Exploring User Adoption and Experience of Automated Machine Learning Platforms with a Focus on Learning Curves, Usability, and Design Considerations

Introduction Human daily activities and businesses generate a significant volume of data, which is expected to be transformed for the benefit of both businesses and humanity. Organisations utilise machine learning platforms to make informed decisions based on well-gleaned insights from their real-time data. The process of learning machine learning is challenging, making it difficult for employees to learn quickly and efficiently. Meanwhile, the introduction of automated machine learning (AutoML) has simplified this process. However, it is essential to understand how users adopt and implement the AutoML platform to address their real-world problems. Methods To achieve this, we conducted a quantitative study with 38 users focusing on four key areas: (1) the learning curve in ML and AutoML environments, (2) the design and usability strengths and weaknesses of AutoML platforms, (3) disparities in user experience between novices and professionals, and (4) design factors to enhance usability. Result Our findings revealed that users, particularly those with limited programming experience, have high expectations for the usability of AutoML; however, they also exhibit low awareness and adoption rates in the African context. Discussion The study illuminates gender disparities in technology adoption and identifies critical usability concerns, including the need for improved interpretability, feature engineering modules, and code integration for learning purposes. Additionally, we provide empirical evidence demonstrating AutoML’s advantages regarding training time and reproducibility compared to traditional machine learning tools. Conclusion This work offers novel insights into human-centered AutoML design, emphasizing inclusivity, explainability, and user-friendly interfaces. By addressing regional and gender-specific challenges, we propose actionable recommendations to democratize ML and enhance AutoML platforms. Future research should expand upon these findings by engaging frequent AutoML users to further refine usability and satisfaction metrics.