to one of feedback abundance, with profound implications for the teaching profession. Three key shifts stand out:
From evaluation to growth: When feedback becomes regular and low-stakes, it naturally shifts from judgmental to developmental.
From isolation to connection: Technology creates new possibilities for teachers to share practice and learn from each other, breaking down classroom walls.
From intuition to evidence: Teachers gain access to objective data about their practice, complementing their subjective experience.
As we look to the future, the ultimate promise is a teaching profession that continuously improves through feedback, just as we expect our students to grow through the feedback we provide them. The historical barriers—resource limitations, technological complexity, and psychological resistance—are gradually dissolving.
Great teaching has always been partly art and partly science. The art will always require human judgment, creativity, and connection. But the science, understanding patterns of effective instruction, can now be supported by technology in ways that make the art more accessible to all teachers.
For education leaders, the message is clear: we no longer need to accept the false choice between quality and scale in teacher feedback. Technology has created a third path: high-quality, personalized feedback at scale. Our responsibility now is to implement these tools thoughtfully, with careful attention to teacher agency, supportive implementation, and ethical use of data.
The classroom observation of tomorrow won't just be more efficient but more human, focusing precious face-to-face time on the aspects of teaching that technology can never capture: the passion, purpose, and personal connection that draws us all to this profession.
