Abstract

Crowdsourcing harnesses the collective intelligence of diverse individuals across organizational boundaries. Traditionally, job posters define the task requirements, post jobs on crowdsourcing platforms, and assess the quality of the work. Recent advancements in artificial intelligence have begun to streamline the entire process. Artificial intelligence can assist job posters in clearly defining job requirements, distributing tasks to qualified workers by matching job requirements with their backgrounds, and evaluating the quality of contributions. The advantage of AI in crowdsourcing is its compatibility with various crowdsourcing models (e.g., idea generation) (Dissanayake et al., 2025). In the idea generation model, AI can group and highlight similar generated ideas as submissions roll in and evaluate their quality based on novelty and feasibility in achieving the goals. Similarly, AI can detect abnormalities (e.g., speedy response times) in the microtasking model and calculate the task error rate. Scholars in information systems (IS) and other disciplines have investigated the role of AI in various fields, demonstrating its potential in handling complex tasks. While current research has explored the use of AI in crowdsourcing, the findings are scattered and vary among crowdsourcing models. The literature review of AI in crowdsourcing provides a clear understanding of how AI transforms crowdsourcing operations and identifies research gaps. Therefore, we organize and analyze the current use of AI in crowdsourcing operations and its impact on these operations. Our work employs the Input-Process-Output (IPO) model, a framework widely used in management research, to examine the current application of AI in crowdsourcing. This framework allows us to decompose the crowdsourcing process into three subprocesses: input, process, and output (Ghezzi et al., 2017). The “input” stage involves defining tasks that workers should perform. Building on this foundation, the “process” stage focuses on how job posters manage the crowdsourcing session (e.g., organizing the submissions during the session), which is necessary for the “output” stage, where solutions are evaluated and selected. In a crowdsourcing process, AI plays a unique role, and how and where AI is used in the process determines the outcomes for each stage (e.g., enhanced clarity of job descriptions). Understanding the outcomes of using AI in crowdsourcing can help us assess the impact of AI on the crowdsourcing process. Therefore, we propose the following research questions: “How is AI applied across the stages of a crowdsourcing process among various crowdsourcing models? ” (RQ1) and “What are the impacts of AI usage across the stages of the crowdsourcing process among various crowdsourcing models? ” (RQ2). In conclusion, this literature review offers a clear understanding of the current application of AI in the crowdsourcing process by breaking down the process into three subprocesses, enabling an examination of the nuances of AI's impact on each subprocess.

Comments

tpp1274

Download

Share

COinS