Baidu AI Cloud Education China Translated from Chinese

Harbin Zhaolin Middle School

Harbin Zhaolin Middle School wanted to bring AI into everyday teaching and campus practice, but most teachers lacked AI knowledge, suitable classroom tools, and a workable method to connect emerging technology with lesson delivery, student engagement, and school management.

Information-technology teacher training pass rate reached 100%.
Lesson-preparation efficiency improved 40%.
Overall teacher AI capability training pass rate reached 75%.
Student participation increased by 90%.

Reported impact

Business outcome

The school raised teacher AI readiness, improved lesson-preparation efficiency, and made AI classes far more interactive, giving students richer exposure to applied technology across multiple subjects.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Harbin Zhaolin Middle School partnered with Baidu Education to deploy an AI Innovation Lab that combines curriculum resources, a teaching platform, hands-on training projects, and immersive experiences in one smart-classroom environment. The implementation is based on Baidu's Artificial Intelligence Education Solution and Industry-Education Integration Solution, which package AI, robotics, big data, IoT, and brain-science technologies into an environment that supports classroom teaching, STEAM education, and teacher capability training. The school uses an online AI teaching-resource platform together with offline hardware teaching aids, AI classroom tools, smart speakers, and AR experience devices so teachers can learn the technology and then apply it directly in English, biology, and other classes.

Data flow

Baidu's online teaching resources, lesson materials, and training content are delivered through the AI Innovation Lab, where teachers use the platform and connected classroom devices to prepare lessons, run AI-enabled classroom activities, and give students hands-on exposure to smart technologies.

Solution architecture

3 components · 2 layers
  1. Serving
    • Artificial Intelligence Education Solution Provides the smart-education framework used for AI teaching, teacher training, and classroom integration.
    • Baidu Education AI Innovation Lab Acts as the physical and digital teaching environment that combines online resources with offline devices and practical labs.
  2. Orchestration
    • Industry-Education Integration Solution Packages curriculum, training projects, and applied teaching workflows for school AI programs.

Architecture clues

  • Classroom examples include XiaoDu-based spoken-English practice and AR-based biology visualization
  • The lab combines online resource delivery with offline soft-hardware practice
  • The same environment supports AI teaching, STEAM education, and new-technology teacher training

Evidence from the source

After the AI Innovation Lab was built, teacher AI teaching ability and AI technology application ability improved through several rounds of training (original: 哈尔滨兆麟中学的百度教育 AI 创新实验室建成之后,通过几次新技术能力培训,提升了教师的人工智能教学能力及人工智能技术的应用能力).
Metrics included 75% overall teacher pass rate, 100% information-technology teacher pass rate, 40% higher lesson-preparation efficiency, and 90% higher student participation (original: 教师 AI 能力培训考核通过率达到 75%,信息技术教师考核通过率达 100% / 40% 备课效率提升 / +90% 学生参与度).
Teachers used Baidu's AI teaching platform, hardware teaching aids, and curriculum system to run AI classes (original: 教师也熟悉掌握了利用百度提供的 AI 教学平台、配套硬件教具及课程体系开展人工智能课程).
The solution integrates AI, robotics, big data, IoT, and brain science with curriculum resources, a teaching platform, training projects, and virtual experience (original: 这是将基于人工智能、机器人、大数据、物联网、脑科学等新兴技术整合起来,集课程资源、教学平台、实训项目和虚拟体验为一体的智慧教育解决方案).
Transparency Evidence & methodology 95/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    Harbin Zhaolin Middle School customer story

    First-party
    https://cloud.baidu.com/customer/case/zhaolin.html
    Source type
    Vendor-published customer story
    Published
    May 14, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page May 14, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.