Case study · Education
Teacher timegiven back to teaching.
A municipal commission of education overseeing a large school district where significant gaps had opened between schools in teaching quality, access to learning materials and instructional support. Teachers spent a large share of their time on lesson preparation, grading and administration, which limited what they could give to student development. Large class sizes and diverse learning needs made individualized teaching hard to deliver, and rising student mental health challenges needed early detection the district did not have.
Constraints and answers
The gap between schools is a resource gap first.
The platform that gave the hours back
Uneven distribution of educational resources
Significant gaps existed between schools in teaching quality, learning materials and instructional support.
Heavy teacher workload
Extensive time on lesson preparation, grading and administrative work limited the focus teachers could give to student development.
Need for personalized learning
Large class sizes and diverse learning needs made individualized teaching difficult to achieve.
Rising mental health challenges
Student emotional issues became more prevalent while early detection capability stayed limited.
AI lesson preparation assistant
Lesson plans, exercises and teaching slides generated automatically, so preparation stops consuming the evening.
Intelligent classroom quality analytics
Real-time evaluation of teaching behaviors, attention and engagement levels, giving the district a view it never had.
Teachers get their evenings back, and the district gets a view of the classroom it never had.
The results
Preparation time down 60%, grading automated.
- 60%
- LESS LESSON PREPARATION TIME
- AI-assisted generation of lesson plans, exercises and teaching materials.
- 24/7
- MENTAL HEALTH MONITORING
- Round-the-clock monitoring with automated risk-level assessment and intervention recommendations.
- GRADING AUTOMATED
- Grading runs for subjective and objective questions alike, across every assignment type.
- INDIVIDUALIZED LEARNING PATHWAYS
- Personalized pathways generated from each student’s profile and performance data.
Early detection is the result that matters here, and it is the one nobody can staff for.
The stack
What was deployed
Adaptive homework generation, automated grading for every question type, and an individual learning pathway per student.
WELLBEING
AI mental health support
Round-the-clock emotional support with sentiment analysis, risk-level detection, and automated intervention recommendations.
역량에 관한 노트
본 사이트 전반에 소개된 역량, 자격, 배포 레퍼런스는 Amanah 핵심 엔지니어링 팀의 총체적인 작업을 반영합니다. 일부 프로젝트는 이전 또는 계열 법인 하에 수행되었습니다. 소버린 배포는 규정상 기밀이므로 개별 고객명은 공개되지 않습니다. 기반이 되는 레퍼런스는 NDA 하에 검증 가능합니다.
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