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In the era of remote education and digital solutions, there's a growing need for tools that not only support online teaching but also help monitor the quality of instruction and student engagement. More and more educational institutions - including language schools - are turning to advanced technologies like artificial intelligence to meet these demands. That was precisely the goal behind a project we had the pleasure of implementing for a language school offering both online and on-site classes.
Client challenges - what needed improvement?
The client, whose operations are based on a packed schedule of both online and in person classes, faced a number of difficulties. Most notably, they lacked effective tools for monitoring lessons and evaluating instructors. Session recordings and notes were reviewed manually, which generated significant time and operational costs. There was also no consistent system for measuring student engagement or analyzing progress based on actual lesson content.
Another issue was the lack of a reliable text-based meeting history - teacher notes were often too general or subjective. This limited the ability of academic coordinators to make informed decisions and made it harder to provide students with meaningful feedback.
Project goal - AI-based automatic lesson analysis
Our task was to implement a solution that would automate the transcription and analysis of lessons, extract key insights from the class flow, and turn them into actionable analytics. We wanted to create a system that not only processed lesson content, but also recognized speakers, tracked conversation dynamics, and generated clear reports - tailored to the needs of both instructors and methodologists.
The project was designed to leverage the full potential of AI-powered tools - from automated transcription via speech-to-text algorithms, through word analysis using NLP (Natural Language Processing), to data insights generated by large language models (LLMs).
What did the technical implementation look like?
The first step was integrating the system with Google Meet, the video conferencing platform used by the school for online classes. For session recording and transcription, we used the fireflies.ai plugin. Thanks to direct API access, we could retrieve both the audio recordings and their textual representation.
For in-person lessons, a simple mobile app recorded audio during class, which was then saved and sent via API to fireflies.ai, where it was processed just like the data captured during online sessions.
The next step involved preparing transcriptions and an initial analysis - this was handled by fireflies' AI models, which performed speech-to-text operations along with a breakdown of how much each participant spoke. The number of people in the meeting didn't matter - the algorithms could identify multiple speakers and summarize their contributions (who spoke and for how long).
Next, the content of the session was analyzed using advanced NLP and LLM models, including those from OpenAI and Gemini. The system was able to recognize the topics discussed and evaluate the dynamics of the conversation, enabling more accurate conclusions about each participant's activity and engagement.
After every class, a detailed report was generated, including both numerical and textual insights, along with tailored recommendations - for both the teacher and the student. All data was stored in a centralized database, making it possible to analyze it later in a broader context.
Discover how AI can revolutionise the learning process.
Implementation results - faster, smarter, more objective
Using artificial intelligence reduced the time needed to analyze a single lesson from about 20 minutes to just 3. Automating the processing and report generation significantly eased the burden on the academic team, allowing them to focus on the substance of teaching. Manual, time-consuming analyses were replaced with automated workflows.
What's more, the system achieved over 95% accuracy in identifying individual participant activity, leading to a level of precision that hadn't been possible before - the institution had never tracked lesson dynamics with this level of detail.
With reports generated after each lesson, instructors received direct feedback on their teaching approach, and students received personalized recommendations for further learning. A key factor was that all reports and analyses were based on hard data, not on the subjective judgment of the teacher - which had previously been the case. Thanks to AI, data was interpreted objectively and consistently for all participants.
| BEFORE | AFTER |
|---|---|
| Manual and time-consuming analysis of recordings and notes | Automated transcription and analysis - time reduced from 20 minutes to just 3 |
| Lack of effective tools to monitor lessons | AI-powered system that tracks conversation flow, identifies speakers and generates reports |
| Subjective and often vague assessment of teacher performance | Objective data automatically extracted from each lesson |
| No consistent system for measuring student engagement | Conversation dynamics and participant activity analyzed using NLP and LLM |
| No complete text record of meetings beyond teacher notes | Full speaker-identified transcription of each lesson |
Added value - what did the client gain?
Thanks to the implemented solution, the client gained full control over the quality of delivered lessons. Teacher evaluations became objective, data-driven, and no longer required time-consuming review of recordings. Students began receiving more meaningful feedback, which positively impacted their engagement and learning outcomes - the school could now demonstrate higher teaching effectiveness. It also became possible to better match teaching materials to students' actual needs, improving overall satisfaction and customer loyalty.
An additional advantage was the improved efficiency of the academic team, which could now analyze several times more lessons in the same amount of time. From a marketing perspective, the school also gained a unique differentiator - the ability to position itself as a modern institution that integrates artificial intelligence into everyday teaching practice. This type of messaging helped attract new clients.
What's next - beyond transcription
We're currently working on new analytical modules that will take the system even further. Upcoming features include:
- Emotion detection in students' voices - helping teachers gauge how confident a learner feels about specific topics and tailor their approach accordingly;
- Language accuracy analysis - the system will highlight areas where students struggle the most and provide teachers with suggestions for targeted improvements;
- An expanded dashboard for the academic team, offering insights like teacher performance rankings and lesson quality metrics, supporting continuous teaching improvement.
All these features are designed to enhance the learning process even further and fully unlock the potential of the data generated during each lesson.
Summary - AI as real support for education
The implementation of an AI-based lesson analysis system demonstrates that technology can meaningfully support education - not only in online settings, but also in traditional, in-person learning environments. By leveraging advanced language models and automation, it's possible to both ease the workload of academic teams and elevate the overall quality of teaching.
From a business perspective, integrating AI in education is no longer just an option - it's becoming essential for institutions that aim to stay competitive, modern, and efficient. Public awareness of AI's effectiveness in education is growing, making it a powerful message in marketing communications that attract new students and clients.
If your organization offers educational programs and you'd like to harness the full value of lesson-generated data, get in touch with us - we'll show you how to turn data into a real competitive edge.
FAQ
Artificial intelligence addresses challenges such as the lack of objective teacher assessment, time consuming manual analysis of lesson recordings, limited and subjective student feedback, and difficulties in tracking progress during both online and classroom lessons.
The AI system automatically transcribes lessons, analyzes participants' statements, identifies speakers, and generates reports summarizing the sessions. As a result, academic coordinators and teachers receive detailed data on student engagement and teaching quality.
In this project, tools such as fireflies.ai (for transcription and speech analysis), NLP and LLM models (e.g., OpenAI, Gemini), integration with Google Meet, and mobile apps for recording in-person sessions were used.
Using AI reduces lesson analysis time from 20 to just 3 minutes, ensures objective insights, improves the precision of feedback, and enhances both the quality of teaching and student engagement.
AI improves the efficiency of the academic team, simplifies quality monitoring, supports learning personalization, and helps position the school as a modern and innovative educational provider.
AI can support both online and in-person learning formats.
The cost depends on the scope and level of integration, but automation makes lesson analysis significantly faster and more cost effective. The investment quickly pays off by improving learning outcomes and saving the teaching team valuable time.
Features worth considering include: emotion detection in students' voices, evaluation of language accuracy, and a dashboard with teacher rankings, all designed to better support each student's educational development.
By analyzing lesson data, educators can tailor materials to individual needs, offer more targeted development feedback, and respond faster to learning challenges, all of which enhance educational outcomes.
Yes, any educational organization can adopt an AI-based lesson analysis solution. It's an effective way to increase competitiveness, improve teaching quality, and enhance student experience in both online and offline models.
