The Outline Topics phase transforms the learning strategy developed during the Design phase into a structured, hierarchical content map. This step serves as the architectural foundation for the learning materials to be created in subsequent phases, ensuring that each instructional element aligns with the project’s aims, objectives, and organizational outcomes.

Using the Analysis Document and Design Document as primary inputs, Learning and Development (L&D) professionals complete the Outline Form in NocoDB. This form captures key data elements including the overall training aim, up to three measurable training objectives, and detailed information on modules, topics, examples, and assessment considerations. The form’s structure enforces alignment between strategic intent and content organization, while maintaining flexibility to accommodate project-specific needs.

To support the creation of effective training aims and objectives, the workflow integrates an AI Chat Assistant accessible directly within Notion. The assistant is trained on the proprietary Weighted Objective Accountability Framework, which combines Mager’s ABCD model with a weighted scoring methodology that links objectives to measurable accountability activities. By referencing the Project, Analysis, and Design document IDs, the assistant can contextualize its recommendations and guide users through drafting and refining precise training aims and objectives. This ensures that the instructional intent is both measurable and strategically grounded.

Once aims and objectives are finalized, users submit the Outline Form, triggering n8n to orchestrate the automation workflow. The AI model synthesizes data from the input documents and form responses to generate a comprehensive Topics Outline Document in Notion. This document presents the full learning content map—organizing objectives, modules, and topics in a hierarchical structure that aligns with instructional best practices.

Within Notion, users can review and refine the generated outline, apply updates, or version up to new iterations directly through NocoDB. Each submission is logged and traceable, preserving transparency throughout the workflow. Once validated, an automated email draft is generated to facilitate stakeholder review and approval.

By the conclusion of this phase, teams possess an approved Topics Outline Document—a cohesive framework that clearly defines what learners will engage with and how those elements map to the overarching training goals. This deliverable bridges strategy and implementation, laying the groundwork for the subsequent Lesson Plan Phase, where each topic evolves into detailed learning experiences, activities, and assessments.

The Lesson Plan phase represents the point at which strategic planning, analysis, and design converge into an actionable instructional roadmap. This phase transforms the conceptual learning framework into a detailed, sequenced plan that defines modules, lesson activities, delivery methods, and timing—ensuring that the learning experience is engaging, efficient, and measurable.

The Topics Outline and Design Documents provide the foundation for this phase, outlining the content structure, instructional strategies, and delivery modalities that shape each lesson. Learning and Development (L&D) professionals begin by completing a Lesson Plan Form in NocoDB, which captures essential inputs and offers optional fields for additional AI guidance. These fields enable designers to define project-specific nuances and refine outputs through human-in-the-loop iteration, ensuring accuracy, alignment, and instructional quality.

Once the form is submitted, n8n orchestrates the automation workflow, prompting the AI to synthesize the topics from the outline with the instructional methods and modalities from the design phase. Drawing on the organization’s centralized training terminology database, the AI constructs a structured Lesson Plan in Notion. The process occurs in multiple sub-steps, each introducing an opportunity for human oversight and refinement.

In the first sub-step, the AI generates modules and sub-modules, each with descriptive objectives and logical hierarchy. L&D professionals review this structure to validate accuracy, relevance, and instructional flow. This review stage is critical: the quality of module organization directly influences the effectiveness of subsequent content generation. After approval, the workflow advances to the next sub-step, where the AI builds learning activities and accountability activities for each module and sub-module.

Each activity is automatically assigned a learning modality, descriptive objective, and estimated completion time. Learning activities focus on content exploration and skill development, while accountability activities connect directly to the training objectives established earlier in the workflow, serving as measurable performance checkpoints. Activity timing, sequencing, and modality selection are informed by parameters drawn from the design document—such as delivery method, content standards, and session duration—ensuring consistency across instructional design layers.

All outputs are generated and editable within Notion, where users can reorganize modules, adjust activity durations, refine descriptions, or delete and add new activities as necessary.

By the end of this phase, the project team possesses a fully developed Lesson Plan Document—a granular blueprint detailing every module, sub-module, and activity, along with delivery sequencing, timing, and evaluation points. This deliverable serves as the operational backbone for content production and project scheduling, directly linking instructional intent to execution. With the lesson plan approved, the workflow advances to the final phase: Project Scheduling, where training tasks are formalized and transferred into the organization’s project management application called Plane for development and delivery.

The Scheduling Phase operationalizes the instructional design process by transforming the Lesson Plan Document into a structured, executable project schedule. This phase bridges the gap between instructional intent and tangible production, converting learning activities into actionable development tasks, complete with assignments, time estimates, and project tracking.

The workflow is initiated from the Lesson Plan Record created in NocoDB which triggers the n8n AI-assisted automation that sources a database of organizationally defined content-creation task codes. This database, maintained in NocoDB, forms the foundation for task generation. Each code includes a task name, definition, modality classification (when applicable), and a default bid time—the estimated number of hours.

During automation, the AI assigns one or more relevant task codes to each activity defined in the Lesson Plan. These codes are then expanded into fully formed task descriptions, which include the task name, purpose, and estimated development time. The resulting Task List is automatically output to Notion, nested within the project’s existing module and activity structure.

Within Notion, instructional designers and project leads can evaluate each generated task for accuracy, appropriateness, and workload balance. Adjustments may include adding supplementary subtasks (e.g., creating slide decks, handouts, or video scripts), modifying bid times, or refining task descriptions. Once reviewed, tasks undergo an internal approval process, ensuring that the final development plan aligns with organizational standards and production capacity.

After approval, users return to the corresponding Lesson Plan Record in NocoDB and activate the “Send Tasks to Plane” trigger. This action initiates an automated data transfer to Plane, the organization’s project management platform. The AI automation mirrors the entire instructional structure—modules, sub-modules, activities, and tasks—within Plane, preserving hierarchy, naming conventions, and descriptors. Visual elements such as emojis and background images are also carried over to maintain project continuity.

Inside Plane, teams gain access to a fully configured project workspace. Each module and activity is represented as a structured work item, complete with task descriptions, time estimates, and traceable identifiers. Project managers can assign ownership, set start and end dates, define urgency levels, and monitor status updates in real time. Plane’s multiple visualization modes—List, Board, Calendar, Table, and Timeline Views—enable flexible scheduling and progress tracking, while Cycle Management allows for sprint-based execution and iterative delivery.

Each cycle within Plane features its own analytics dashboard, providing immediate insight into progress metrics, burn-down rates, workload distribution, and completion percentages. Custom views and filters further empower teams to monitor specific subsets of work—such as activities by modality, assignee, or priority—enhancing both visibility and accountability across the L&D production process.

By the conclusion of this phase, the organization possesses a fully structured Plane Project that mirrors the instructional framework defined in Notion. All learning activities are now decomposed into actionable tasks, scheduled, and assignable within a centralized system. The AI-driven automation has transformed high-level instructional design artifacts into a dynamic, trackable production environment—completing the human-in-the-loop AI workflow for scalable, transparent, and efficient learning content development.