Bulk Edit Support for Period-Based Training Models
Bulk edit operations in the Training Model Manager now support period-based training models (one-time and sequential) in addition to recurring models. Administrators can add, remove, or replace trainings across multiple training models of any cycle type in a single workflow, with cycle-specific assignment rules and full audit tracking.
What's New
- Bulk add, remove, and replace trainings across one-time, sequential, and recurring training models
- Categorized review of selected training models by cycle type with see-all visibility
- Cycle-specific assignment rules including due dates, validity periods, and sequence selection
- Push trainings to qualified users with configurable extension periods for one-time models
- Sequence-level targeting (initial or renewal) for sequential training models
- Full audit trail of bulk edit actions via Modify History and detailed status reports
Details
Description
This release extends bulk edit operations in the Training Model Manager to cover period-based training models, specifically one-time and sequential cycle types. Previously, only recurring training models supported bulk add, remove, and replace operations, forcing administrators to manage period-based models individually.
Administrators can now run a single bulk edit workflow that spans all three cycle types simultaneously. The interface categorizes selected training models by cycle type during review, lets admins define cycle-specific assignment rules in one pass, and records every change in the Modify History for full auditability.
Business Need
Managing period-based training models at scale was previously a manual, model-by-model exercise. Administrators handling compliance programs with mixed cycle types had no efficient way to propagate training changes across one-time and sequential models, leading to inconsistent updates, higher misconfiguration risk, and significant administrative overhead.
By extending bulk operations to all cycle types, Cornerstone reduces the effort required for large-scale training updates, lowers the risk of partial or inconsistent rollouts, and delivers a more continuous training experience for end users. Changes propagate consistently across the affected models, minimizing disruption for learners already in progress.
Key Features
- Administrators can perform bulk add, remove, and replace operations across one-time, sequential, and recurring training models in a single workflow.
- The review step categorizes selected training models by cycle type, with a see-all option that lists every training model included in each category.
- Cycle-specific assignment rules can be defined per type, including custom or default due dates, validity periods, and recurrence settings.
- For one-time training models, administrators can push trainings to qualified users in addition to in-progress users, with a configurable extension period for qualification.
- For sequential training models, administrators can target specific sequences such as initial or renewal when adding trainings.
- All bulk edit actions are captured in the Modify History and detailed status reports, including per-model success or error outcomes.
Deployment & Considerations
The feature is available in both UAT and Production with this release. Period-based training models remain an Early Adopter feature and must be explicitly enabled, along with the base Training Model feature, before bulk edit support becomes visible for one-time and sequential cycle types.
No new permissions are introduced; existing bulk edit permissions apply. Key guardrails include: training models containing 100 or more trainings are excluded from bulk edit operations, and only active training models with properly configured cycle types and requirements appear in selection. Qualified user targeting and extension periods are supported only for one-time models, while sequential and recurring models push updates to in-progress learners (recurring also supports qualified users). Administrators should validate custom due dates against model-level guardrails to avoid partial-success outcomes in the bulk edit report.