Artificial Intelligence in Education: 9 High-Impact Use Cases

Education is facing an efficiency crisis. Despite steadily increasing budgets, educational systems struggle to personalize learning, produce high-quality educational content at scale, and fairly assess millions of learners with diverse profiles.

Generative artificial intelligence now offers concrete answers to these structural challenges—not technological promises, but deployed solutions already transforming the learning experience of millions of users around the world.

This study analyzes nine deployments that illustrate how AI is revolutionizing three pillars of modern education. Documented results show dramatic performance gains: academic improvement from 15% to 33%, a 60% to 90% reduction in educational content production costs, and engagement rates reaching 98%. More importantly, these technologies are beginning to solve the impossible equation of large-scale personalization: offering tailored learning experiences to cohorts of several million students.

From China to North Dakota, from American universities to Asian ministries, a new generation of tools is radically transforming the traditional economic models of education.

1. Personalization and Intelligent Adaptive Learning

Squirrel AI Learning: 1 million students, 2,000 centers – The Chinese recipe for AI in education
Squirrel AI Learning solved a challenge education has faced for centuries: how to tailor instruction to each student's needs when classrooms are filled with learners of diverse profiles.

Their approach relies on ultra-granular knowledge mapping. The platform breaks each subject into thousands of interconnected “knowledge points,” creating a detailed map of prerequisites and dependencies. When a student struggles, the AI precisely identifies missing foundational concepts and automatically generates a personalized learning sequence: targeted videos, progressive exercises, contextual examples.

This granularity drives extreme efficiency. The infrastructure now spans 2,000 centers in 200 Chinese cities, with over one million student users. The technology optimizes learning time by eliminating redundancy in standardized curricula, accelerating progression while reducing operational costs.

GovTech Singapore: How a nation digitized 100% of its public schools
Singapore’s government, through its tech agency GovTech, chose full transformation over gradual experimentation. Since June 2023, all public schools in the country have used the Student Learning Space platform, equipped with three generative AI modules.

The Adaptive Learning System automatically personalizes paths based on each student’s performance. The Authoring Copilot enables teachers to generate lessons in just a few clicks. ShortAnsFA provides instant, personalized feedback for every student answer—even in subjective subjects.

This strategy creates powerful network effects. More than 4,000 teachers actively use the Appraiser tool, collectively producing over 40,000 automated pedagogical feedbacks. Collective adoption eliminates typical resistance to change via institutional momentum, creating a virtuous cycle of continuous improvement on a national scale.

North Dakota: +17 points on state tests thanks to reading AI
The State of North Dakota fully funded the deployment of Amira Learning across all school districts. This specialized AI acts as a digital speech therapist: it listens to children read aloud, detects real-time difficulties, and delivers personalized micro-interventions.

It analyzes pronunciation, rhythm, hesitations, and fluency. Upon detecting a struggle, it instantly suggests targeted exercises: syllabic breakdowns for complex words, guided repetition to improve fluency, contextual explanations to enrich vocabulary.

State standardized test results quantify the public ROI. A 15-point improvement in Grade 3, 17 in Grade 4, and 10 in Grade 5. Regular users (20–30 minutes per week) progressed twice as fast as the control group. This early intervention on a foundational skill yields long-term benefits for the entire education system.

2. Automated Content Creation and Large-Scale Training

University of Michigan: 34,000 users adopt the first university-owned AI platform
The University of Michigan made a strategic decision: to develop its own generative AI platform instead of relying on commercial solutions. U-M GPT integrates GPT-4o, DALL-E 3, and Llama 3.2 in a secure, customizable environment for the entire university community.

It provides three core functionalities: a conversational interface for academic support, tools for discipline-specific chatbot creation, and a development environment for advanced projects. Students build their own learning assistants. Professors automate course material generation. Staff optimize administrative processes.

Institutional adoption exceeded expectations. 34,000 unique users log in regularly, with 14,000 to 16,000 daily sessions. The community created 1,692 custom chatbots, validating the investment in proprietary development. This confirms the strategic edge of controlling one’s AI stack—especially in a sector where pedagogical differentiation is critical.

