Release Notes: Smart Classroom#

Version 2026.2#

Release Date: September 9, 2026

Smart Classroom 2026.2 refactors the backend around a modular feature-module architecture and adds two new classroom AI capabilities — VLM-based exam grading with graded reports and Board (content-screen) OCR. It also introduces a cross-platform Flutter application for Content Search. All new capabilities run locally on Intel hardware using OpenVINO.

New:

  • Flutter Content Search application — a cross-platform (Windows Desktop and Web) Flutter + Riverpod client for the Content Search backend, delivering file ingestion, RAG-powered Q&A with cited sources, multi-turn conversations, and file management.

    • Runs from its own dedicated config.yaml so it can be deployed independently of the main Smart Classroom application.

    • Talks to a lightweight standalone VLM service (Qwen3-VL-8B) instead of requiring the full application stack, for faster startup and lower resource use.

    • Adds an agentic Coding Companion mode with skills that drive setup, ingestion, Q&A, file management, and health diagnostics from natural-language commands.

  • VLM-based exam grading and graded reports — a new grading service that scores student papers against a teacher-provided rubric and produces per-student graded reports.

    • Runs as its own feature/service, gated by the grading feature flag.

    • Supports single-column and two-column exam page layouts.

    • Accepts student papers organized by directory (one folder per student).

    • Adds Qwen3.5 9B VLM support with configurable INT8/INT4 quantization.

    • Ships sample rubrics and sample exams for a guided quick test.

  • Board OCR — extracts text written on the classroom content/whiteboard screen from an RTSP stream or recorded video.

    • Extracts frames using FFmpeg + Intel QSV and feeds them to an OCR reader with text normalization and deduplication.

    • Integrated into the audio-summary pipeline so recognized board text enriches the generated class summary.

Improved:

  • Modular feature-module architecture — the backend is now composed of self-contained feature modules (asr, summary, mindmap, topic_segmentation, video_analytics, board_ocr, content_search, qa, grading, report), each exposing a common interface (router, build/teardown, and a UI descriptor).

    • Features are toggled independently via the features block in config.yaml.

    • A dependency-ordered registry bootstraps only the enabled features and detects dependency cycles at startup.

  • Unified Content Search Python environment so that Content Search shares a single managed virtual environment with the rest of the application.

  • Externalized LLM prompts to make prompt tuning easier without code changes.

  • Health checks hardened against proxy interference.

Version 2026.1#

Release Date: June 17, 2026

Smart Classroom 2026.1, a modular, extensible framework for the Windows OS, adds a Content Search subsystem, document upload, text/image retrieval, OCR, QnA, and multilingual processing including Mandarin/Chinese. This release also adds WebRTC WHEP streaming, Intel Wildcat Lake platform support, and updates to audio transcription.

New:

  • Content Search module for uploading documents and media, indexing them with OpenVINO™-accelerated embedding models, and retrieving results by text or image query.

  • Q&A support in Content Search for querying uploaded content with locally running LLMs.

  • OCR in Content Search with OpenVINO™ and PaddleOCR for printed and handwritten documents.

  • Mandarin/Chinese language support for QnA and transcription pipelines.

  • File listing and file removal endpoints in the Content Search API.

  • Config API endpoint in Content Search for runtime search and embedding settings.

  • WebRTC WHEP streaming for low-latency live video delivery.

  • Intel(R) Core(TM) Series 3 (Wildcat Lake) processor support.

  • Video start and end timestamps in Content Search results for precise navigation.

  • Resource utilization monitoring to cap pipeline resource usage.

Improved:

  • Long audio transcription is now supported with and without speaker diarization.

  • RTSP playback mode and file duration validation in the video ingestion pipeline.

  • Content Search embedding models upgraded to multilingual and cross-lingual models.

  • Batch embedding insertion for improved indexing throughput on large document sets.

  • Local PyAnnote audio model caching for offline diarization loading.

  • Pipeline startup reliability under concurrent load.

  • YOLO model configuration and quantization setting updates.

  • CMake configuration and build scripts for the gvasmartclassroom GStreamer plugin.

  • Error tracking for pipeline status reporting.

  • Video summarization output consistency.

  • UI behavior to stop active streaming sessions automatically after 10 minutes.

  • Support for DL Streamer 2026.1.

  • Summary scoring formula updates and reranker configuration refactoring.

  • Content Search download streaming optimization to reduce memory overhead for large file transfers.

Fixed:

  • Crash in the video analytics pipeline.

  • Noise in per-class attendance statistics in the video pipeline.

  • Content Search cleanup when no task ID was present.

  • Duplicate file entries caused by missing task IDs during upload.

  • Inference request conflicts when embedding multiple documents concurrently.

  • Incorrect video scaling in the HLS player at some resolutions.

  • Encoding errors in video analytics output.

  • Incorrect YOLO model quantization results.

  • Content Search upload and delete operations under some conditions.

  • Corrupted file handling in Content Search that could cause indexing failures.

  • Hardcoded model name in the startup script; model name is now read from configuration.

Version 2026.0#

Release Date: April 1, 2026

The Smart Classroom application now offers a series after-class summary enhancements in the form of next‑generation real-time audio and visual analytics, giving teachers and schools a better understanding of classroom dynamics through AI‑driven summaries and engagement metrics.

The Education AI Suite now also includes built-in telemetry hooks and benchmarking.

New:

  • Speaker Diarization (via the Audio Pipeline):

    • identifies teacher and student speakers using NPU-accelerated diarization

    • generates an interactive audio timeline for replay and analysis

    • enables time-coded navigation within class video recordings

  • Class Engagement Metrics – Audio:

    • measure teacher and student speech duration

    • track questions asked and answered

    • track student-teacher interaction frequency

  • Class Engagement Metrics – Video:

    • track student hand raises

    • track posture changes (stand up/sit down)

    • track teacher movement

  • Built‑in telemetry to measure classroom workloads across Intel platforms (CPU core utilization, iGPU load, NPU load, memory usage, workload-specific performance counters)

  • Benchmarking scripts to reproduce Intel internal performance measurements, and validate XPU performance

Improved:

  • Knowledge Graph UI readability and formatting, and increased clarity when visualizing topic relationships

Documentation and Source Code#

Previous releases#