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Learning Paths
A Learning Path is a training programme made up of sessions that users complete in order. In a Fellow Session, the AI Fellow guides a live conversation and checks whether the participant has met the learning objectives. Once the required step is complete, the next one becomes available.
When to use
Use a Learning Path when topics need to be covered in order and you want to track progress across a group. Common examples include new-hire onboarding, AI adoption, regulatory training, manager development, and customer training.
For a single coaching tool, simulation, or public demo, a standalone AI Fellow is generally sufficient.
Setup process
Start with Create a Learning Path. The New Learning Path window offers Start from scratch, Build with AI, or Import. Import accepts a SCORM package or a Frontierz Learning Path file. After creation, use Edit a Learning Path to modify sessions, objectives, general info, and Connectors.
The core configuration is performed in Sessions and objectives, where an existing AI Simulation can also be placed between sessions as a role-play step. An existing SCORM package can be imported as a complete Learning Path or added as a session in the editor. A Frontierz file can recreate an authored path, including Fellow Sessions and AI Simulations. From there, use Manage Users to enroll users, import CSVs, export data, clear progress, or unenroll one or several people. Connectors can also be enabled to add capabilities such as screen sharing or Schedule Next Session, and Learning Path knowledge files give the AI Fellow the source documents behind the program. Learning Path communications on Settings turns follow-up emails and the Monday digest on or off.
Functional overview
Sessions build on one another, so users complete the foundational topics before moving to more advanced material.
- A user cannot skip foundational sessions and jump to a more advanced one.
- Each session can assume the competencies built in the previous ones.
- Progress data tracks where users are, not what they have clicked through.
If users need to consult reference material in any order, consider a single AI Fellow with a broad Knowledge Base.
Each session holds a set of objectives, defined as the observable actions a user must demonstrate during the conversation. The AI Fellow checks them off as the conversation progresses. There are no multiple-choice quizzes in this process. Assessment happens through the conversation itself.
In addition to objective verification, a session can collect structured data during the conversation, such as department, role, current tools, goals, and key challenges. The AI Fellow asks, the user answers naturally, and the data is recorded in the analytics dashboard alongside progress data.
Completion certificate
When a user completes every step in a Learning Path, Frontierz issues them a completion certificate. A Certificate card appears at the end of their Journey. The certificate is headed Certificate of completion, with the user's name, the Learning Path name, the company as issuer, Issued on, and a certificate ID. The company logo and colors are used when they are set. The right-hand panel shows up to three logos over a background image. Branding is optional: without any setup, the certificate uses Frontierz branding.
To co-brand the certificate, set the logos and background for the whole workspace in Certificate branding, or give one Learning Path its own logos under Certificate logos.
The owner sees Go Back and Actions (Print, Download as PDF, Export to LinkedIn). Other viewers see the certificate without that bar. Your Progress shows the same certificate as a card with an icon menu: View, Download PDF, and Add to LinkedIn.
Some Learning Paths can also award a Microsoft credential alongside the certificate. When that is set up for a path, the certificate page shows a Microsoft badges section with a link the user can follow to redeem the credential on Microsoft Learn. This is configured by the Founderz team on request; contact your Founderz representative to add a Microsoft badge to a Learning Path.
Limitations and considerations
AI Fellow assessments are intended to support learning, coaching, and competency visibility. They are not suitable as the sole basis for decisions affecting employment, certification, compliance status, or legal liability, unless the Learning Path has been explicitly configured, validated, and approved for such use, with human review in place.
Assessment data should be treated as an indicator that may justify a follow-up review. Decisions with real-world consequences must be reviewed by a human reviewer.
Monitoring and optimization
Use Analytics to review participation, session progress, conversation volume, transcripts, and User Insights. From the same dashboard, generate a Learning Path report summarising progress, completed objectives, and examples of Fellow assessments. Completed reports can be printed or saved as PDF.
Open a user from Manage Users to review their sessions, update incomplete objectives, or reset progress. For instructions on adding or removing one participant, see Enrol a user.