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✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
✨ New Plugin Alert ✨ SleekRank is now available with €50 launch discount
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AI Chatbot for AI Tutor Use Cases

SleekAI reads your course content, lesson progress, and assessment rubric from WordPress, then explains concepts at the student's level, walks through worked examples, and surfaces the next exercise. Plug in OpenAI, Anthropic, Google, or OpenRouter with your own key.

♾️ Lifetime License available

SleekAI chatbot for AI Tutors

Static course content can't answer the question a student actually has

A pre-recorded lesson and a quiz at the end of the module work fine when the material lands cleanly. The moment a student misunderstands one prerequisite, the rest of the module falls apart. Forum posts get answered in two days, office hours run once a week, and the student fakes their way through the next quiz hoping it doesn't matter. By the time the instructor notices the gap, the student has either dropped the course or built three more misconceptions on top of the first one.

SleekAI grounds replies in your course content (lessons, transcripts, worked examples, rubric notes) and reads the student's progress meta from WordPress, LearnDash, LifterLMS, or any LMS that writes lesson completion to user meta. The bot explains a concept at the student's stated level, walks through a worked example using the same notation the lesson used, and surfaces the next exercise when the student is ready. Display conditions scope the tutor to the course area only, so the same bot doesn't show up on the public marketing pages.

Logs capture the questions students actually ask, segmented by lesson and cohort. The recurring confusions reveal the lesson that needs a rewrite, the worked example that's missing, and the prerequisite nobody links to. That feedback loop is the curriculum research artefact instructors usually only get from end-of-course surveys.

Workflow

How SleekAI runs as a one-on-one tutor

1

Ground in the course

Point SleekAI at lesson transcripts, worked examples, and rubric notes via the wizard. ACF and postmeta fields on lesson posts carry prerequisite links and notation so the bot uses the course's vocabulary.
2

Sync student progress

LearnDash, LifterLMS, or any LMS that writes lesson completion to WordPress user meta feeds the prompt context. The bot reads which lessons are done and which are still ahead on every turn.
3

Write a coaching prompt

Tell the bot to explain at the student's level, use the course notation, decline to answer assigned exercises directly, offer parallel worked examples instead, and escalate graded or out-of-scope questions to the instructor.
4

Mine the gaps

Filter logs weekly by lesson and concept. The recurring confusions become the basis for a clarifying note, a new worked example, or a transcript rewrite that lifts the next cohort's outcomes.

Try it now

Try the tutor

A student asks a concept question and gets a grounded explanation, a worked example, and a pointer to the next exercise.

Comparison

Generic chatbot vs SleekAI for AI Tutors

Generic chatbot

  • Cannot read which lesson the student is on
  • Uses different notation than the course
  • Doesn't know which prerequisites the student completed
  • Cannot point to the right exercise next
  • No log to spot the recurring confusions per cohort

SleekAI chatbot

  • Reads lesson progress from LearnDash or LifterLMS user meta
  • Grounds in lesson transcripts, worked examples, and rubric notes
  • Uses the course's own notation and terminology
  • Bring your own OpenAI, Anthropic, Google, or OpenRouter key
  • Logs surface the concept gaps per lesson and cohort

Features

What SleekAI gives you for AI Tutors

Course-grounded

Every explanation references the lesson it comes from and uses the same notation and terminology the course already established. No reinventing the vocabulary mid-stream, which is what most generic AI tutors do.

Student-aware

The student's progress, completed lessons, and stated level flow into the prompt. The explanation lands at the right depth, references prerequisites by name, and doesn't pretend the student already knows something they haven't covered.

Curriculum logs

Filter logs by lesson and concept. The recurring questions are the lesson that needs a rewrite, the worked example that's missing, and the prerequisite nobody links to. Instructors get research instead of survey responses.

Use cases

Where instructors use SleekAI as a tutor

Cohort-based courses

Live cohort programs where students need help between live sessions. The bot fills the gap with grounded explanations, freeing the instructor to spend office hours on the harder problems that actually need a human.

