The AI-Enabled Grading System

How it works

Level’s AI-enabled grading system employs advanced natural language processing (NLP) algorithms based on OpenAI to analyze open text responses to specific questions related to e.g. business models. Here’s a step-by-step breakdown of the process:

1. Question Design: Each question is crafted to elicit detailed information about key aspects of e.g. a business model, such as value proposition, market strategy, revenue streams, and operational plans.



   
   

2. Anchor Definition: For each question, a set of anchor points is established. These anchors define the criteria for each score on the numerical scale (e.g., 1 to 5). Anchors provide clear expectations for what constitutes a high, medium, or low score, ensuring consistency and transparency.



   
   

3. Response Analysis: The AI analyzes the entrepreneur's response, comparing it against the predefined anchors. It assesses various factors, including clarity, completeness, feasibility, and innovation.

   



4. Score Assignment: Based on the analysis, the AI assigns a score to the response. Each score is backed by a rationale derived from the anchors, potentially used to offer actionable feedback to the entrepreneur.

Practical Applications

Accelerators & Venture Studios: Evaluating applications to select the most promising start-ups.

Funders: Assisting investors in assessing the viability and potential of e.g. business models.

Entrepreneur Feedback: Providing detailed feedback to entrepreneurs for refining their business strategies.

Reducing Bias in Entrepreneur Selection

The problem of Bias

Traditional evaluation methods often suffer from biases, including unconscious bias, and subsequent halo effect, or cultural biases. These can lead to inconsistency in decision quality as well as unfair selection processes, 

thus undermining diversity and innovation.

Anchored Scoring: A Solution

Our AI-powered grading system addresses these biases through anchored scoring. Here’s how:

• Standardization: Anchors provide a standardized framework for evaluation, ensuring that all responses are judged against the same criteria.



• Transparency: The clear expectations set by anchors make the evaluation process transparent. A reasoning is created on what is required to achieve higher scores, promoting fairness.



• Objectivity: By relying on objective criteria defined by anchors, the system minimizes subjective judgments and reduces the influence of individual biases.



• Consistency: Automated grading ensures consistent application of criteria across all responses, enhancing reliability and fairness in selection.

Conclusion

Level's AI-enabled answer grading system represents a significant advancement in the selection processes of the entrepreneur financing and support ecosystem in Africa. By leveraging advanced NLP algorithms and well-defined anchors, our system provides objective, transparent, and consistent evaluations of e.g. business model questions. This not only enhances the selection process but also promotes diversity and reduces bias, ensuring that the most promising entrepreneurs receive the support they need to succeed.

Let us show you how it works in a one-on-one conversation!