WIP: Citizen Science Tools with Machine Learning as a Pathway to Engage High School Students in Research

This research-to-practice WIP paper describes an approach to engage high school students in research through the utilization of citizen science tools embedded with Machine Learning (ML) models. In the context of fostering early engagement in scientific research among high school students, this paper explores the integration of citizen science and ML using SmartCS, an existing platform for creating citizen science smartphone applications. The process requires no prior programming knowledge, making it accessible to a broad range of students. For our approach, a group of high school students participated in a two-month-long summer research program, where they were introduced to the principles of citizen science as a method for data collection across diverse scientific projects from different research domains. The program's initial task involved students in the conceptualization of a citizen science project, adopted based on a thorough literature review, followed by the practical task of developing a smartphone application for data collection and educational purposes. Students either created new datasets or curated existing ones to train lightweight ML models for computer vision tasks, specifically focused on providing visual guidance within these mobile apps. The final task involved deploying these applications for public use and collecting user feedback. Our experience suggests that this approach not only enabled students to learn aspects of computer science and engineering, particularly in the area of ML model training and mobile application software development, but also allowed them to experience firsthand the significant role citizen science can play in collecting and analyzing scientific data.