Lynk’s AI is designed to work with your coaching data, not in isolation. Instead of producing one-size-fits-all session plans, the Lynk AI reads the context created through your actual coaching activity—batches, sessions, attendance, and notes—to generate plans that reflect how your learners are really progressing.

This ensures that every generated session plan feels grounded, continuous, and appropriate for the batch you are coaching.

The AI starts by understanding the batch setup. This includes age group, skill level, session duration, batch size, and participant mix. These inputs help the AI shape the intensity, pacing, and complexity of the session plan.

Previous sessions give the AI insight into what has already been taught, the typical structure of your classes, and how progression has been unfolding over time. This prevents repetition and supports logical skill building.

Attendance data helps the AI assess consistency. If students have missed sessions, the AI avoids assuming skill mastery and may recommend reinforcement or revision instead of advanced drills.

Qualitative coach notes are a critical input. The AI extracts themes such as strengths, difficulties, fatigue, confidence issues, or constraints, and uses them to adjust focus areas in the next session plan.

Details you explicitly provide while generating the plan—such as objectives, skill focus, age range, or special conditions—directly guide the AI’s output. These inputs help fine-tune the plan and override defaults when necessary.

Rather than relying on a single signal, Lynk AI combines structured data (batch setup, attendance) with qualitative insights (coach notes and prompts). This layered approach allows the AI to understand both what should happen next and what realistically can happen in the next session.

Lynk AI generates session plans using the real context of your coaching workflow—batch structure, past sessions, attendance, coach notes, and your direct inputs. This ensures that plans are relevant, adaptive, and aligned with actual learner progress.

The result is a session plan that feels continuous, practical, and tailored to your batch—while keeping you fully in control of how the session is run.