Industries
Education & e-learning
LMS platforms, course marketplaces, assessment engines and AI tutors — for schools, universities and corporate learning.
What are the typical challenges in education and e-learning software?
Learning platforms break on enrolment day, hide content in ten formats with no search across them, and rely on grading and feedback that cannot scale with cohort size. Engagement drops go unnoticed until a student has already left. UNESCO’s Global Education Monitoring Report (2023) warns that technology in education only helps when it is designed around learners and evidence, and the scale is real: Statista (2024) estimates the global online education market in the hundreds of billions of dollars. Add accessibility (WCAG 2.2, W3C, 2023) and minors’ data under GDPR, and the platform is a serious engineering task.
- Platforms that fall over on enrolment day
- Content in ten formats and no search across it
- Grading and feedback that do not scale
- Engagement drops nobody notices in time
What do we build for education?
We build LMS and course platforms with video and live-class integrations, assessment and proctoring systems, student and teacher mobile apps, and integrations with student information systems, payment and identity providers. Content interoperability follows SCORM and xAPI, the standards maintained by ADL (2023), so courses and learning records move between systems instead of being locked in. Platforms are load-tested for the enrolment peak before launch and built with search across all content types from the start.
- LMS and course platforms, video and live-class integrations
- Assessment and proctoring systems
- Student and teacher mobile apps
- Integrations with SIS, payment and identity providers
Where does AI move the numbers in education?
AI moves education numbers in tutoring, grading assistance, content search and retention. The reference point is Bloom’s 1984 finding that one-to-one tutoring produced roughly two standard deviations of improvement over classroom instruction; AI tutors are a practical attempt to approach that at scale. We ground tutors and Q&A in the course material so every answer can be traced to a source, keep a teacher in the loop for grading, and use engagement models to flag dropout risk early. The EU AI Act (2024) classifies AI that determines access to education or evaluates learning outcomes as high-risk, so logging and human oversight are built in.
- AI tutors and Q&A grounded in course material
- Automated grading assistance with human review
- Content tagging, search and recommendations
- Dropout and engagement prediction
Adaptive vs linear learning paths: which do you need?
Linear paths are right for compliance training, short courses and any content that must be consumed in a fixed order; adaptive paths pay off in long programmes with heterogeneous learners and enough interaction data to drive them. Adaptive learning is only as good as the assessment behind it, so we usually start linear, instrument everything, and switch on adaptivity per module once the data supports it.
| Criterion | Linear path | Adaptive path |
|---|---|---|
| Authoring effort | One sequence | Many branches, prerequisites, mastery rules |
| Data needed | None | Per-learner assessment and interaction data |
| Learner experience | Predictable, simple | Personalised pace and remediation |
| Measurability | Completion rates | Mastery, time-to-competence, retention |
| Fit | Compliance, short courses | Long programmes, mixed-ability cohorts |
Why Glanit for education?
Because we build learning platforms to be loaded on day one and searchable on day two, and we add AI tutors only where the course material can back every answer. Over 16 years and 500+ projects we have shipped high-traffic platforms and RAG assistants, and we apply both here. Minors’ data gets GDPR-specific handling (Article 8 consent rules, data minimisation), accessibility is tested against WCAG, and every model decision that affects a learner is logged and reviewable. Start with a conversation.