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Automated personality and career assessments

Generative AI models like LLMs (Language Learning Models) and image generators can revolutionize personality and career assessments. For example, a chatbot developed using OpenAI's ChatGPT can conduct an interactive interview with a candidate, probing deeper on responses using its vast knowledge base. It can assess multi-dimensional traits and provide a comprehensive personality insight based on a candidate's responses.

Similarly, an image generator AI can evaluate candidates' non-verbal cues during a video interview. For example, it could analyze facial expressions, posture or gestures, and match them with their verbal responses to create a complete candidate profile. This can provide a multi-dimensional view of a person's confidence, emotional intelligence, or leadership potential, and matching it with job roles they are most likely to excel in.

Such AI assessments offer personalized, consistent, and scalable tools for career guidance professionals.

How to build with Clevis

Text Input

Ask the user to enter their name for personalization

Prompt ChatGPT

Prompt ChatGPT to ask the user a series of questions about their interests, values, and preferences to assess their personality

Prompt ChatGPT

Use the output from the previous step to prompt ChatGPT to suggest potential career paths based on the user's personality

HTTP Request

Fetch career data from an API based on the user's suggested career paths

Display Output

Format and display the assessment results, including the user's personality traits and recommended career paths

This is an illustration of an AI App called 'Automated Personality and Career Assessments', created using Clevis. Just like this one, you can use Clevis to build apps within the same area, leveraging AI and machine learning.

The said application commences by letting the user enter their name for a more personalized experience (Text Input). Subsequent to this, it employs OpenAI's language model, ChatGPT, to converse with the user. Through a series of inquiries about their interests, values, and preferences, the model can map out their personality (ChatGPT).

ChatGPT is again used in the following step to extrapolate potential careers suitable for the user. This is learning-based derived from the first interaction detailing the user's personality (ChatGPT). In order to correctly assort the suggestions, an HTTP request retrieves career data from an API corresponding to these prompted career paths (Http Request).

Upon receiving this, the application formats and showcases the assessment outcomes. This includes a description of the user's personality traits and the recommended career paths ensuing the assessment (Display Output). This presents the user with meaningful and personalized feedback about potential career paths tailored to their unique personality profile.

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