SkillPath AI

Inspiration

In today's job market where it is getting increasingly harder to land entry level jobs, we can only imagine how harder it can be for individuals from underrepresented communities. We wanted to create an application that contains everything that you would need to prepare to break into tech. Our solution challenges traditional methods by creating a platform that is easily accessible to everyone thus mitigating any barriers that students from underrepresented communities may have while kickstarting their careers.

What it does

SkillPath AI is an all in one platform that contains several resources to ensure that students succeed. Some of the features that we have in our application are:

1. SkillChat - A personalized career coach that adapts to the user creating a comfortable environment for them to thrive.
2. Job Matching - An interactive feature that filters out jobs for students looking for jobs and enabling them to find only the most relevant ones to their needs.
3. Mentor Pairings - Students can submit their information to the platform and can be matched to mentors who have common interests. Mentors can also sign up and students can reach out to them using their contact information

How we built it

SkillPath contains a comprehensive technology stack that maximizes the user experience. The components can be broken down as follows:

1. Frontend - We used Streamlit as the frontend dashboard as it is a feature that is easy to use and understand for non-tech-savvy users, making it accessible to all, especially those from underserved communities.
2. Backend - The backend was built primarily using Python and SQL. Python was the major programming language that was used to piece together all files in our program. We used SQLAlchemy for object relational mapping to our server, enabling students to save and retrieve their information in our platform. SQLite was used as a server to store user information and job details for students to easily access them.
3. AI Integration - We used OpenAI API to create a personalized career coach through an interactive chatbot feature. This enables the students to have career-focused conversations with the latest gpt models.

Challenges we ran into

The main challenge that we ran into was the scalability of the code. As our program files grew bigger, it became harder for us to maintain the code with all the breaks that it had. Also, connecting multiple files in one big project was a big challenge but we were able to solve the conflicts that we had. As for the scalability, we had to adapt to a modular code design to ensure that we control the function calls that we desired to prevent unwanted code behavior.

Accomplishments that we're proud of

Despite the time constraints that we had, we are proud that we got to build an application that we wish we had as we started out in tech. As it was our first time using OpenAI APIs, we were impressed by how we were able to create a chatbot that used LLMs in the backend in such a short period. Additionally, we are proud of completing the large project through adapting to scalable infrastructure.

What we learned

We gained a deeper understanding of how to use Python in various applications, and SQL in database management. We also learned more about AI and the implications of AI on the workforce. We do not view AI as a disruptive force to replace jobs but as a way to enhance productivity and efficiency of human labor in the workplace.

What's next for SkillPath AI

Next up would be linking the backend directly to job applications so as to make it as easy as possible for students from underrepresented communities to apply to jobs. Another AI agent can be used for realtime resume tailoring to ensure that students are best fit for the jobs that they are applying to

Built With

• openai
• python
• sql
• sqlalchemy
• sqlite
• streamlit