The project aimed to enhance traditional coaching methods by leveraging AI capabilities with human expertise to analyze audio recordings of teaching sessions, provide personalized feedback, and integrate these insights with existing teaching frameworks. Despite the clear vision from our client, the project posed significant challenges, especially since the team and I had no prior experience working with AI or machine learning models.
One of the primary hurdles was translating the vision into a practical, user-centered tool. We had to address complex issues, such as how the AI would analyze classroom dynamics using only audio inputs and provide accurate, actionable feedback. Additionally, we faced technical uncertainties regarding the feasibility and scope of integrating AI into the feedback process. I delved into online resources to overcome these challenges and consulted with subject matter experts. I understood Acoustic Feature Extraction, Voice/Sentiment Analysis, and Natural Language Processing models. Another profound challenge was to bridge the experience between Human and AI interactions. I specifically focused on the feedback section of the product. My goal was to make receiving feedback and putting it into action seamless, along with unhindered navigation between multiple feedback and chat channels with AI and human coaches. Through multiple iterations backed with testing, we successfully designed a platform that provided educators with timely, personalized feedback and possibly empowered them to improve their teaching practices continuously. Our user research revealed that a significant percentage of educators believed that AI could play a vital role in their professional development, and our design directly addressed their pain points, such as delayed feedback and lack of personalization.
The project taught me several key lessons. First, the importance of adaptability in the face of unforeseen technical challenges must be considered. Our team had to continuously iterate on our designs and refine our approach as we learned more about AI's capabilities and limitations. Second, considering the project's complexity and since there was no engineering team in place, collaboration with subject matter experts and users was crucial in designing for different user groups—this ensured that our solution was both technically feasible and genuinely valuable to educators. Lastly, the iterative nature of design, driven by real-world feedback, was critical in delivering an MVP that met the client's vision and the end-users satisfaction.