Recently, Website Planet published an interview with Geniusee CTO Nazariy Hazdun. In the conversation, we shared our journey in EdTech, discussed the biggest challenges facing today’s Learning Management Systems (LMS), and explained how AI can bring real, measurable value to both educators and learners.

Here are the main highlights from the interview 👇

Three waves of Geniusee’s edtech journey

We described the company’s development in three distinct phases.

  • 2017–2019: Early foundations in language learning platforms and tutoring solutions, building expertise in adaptive testing and teacher workflows.

  • 2020–2021: The COVID rush — rapid LMS deployments under tight deadlines, with AI moving from buzzword to utility (auto-grading, onboarding thousands of students, ticket prioritization).

  • 2022–Today: Productized intelligence — reusable AI modules for adaptive sequencing, feedback engines, RAG search, and integrity checks, seamlessly integrated into Canvas, Moodle, Blackboard, and custom stacks.

Where LMS still fall short — and how AI helps

We emphasized the most common gaps in today’s LMS platforms and how AI fills them.

  • Static flows → Adaptive sequencing: AI tailors learning paths to each student, not the “average” learner.

  • Shallow dashboards → Early risk detection: Predictive models flag struggling students 1–2 weeks earlier.

  • Manual updates → AI-driven freshness: Content is refreshed in hours, not semesters.

  • Generic reports → Smart summaries: NLP condenses long discussions into actionable insights.

AI features that deliver real value

Not every AI feature matters, but some consistently make a measurable difference.

  • Instant rubric-aligned feedback that saves instructors hours.

  • Adaptive learning paths to keep students engaged and progressing.

  • Early-warning dropout radar based on engagement signals.

  • Content refresher tools to keep learning material relevant.

  • Authoring copilots that cut lesson prep time by up to 70%.

How we approach LMS transformation

When institutions seek to modernize their LMS, we begin with a focused 10-day audit.

  • Pulling data from the last 12–18 months.

  • Stakeholder interviews across roles.

  • UX heatmaps and tech scans.

  • Compliance and integrity checks.
    The outcome is a set of quick wins, a mid-term roadmap, and a clear business case.

Guardrails, cost-efficiency & trust in AI

Building trustworthy AI requires careful design and smart architecture.

  • Grounded generation with in-line citations.

  • Strict guardrails (no guessing, no grading without rubric evidence).

  • Tiered model architecture for speed and cost savings (lightweight browser models for hints, cloud models for grading).

  • Aggressive caching and batching to cut latency and expenses.

What’s next for AI in education

Looking ahead, we believe several trends will reshape the LMS user experience.

  • Multimodal models (text, image, audio) to create more interactive, observant courses.

  • On-device inference with WebGPU for real-time hints and cost reduction.

  • Interoperable credentials that allow skills and competencies to travel with learners across schools and employers.

Quick wins for edtech startups

For startups just starting their AI journey, we recommend small, fast pilots.

  • Automated rubric-based feedback on student work.

  • FAQ chatbots trained on course material.

  • AI-generated practice quizzes.

  • Early-warning dashboards for student engagement.

Looking ahead

Finally, we shared our excitement about the future of AI in education. We see huge potential in multimodal, mixed-reality tutors, browser-based pocket coaches, and AI-maintained learning passports. At Geniusee, continuous R&D with partner universities, early adoption of secure AI platforms, and compliance-by-design keep us ahead of the curve.

The full interview is available on Website Planet, but these highlights give a clear picture: AI in EdTech is no longer about buzzwords — it’s about creating systems that truly help both teachers and learners succeed.