Turing: 300 employees trained in 1 week, revenue tripled in a year
Turing transformed technical onboarding into a competitive advantage with A.L.A.N. (Always Learning, Always Nimble), their proprietary LLM that revolutionizes training content creation.

The process fully automates educational production. Experts record 30–60-minute sessions. A.L.A.N. analyzes the material, identifies key concepts, structures pedagogical progression, and generates complete e-learning modules: interactive quizzes, hands-on exercises, level-adapted assessments.

The operational efficiency is unprecedented: 300 employees can be trained simultaneously in just one week. The impact is direct and measurable—2024 revenue tripled, reaching $300 million. Faster onboarding accelerates project delivery, creating a virtuous cycle of growth.

Safran University: 60% time savings across 136,000 training hours
Safran University handles technical training for 90,000 employees in 27 countries, in a sector where errors can be fatal. The integration of generative AI via 360Learning turns this operational constraint into a performance driver.

AI automates the creation of regulatory-compliant technical content. It generates job-specific quizzes, interactive simulations for critical procedures, and conversational agents tailored to each technical domain.

Operational gains exceed all expectations: a 60% reduction in content creation time and a 50% acceleration in upskilling. All 136,000 annual hours of online training now benefit from this optimization, demonstrating the viability of AI in complex industrial settings.

3. Intelligent Assessment and Automated Feedback

British University Vietnam: Zero academic violations with ethical AI
British University Vietnam designed a systemic response to a universal challenge: integrating AI into assessment without compromising academic integrity.

The AI Assessment Scale (AIAS) structures this through a five-level framework. Level 1: total ban for traditional exams. Level 2: limited support for research. Level 3: guided collaboration for creative projects. Level 4: co-creation with mandatory tool citations. Level 5: evaluating students’ mastery of AI tools themselves.

This methodological approach yields stunning results. All AI-related academic violations dropped from over 100 cases to zero. Meanwhile, performance improved: a 33.3% increase in module pass rates and a 5.9% average grade increase among 2,500 students. The framework reconciles tech innovation with educational fairness.

Ebury: From 3 weeks to 3 hours to create compliance training
Ebury, a fintech with 1,700 employees in 25 jurisdictions, faced a major challenge: training teams on complex, ever-changing regulations—with just four people in L&D.

Sana Labs solves this impossible equation. AI auto-generates training tailored to each market’s regulatory requirements: AML modules in the UK, cryptoasset courses in Europe, automatic updates as rules evolve.

Efficiency is precisely measurable: 98% completion rate versus 90% on the old platform. More impressively: creation time cut from 3 weeks to 3 hours—a 2,800% productivity gain. The 4-person team can now manage compliance training for 1,700 employees across 25 countries, turning regulatory constraint into competitive edge.

JISC: 12 UK universities test AI, -30% student support tickets
JISC coordinates one of Europe’s most ambitious AI experiments: 12 UK institutions (8 colleges, 4 universities) simultaneously testing various educational AI tools.

The approach compares technologies: chatbots for student support, automated feedback systems for assignments, AI-enhanced VR for immersive classes. Each is measured with standardized metrics: response times, resolution rates, user satisfaction.

Preliminary results confirm transformative potential: 30% fewer student support tickets and significantly faster academic question response times. The coordinated program reduces experimentation costs while generating reliable comparative data for future tech investments.

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These nine deployments reveal a structural transformation underway. Educational AI has reached industrial maturity, moving from pilot tests to large-scale rollouts backed by rigorous performance metrics.

Three trends are reshaping the sector: large-scale personalization is now economically viable, with academic gains up to 33% and user bases in the millions. Automated educational content creation is redefining production economics, slashing costs by 60–90% while improving quality. Smart assessment systems drive engagement rates close to perfection.

Pioneering institutions are developing their own tech ecosystems, securing long-term advantages in a sector where pedagogical excellence is a core differentiator. This strategy of technological differentiation rewrites the competitive rules.

The future is already emerging in R&D labs: interoperability between systems, predictive learning analytics, immersive modalities integration. For sector leaders, these examples prove that a methodical approach—centered on objective performance metrics—maximizes technological ROI while preserving academic excellence.