Education · AI tutoring systems
Banao deploys AI tutoring systems that hold a diagnostic dialogue with each student — asking the next question based on the last wrong answer, surfacing knowledge gaps before they compound, and feeding teachers a structured per-session summary instead of a raw transcript.
The tutor lives inside your existing LMS. Students interact in tools they already use. Teachers get the signal that matters — which students are stuck and on what — rather than another dashboard to open.
Studylab AI— AI tutoring and adaptive practice shipped into the product, live with real students.
We build for the specific pedagogical problem — not a generic Q&A chatbot. Each item below closes a gap that classroom-scale delivery cannot close on its own.
The AI asks the next question, not the next page. When a student answers incorrectly, it probes the underlying misconception rather than restating the correct answer — following a structured questioning sequence aligned to your curriculum.
Difficulty adjusts per student per topic, based on accuracy, response time, and session history. A student who has mastered fractions sees fraction practice set aside; one who hasn't is not pushed to the next chapter.
The model identifies where a student's understanding diverges from the correct model before that gap compounds. Teachers receive a flagged list at the start of each class period, not after the exam.
Each tutoring session produces a structured summary: topics attempted, errors made, questions that blocked the student, and a suggested next-session focus. Teachers read one screen, not a full session log.
The tutor lives where students already are — Canvas, Moodle, Google Classroom, or a custom LMS. Assessment results write back to your student information system without a separate export step.
The same tutoring logic runs across your institution's language mix. We handle code-switching and regional terminology so a student working in a second language is not disadvantaged by an English-only interface.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Studylab AI
Studylab needed AI tutoring and adaptive practice built directly into its product — not a third-party widget bolted on. Banao designed the dialogue engine, knowledge-gap model, and adaptive sequencing logic and shipped them into the existing platform.
Banao is a ~300-person engineering company that deploys the same category of systems it builds for clients. InterviewGod — our own AI hiring tool — screens every engineering candidate who joins the company. Vikaas runs our own demand-generation pipeline.
A tutoring or feedback model that has to survive the scrutiny of an engineering team that builds AI is already tested harder than most edtech deployments. That is the standard we bring to your institution.
We will tell you this before you commission a build:
We audit your existing content, LMS data, and assessment structure before writing a line of tutoring logic.
We review a sample of your content and assessment data, map the knowledge-graph structure for your target subject, and build a working prototype dialogue on your hardest topic. You get a feasibility report and effort estimate — yours to keep, credited against the build if you proceed.
Design the dialogue engine, knowledge-gap model, and adaptive sequencing, then integrate with your LMS and SIS. Content tagging, edge-case QA, and the teacher-facing dashboard are part of the deliverable.
Live deployment with session logging, teacher adoption support, and a model-improvement cycle fed by real student interactions. We track session completion and gap-closure rates, not just uptime.
What subjects does AI tutoring handle well?
Subjects with structured correct answers — mathematics, sciences, language grammar, coding, and standardized-test preparation — are the strongest fit. Subjects that depend on extended argumentation or creative judgment are weaker fits; we scope to the parts of a curriculum that benefit most.
How does the tutoring system connect to our LMS?
Banao integrates via your LMS's API or LTI standard — Canvas, Moodle, Google Classroom, or a custom platform. Students access the tutor inside their normal course shell. Assessment results write back to the grade book without a separate export.
How do teachers stay in control of what the AI teaches?
The curriculum and grading rules are yours. Teachers review and approve the knowledge graph that drives the tutor before it goes live. They can flag any AI-generated feedback for correction, and those corrections feed back into the model.
What is the difference between this and a chatbot on the course materials?
A retrieval chatbot surfaces text from the materials when asked. An AI tutor tracks where a specific student is in their understanding, asks questions designed to surface the next misconception, and adjusts the sequence based on that student's session history. The dialogue is diagnostic, not search.
How long from Discovery Sprint to students using it?
The Sprint is two weeks. A focused single-subject deployment typically takes eight to twelve weeks from Sprint sign-off to live students — depending on content volume, LMS complexity, and the number of language variants required.
In 45 minutes we will tell you whether AI tutoring addresses the problem — and what the diagnostic would cost to run.
Book a 45-min scoping call