Use Case: Choosing a Lesson Preparation Approach
Two strategies for preparing lesson content — shared prebuilt courses versus personalized AI-generated lessons — and when to use each.
Who this is for
Teachers and school administrators deciding how to structure their lesson preparation workflow — whether to invest upfront in polished reusable content, or to use AI to generate personalized content at the point of assignment.
What this page answers
- What are the two main approaches to preparing lessons?
- When should I prebuilt shared content versus generate per student?
- What student data does AI use for personalization?
- Can I combine both approaches in one school?
Prerequisites
- A published course with at least one lesson or Lesson Builder configuration
- Familiarity with the Lesson Builder
- For the personalized approach: enrolled students with profile data filled in
The two approaches at a glance
Locuto.PRO supports two distinct strategies for preparing lesson content. You can use one or both depending on your teaching setup.
| Dimension | Shared Course (Prebuilt) | Personalized Course (AI per student) |
|---|---|---|
| Content creation | Created once, fully rendered and reviewed | Structure defined via Lesson Builder; content generated at assignment time |
| What the student receives | Identical copy of the finalized lesson | Unique lesson tailored to their profile |
| AI role | Optional — used during authoring if desired | Required — generates content at assignment time |
| Student profile data used | None | Gender, age, native language, strengths, weaknesses, teacher notes |
| Predictability | 100% — content is tested and locked | Variable — each generation is different |
| Teacher effort per student | Low — assign and go | Medium — generate, review, and adjust |
| Best for | Groups, busy teachers, standardized programs | Individual students, tutoring, adaptive learning |
| Reusability | High — create once, reuse many times | Moderate — teacher can copy-paste interesting materials between lessons |
Approach 1: Shared Course (Prebuilt Content)
How it works
The teacher creates a course, builds every lesson with finalized content (using AI or manually), reviews and tests everything, then publishes. When students are assigned lessons, they all receive identical copies of the same proven material.
Content is tested once and reused many times. Every student gets the same exercises, the same examples, and the same structure. The teacher's effort is front-loaded during course creation — after that, assignment is fast.
This approach relies on the master-and-copies model. When you assign a course lesson, the system creates a copy for the student. The master stays unchanged.
When to use it
- You teach groups where everyone follows the same program
- You want predictable, tested content that works reliably every time
- You have a busy schedule and need to assign lessons quickly without extra steps
- You are building a standardized curriculum that multiple teachers will use
- You want to build a lesson library once and reuse it across semesters
Strengths and limitations
Strengths:
- Content is 100% predictable — tested once, used many times
- Fast to assign — no generation or review step at assignment time
- Works well for groups and standardized programs
- Multiple teachers can share the same course
Limitations:
- No personal approach to an individual student's needs
- All students get the same material regardless of their level nuances
- Updating content requires editing the master, publishing it, and then pushing the change to copies students already hold
Approach 2: Personalized Course (AI-Generated Per Student)
How it works
The teacher creates a high-quality course that contains the structure of lessons — topics, Lesson Builder configuration, ordered Steps, and complexity settings — but the actual content is not rendered. During course preparation the teacher tests the AI generation to verify the configuration produces good results, but the generated content is not kept.
When it is time to assign a lesson to a specific student, the teacher assigns the course lesson, opens the assigned lesson (which carries the Lesson Builder configuration), and generates content with AI. The AI uses the student's profile data to produce a personalized lesson.
The same course lesson (same topic, same Lesson Builder configuration) produces different content for each student based on who they are.
AI content generation uses the Lesson Builder configuration to define structure, and the student profile to personalize the result. See AI Lesson Generation for the full generation workflow.
The role of the student profile
When generating content for a specific student, AI considers the following profile fields:
| Profile field | How AI uses it |
|---|---|
| Gender (if defined) | Adjusts pronoun usage and cultural examples |
| Age | Selects age-appropriate topics, vocabulary, and scenarios |
| Native language | Anticipates interference errors and provides contrastive notes |
| Strengths | Builds on known competencies to maintain engagement |
| Weaknesses | Adds targeted practice for gap areas |
| Teacher notes | Incorporates specific instructions or context from the teacher |
| Additional requirements | Applies any extra constraints the teacher defines for this specific lesson |
For AI to use profile data during generation, the fields must be filled in beforehand. If a student's profile is incomplete, AI will work with whatever information is available — but the more complete the profile, the better the personalization.
When to use it
- You teach individual students and want maximum personalization
- Your students have different native languages, ages, or levels within the same course structure
- You want to adapt exercises to each student's strengths and weaknesses
- You are a tutor aiming for the highest possible learning efficiency per student
- You want to generate content that reflects specific teacher notes for each lesson
Strengths and limitations
Strengths:
- Every student gets content tuned to their profile — maximum relevance
- Allows a truly personal approach even at scale
- The same course structure works for students with very different backgrounds
- Teacher can add per-lesson notes and requirements to guide each generation
Limitations:
- Requires AI generation and teacher review for every student assignment — more time per lesson
- Content is not 100% predictable — each generation is different
- Uses AI credit per student rather than once per course
- Not ideal for group sessions where everyone needs the same content
Reusing generated content
If AI produces a particularly good exercise, text passage, or audio example for one student, you do not have to lose it. Open the student's generated lesson in the Lex Editor, copy the content blocks you want, and paste them into any other lesson — whether it belongs to the same student, a different student, or even a course lesson.
This way, the personalized approach can feed back into your shared content over time: interesting materials discovered during generation become part of your reusable library.
Choosing the right approach
Use this decision tree as a starting point:
Combining both approaches
You do not have to pick one approach for your entire school. Many schools use both:
- Shared courses for group classes, standardized exams, and homework libraries
- Personalized courses for individual tutoring, adaptive programs, and students with specific needs
A teacher might even start with a shared course for a new student and switch to a personalized course once they know the student well enough to benefit from tailored content.
Related pages
- Lesson Document Lifecycle — how content flows from course lessons to student copies
- AI Lesson Generation — the full AI generation workflow
- Lesson Builder & Lesson Blocks — configuring lesson structure
- Building a Lesson Library — scaling your shared content collection
- Manage Course Lessons — organizing lessons within a course
Q&A
Can I switch a course from shared to personalized (or vice versa)?
Yes. To go from shared to personalized, keep the Lesson Builder configuration in your course lessons but remove or ignore the rendered content — future assignments will use AI to generate fresh content. To go from personalized to shared, generate the content once, review and finalize it, and keep it as the permanent lesson document.
Does the personalized approach use more AI credit?
Yes. In the shared approach, AI generation (if used at all) happens once per course lesson. In the personalized approach, AI generates content for every student assignment. See Billing & AI Credit for details.
What if a student's profile is incomplete?
AI will generate content using whatever information is available. If the native language is missing, for example, AI will skip contrastive notes. For the best personalization, fill in at least the native language, age, and any known strengths or weaknesses before generating.
Can I copy-paste generated content between student lessons?
Yes. Open a student's generated lesson in the Lex Editor, select and copy the content blocks you want (text, audio, images, exercises), and paste them into any other lesson. This is a good way to extract interesting materials from personalized lessons and build up your shared library.
Which approach works better for group sessions?
Shared Course. During a live group session, the teacher's edits push to all participant lessons simultaneously. Personalized content would conflict with this model because each student would have different material. Use shared prebuilt content for groups and reserve the personalized approach for individual sessions.