Context

In wrapping up our study, we've grasped the importance of crafting innovative solutions to amplify skill-building features. Enhancing skills is crucial for boosting user retention on the DeepHow platform.

Problem Statement (Supported by Evidence)

Our platform acts as a conduit for skilled factory workers to hone their abilities by crafting and sharing insightful content. We envisage the incorporation of a skill assessment functionality within our platform to bridge the existing void of a validation mechanism.

Research:

Our investigations revealed a window of opportunity to devise a flexible assessment framework tailored for specific verticals, given that many counterparts are tethered to conventional assessment paradigms. The conventional assessment approach often falls short or appears overly simplistic, whereas AI possesses the prowess to streamline content creation and facilitate evaluation design by non-experts. A conspicuous market gap exists as current players overlook practical knowledge assessment, opening avenues to render evaluation engaging and transcending the mundane checklist task.

Performance Metrics:

Quiz Completion Rate: Tracking the fraction of users who navigate through the quiz post-engagement with a video or skill course.
Quiz Pass Rate: Gauging the fraction of users who clinch a passing grade in the quizzes.
Knowledge Retention: Appraising the uptick in knowledge retention by juxtaposing quiz scores over a period or via pre- and post-assessment scrutiny.
Risks:

Identify pertinent risks impacting users, usability, technology, business, safety, and cross-vertical/tribe/squads dynamics. Employ N/A for null areas.

Value Risk: A potential scenario where users exhibit tepid engagement or tender negative feedback. We'll orchestrate user testing and garner feedback as a bulwark against this risk, refining the feature accordingly.
Usability Risk: The quiz interface or navigation could perplex users, spawning frustration or disengagement. Our commitment is to a user-centric design, achieved through iterative testing and refinements.
Feasibility Risk: Technical hurdles may loom in the course of developing and deploying the AI-facilitated quiz feature. A close-knit collaboration with the development ensemble will be instrumental in troubleshooting and pinpointing solutions.
Business Risk: Certain clients might balk at the new feature adoption due to cost, time, or resource reservations. A comprehensive communication of the benefits coupled with flexible pricing and support provisions will be our strategy to entice adoption