Interactive Content Engagement Strategy Development for FreeFuse

FreeFuse aims to enhance user retention and platform engagement through a data-driven interactive content strategy. This project involves analyzing user behavior to identify engagement trends and improvement opportunities. Interns will develop interactive content prototypes tailored to user preferences and test engagement tactics to optimize user experience. By applying user experience design, data analysis, and content creation, learners will provide actionable recommendations that improve interaction rates and platform effectiveness.

Strategic Partnership Playbook for Ecosystem Growth

FreeFuse has use cases across education, creator economy, AI research, and smart media tech. This project will identify potential partners (accelerators, incubators, creator tools, universities) and define a tiered partnership strategy for platform expansion. Objective: To develop a comprehensive and actionable partnership strategy that enables FreeFuse to grow its ecosystem through aligned external organizations—such as innovation labs, educational institutions, creator platforms, AI research hubs, and enterprise collaborators. The goal is to identify and prioritize partnership types that will accelerate adoption, credibility, and product integration , while also building a structured framework FreeFuse can use to evaluate and activate new opportunities over time. Project Goals: Map the Strategic Ecosystem Identify key sectors where FreeFuse can gain traction (e.g., edtech, immersive learning, AI tools, digital media production, nonprofits). Research 10–15 potential partners and categorize them by alignment level (e.g., product synergy, shared audience, growth acceleration). Develop a Tiered Partnership Framework Define partnership levels (e.g., Community Partner, Pilot/Co-Builder, Strategic Anchor). Outline benefits and commitments required at each level. Include partner fit criteria (technical, operational, mission alignment). Create Partner Value Propositions Draft use-case-aligned messaging that explains the value of partnering with FreeFuse from each segment’s perspective (e.g., “Why a university would want FreeFuse,” “Why a tech incubator would align”). Design an Activation Strategy Recommend onboarding flows, pilot programs, and co-marketing tactics. Develop templates for outreach, onboarding checklists, and success metrics. Deliver a Visual Playbook Present a final deliverable that includes visual partner maps, case study examples, and an actionable next-steps calendar.

Pathway Intelligence: Forecasting Interactive Journey Effectiveness on FreeFuse

FreeFuse is an AI-powered platform for building interactive, multi-path digital experiences. As the company expands into personalized content journeys and Agentic AI assistance, there is growing interest in understanding which types of interactive pathways lead to higher engagement and long-term user retention. This project will focus on analyzing and forecasting content journey effectiveness using structural data and behavioral metrics from FreeFuse pathways. In addition to traditional engagement data (e.g., completion rates, drop-offs), students will explore time-to-decision—how long a user takes between choice points—as a signal of content clarity, complexity, and user confidence. Learners will apply data science, predictive modeling, and visualization techniques to identify high-performing pathways, segment engagement styles, and forecast content success based on journey composition and user behavior.

AI Model Optimization for Data Refinement

This project focuses on improving data preparation and AI model training techniques to enhance predictive accuracy. The goal is to create a systematic process for refining datasets, ensuring high-quality input for AI models used in various business applications. Students will analyze data pre-processing methods, evaluate how data inconsistencies impact model performance, and develop an optimized approach to dataset curation. This project is best suited for computer science, AI, or data science students with experience in machine learning and data engineering.