Enhancing user engagement in online collaborative communities using artificial intelligence

Crowdsourcing platforms are digital spaces where individuals contribute their ideas, skills, or resources toward a shared goal. These platforms have become essential tools in today’s connected world, taking different forms. For example, crowdfunding platforms like Kickstarter and Indiegogo help people and organizations raise money for creative projects, social causes, or new businesses. Innovation platforms like OpenIDEO give users a space to share and improve ideas to solve tough social and environmental problems. Labor marketplaces like Upwork connect businesses with freelancers who offer specific skills. Macrotask platforms like Arcbazar host design competitions where users submit solutions to particular challenges. The success of these platforms depends on one key factor: user engagement. Engaged users keep the platform active, contribute work and resources, and build a strong, collaborative community. This active participation speeds up problem-solving, encourages new ideas, and creates a shared sense of purpose. However, keeping users engaged is challenging. Without strong engagement, even the best-designed platforms can slow down, with users losing interest, contributing less often, or leaving the platform entirely. This makes it crucial to understand what drives user engagement and to create strategies that keep users motivated, involved, and participating regularly over time. Hence, user engagement is the heart of crowdsourcing platforms. It transforms crowdsourcing platforms from simple tools into lively communities where innovation, teamwork, and creativity grow, helping these platforms remain relevant, effective, and successful in a fast-changing digital world. This thesis explores the multifaceted nature of user engagement in crowdsourcing platforms, focusing on the challenges and opportunities that shape user participation. By examining factors such as intrinsic and extrinsic motivation, platform design, trust and transparency, and social influence, this research develops data-driven solutions to enhance engagement. It proposes the integration of AI-driven technologies, such as interactive chatbots for creative stimulation, machine learning models for long-term engagement, and transparency mechanisms for building trust in crowdfunding. Additionally, structured peer feedback systems and gamification strategies are introduced to improve user interaction. By addressing these challenges, this study offers a comprehensive framework to optimize user engagement in crowdsourcing environments, ensuring personalized, transparent, and interactive participation experiences. The findings contribute to the fields of human-computer interaction, AI-driven engagement strategies, and the optimization of crowdsourcing platforms.