Team: Pari, David, Sriman, Anishka

Table number: 30

With a variety of language learning resources out there, we set out to create a tool that can help us practice a part of language that keeps things flowing -- conversation! LanguaLine aims to empower users to speak their foreign language by helping them develop their conversational skills.

We wanted to create an interface that can help users practice speaking a foreign language. Through LanguaLine, users can:

• Select a language they wish to practice speaking in
• Select how "motivating" they want their Mentor to be (Basic is normal, Extra Motivation is a tough love approach).
• Enter their phone number and receive a call from our AI Mentor
• Answer questions posed by the Mentor
• Receive real-time feedback about their performance and a generalized report on strengths and weakensses
• View a transcript and summary of the call after the conversation is completed

We used React.js for our frontend, and Firebase for our database. To style our components, we utilized TailwindCSS and React MaterialUI.

To create, tune, and prompt engineer our AI Mentor, we used the VAPI.ai API. Our transcriber model is Deepgram's nova-2 multi and our model is gpt-4o-mini provided by Open.AI. Our Mentor's voice is Alloy, also provided by Open.AI.

We are also using Gemini to analyze users' call transcripts and generate a report identifying strengths and weaknesses in their speaking skills.

Our biggest challenge was understanding the VAPI documentation, as it was our first time working with a voice AI API. We had to make a few changes to our project stack to accommodate for VAPI, as we could only make client-side API calls. unlike our original plan of using Express.js.

Since the majority of our team has limited experience working with LLMs and voice AI Agents, we faced some difficulties prompt engineering our Mentor, requiring us to tweak various model parameters and experiment through VAPI's dashboard.

The turning point in our development process was when we were able to start conversing with our Mentor. After this was solidified, our project trajectory only went upwards. We're proud of the fact we were able to turn this idea into an operational and functional application.

The team behind LanguaLine had a variety of skill levels; for some, this was their first project using this tech stack, while for others, this was familiar. Some of us mastered the ability to send API calls and parse JSON data. Some of us also learned how to prompt engineer for a particular language choice. There were lessons being learned all throughout the 36 hours of development, which helped us feel connected to the project and motivated to keep creating.

We plan to offer more variation in the Mentors we offer. Right now, we only offer Mentors based off of a language choice and motivation level. In the future, we plan to include language difficulties, personalities, greater language support, custom prompts, and scheduled calling.

We also plan to offer improvement plans for grammar, pronunciation, and vocabulary, as well as a scoring system for users' performances during Mentor sessions.