Self-paced courses

Asynchronous programs where students stall on a single concept and disappear. The bot intervenes at the moment of friction with a worked example, which is the difference between completion and refund.

Skill bootcamps

Coding, design, or trades programs with rubric-graded exercises. The bot uses the rubric to explain what "better" looks like on a draft submission, without doing the work for the student.

The bigger picture

Why grounded tutoring beats a generic AI helper

Most courses lose students at a single concept that didn't land. Forum posts get answered in two days, office hours run once a week, and the student fakes their way through the next quiz hoping it doesn't matter. By the time the instructor notices the gap, the student has either dropped the course or built three more misconceptions on top of the first one.

A grounded tutor closes that gap by intervening at the moment of friction, in the course's own notation, using lessons the student has actually completed as the grounding for the explanation. The operational win is completion. Students who get a grounded explanation at the moment of confusion finish at a higher rate, because the friction that would have ended the course gets resolved in minutes instead of days.

The pedagogical win is depth. A tutor that uses the course's notation reinforces the vocabulary the curriculum spent two lessons establishing, rather than introducing a competing standard from a textbook the student isn't reading. The longer-term win is curriculum research.

The logs become a structured artefact for the instructor: top concept gaps per lesson, recurring vocabulary confusions, prerequisites that didn't get established firmly enough. Each fix lifts the next cohort's outcomes without rerunning the course from scratch. The right metric is not chats handled but lesson completion within the cohort's expected timeline, which is the input the course's reputation and renewal rate actually respond to.

A grounded tutor that holds the line on assigned exercises, refuses to give graded judgement, and escalates to the instructor when warranted is the closest thing to a TA most online courses ever budget for.

Questions

Common questions about SleekAI for AI Tutors

Only if you let it. The system prompt should explicitly limit the bot to explaining concepts, walking through example problems that are not the assigned exercises, and pointing the student to the rubric. Most instructors instruct the bot to refuse to answer the current assignment directly and to offer a worked example on a parallel problem instead. Logs make it easy to audit whether the bot held that line.

 

Yes. LearnDash and LifterLMS write lesson and quiz completion to WordPress user meta natively. SleekAI reads that meta into the prompt context, so the bot knows which lessons the student has finished and which are still ahead. The grounding can also include the most recent quiz score per topic, which lets the bot focus on the concepts the student actually got wrong.

 

If the lesson transcripts and worked examples are in the grounding context, the bot picks up the notation from those sources. The system prompt should also reinforce "use the notation from the course, not a textbook standard you might know", which keeps the bot from quietly switching variable names mid-explanation.

 

Yes. The grounding can include the exercise index per lesson, and the prompt instructs the bot to recommend an exercise that builds on the concept the student just asked about. If the LMS exposes a prerequisite graph, the bot follows it; if not, the prompt encodes the recommended order explicitly.

 

Include the student's self-rated level or a placement-quiz outcome in the user meta, and the prompt adjusts depth accordingly. An advanced student gets a tighter explanation that assumes prerequisites; a beginner gets a worked example and a pointer to the prerequisite lesson. Same course, different entry points.

 

The bot should never give medical, legal, or financial advice as if it were a practitioner. The system prompt scopes it to coursework only and instructs it to escalate to the instructor for anything that smells like advice-seeking. The course's own disclaimers should be referenced in the prompt so the bot can quote them when needed.

 

It can describe what a high-rubric submission looks like and identify gaps in a student-submitted draft, but it should not assign a final grade. The grading judgement stays with the instructor for accountability reasons; the bot's job is to coach the student to a better draft before submission, which is where most rubric-based feedback adds the most value.

 

The logs are filterable by lesson, concept, and date. Most instructors export the weekly log to spot the top three concept gaps per lesson. Those gaps become the basis for a clarifying note, a new worked example, or a rewrite of the lesson transcript. The bot improves the curriculum, not just the individual conversation.

 

Pricing

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