Skill Assessment Types & Impact

Hi ​@rfitzpatrick, Please let me know if this is helpful. 

How Assessment Types Change the AI’s “Understanding” of Skills

In Eightfold TM, skills and proficiency levels are the foundational inputs for matching and recommendation systems (career paths, learning, projects, mentors). Skill Assessments are the structured mechanisms used to write these proficiency signals onto an employee’s profile.

Enabling new assessor types, like managers or peers, goes beyond simply adding new fields to the UI. It fundamentally alters the underlying proficiency signals the platform relies on to calculate skill gaps and rank recommendations.

The Impact of Adding Manager Assessments

Moving from a "Self-Only" model to including Manager Assessments primarily upgrades how the AI handles Learning & Course Recommendations.

The Impact of Adding Peer Assessments

Peer assessments are typically handled via a request-based workflow (initiated by the employee, with a one-time submission window for the peer, and the manager copied for governance).

Adding peer ratings introduces an external proficiency signal that actively updates skill gap calculations, which in turn influences course suggestions. Eightfold factors peer input into skill-gap scoring using the following documented methods:

• Average(Manager + Peer) - Self

• Average(Peer) - Self

• Manager - Self

By enabling this, you are adding variables that change which skills the platform flags as a "high gap," directly altering which courses the AI favors for that employee.

Influence Beyond Learning (Jobs, Projects, Mentors, Career Paths)

While Eightfold's documentation is most explicit about how manager precedence affects Learning, the entire product suite is heavily skills-driven. Enabling manager and peer assessments indirectly influences all recommendations by upgrading the quality and objectivity of the structured data on the profile:

• Career Navigator: Personalizes paths using role skills, employee skills, experience, and goals.

• Project Recommendations: Weighted 100% on skill alignment via a deep analysis of the employee profile.

• Mentor Recommendations: Prioritizes skills (interests, gaps, and experiential skills) as top-tier signals.

• Learning Recommendations: Explicitly uses proficiency derived from manager ratings, self-ratings, or a seniority fallback.

(Note: While manager ratings definitively override self-ratings for learning, the broader platform simply consumes this upgraded profile data to refine its overarching matching algorithms).

The Bottom Line for Your Setup

• If you enable Manager Assessments: The AI will upgrade its learning recommendations to prioritize manager ratings over self-ratings. Skill gaps used to recommend courses will now be calculated using the manager's view against the role benchmark.

• If you enable Peer Assessments: You introduce a new metric that further shifts skill-gap calculations for course suggestions based on aggregate peer, manager, and self comparisons.