Synthetic Data for Responsible, Classroom-Ready AI
AI in education works only as well as the data behind it. Magic EdTech brings together educators, linguists, and AI specialists to create training and evaluation datasets that reflect real classrooms. From synthetic Q&A pairs to multimodal reasoning scenarios, we deliver the data that makes your AI effective, inclusive, and instructionally sound.
Who we work with
What we offer
Accurate AI begins with datasets grounded in real subject knowledge. Content is tagged, broken down, and structured by specialists in math, science, language arts, and other disciplines, so your models respond with academically correct and contextually appropriate answers.
Synthetic Q&A creation, prompt-response ranking, and bias-free consensus labeling are designed specifically for learning contexts. This ensures AI systems not only generate correct answers but also communicate them at the right grade level, with clarity and instructional value.
From step-by-step problem-solving sequences to rare edge cases, datasets include reasoning chains, complex learner-agent dialogues, and adaptive workflows. These help AI tutors handle multi-step academic tasks, ambiguous queries, and unexpected learning challenges.
Clean, labeled audio from classrooms, voice commands, and sentiment tags allow AI to understand spoken input, detect engagement or confusion, and deliver timely, personalized responses in voice-first learning tools.
Relevance scoring, curriculum tagging, and multilingual query evaluation make it easier for students and educators to find exactly what they need, whether they’re asking a question, typing a concept, or searching via images.
Cultural and curricular adaptation ensures that your AI is understood, trusted, and effective across languages, regions, and education systems, without losing academic rigor.
- Service Offering in Numbers
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300+
Subject-specialist annotators
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50M+
Educational data points labeled
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40+
Languages and dialects covered
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99%
Accuracy in quality-verified datasets
Why Magic EdTech
Our teams are made up of educators, linguists, and subject-matter experts who work alongside AI specialists. The result is training and evaluation datasets that reflect how real learners think, how teachers teach, and what curricula demand.
Large datasets often mean quality loss, but not here. Every annotation pipeline is designed to maintain 99%+ verified accuracy, whether the requirement is 500 labels or 5 million, so you can expand confidently without risking model drift.
Whether your product listens, speaks, shows, or reasons, we handle text, audio, video, and images in one integrated workflow. This gives you a single partner for building multimodal AI without stitching together multiple vendors.
From multilingual voice commands to culturally adaptive classroom scenarios, our datasets are built to work in every geography you target, reducing bias and increasing adoption across diverse learner groups.
Leading K–12, higher education, and corporate learning platforms trust us for AI tutor accuracy boosts, search relevance upgrades, and multimodal training sets. We’ve been behind the scenes of products that now serve millions of learners.
FERPA, COPPA, WCAG, and responsible AI practices are baked into how we collect, label, and review data, so your launch meets both technical and regulatory benchmarks from day one.
Case Studies
Accessible Multilingual Educational Simulations
- 50+ Custom Complex Simulations Created
- 7 Months Timeframe
University’s Gender-Specific Program WCAG 2.1 Compliant
- Successful Audit and Remediation for Compliance
- Tested with People with Disabilities (PWD)
WCAG Compliant Digital Learning Elements
- WCAG 2.0-compliant Complex SCOs Audited
- Design and Implementation Consultation & Remediation
Texas State Submissions Accessibility Compliance
- 24x7 Remediation Support
- 30K HTML resources, PDFs & ePubs Audited
Frequently Asked Questions
We work across text, audio, video, and images, covering core subjects, specialized domains, and multimodal learning interactions.
Yes—our team generates synthetic Q&A pairs, edge-case scenarios, and reasoning chains to train and fine-tune AI models.
Absolutely. We run human-in-the-loop evaluations, ranking outputs by accuracy, clarity, and instructional value.
We use consensus labeling across diverse demographics to detect and eliminate bias in AI training data.
Yes—we support over 40 languages and adapt datasets for cultural and curricular relevance.
We handle projects ranging from small pilots to millions of annotated data points, with quality maintained at every scale.
