An official website of the United States government
This article portrays how citizen science (CS) projects can be integrated into elementary classrooms to enhance students’ sensemaking skills and connect to real-world science problems. For the last several years, we have been involved in a study, Teacher Learning for Effective School-Based Citizen Science (TL4CS), that developed materials for elementary school teachers to engage their students in data collection, analysis, and interpretation for two existing CS projects: Community Collaborative Rain, Hail, and Snow Network (CoCoRaHS) and the Lost Ladybug Project (LLP). After piloting the TL4CS materials for two years, two teachers, Penny and Amy, share the ways they used the materials to create rich sensemaking experiences for their students. Penny used our TL4CS CoCoRaHS materials to make connections between their daily precipitation data and local weather phenomena, patterns in ecosystems, and student-created graphs. Amy used our TL4CS LLP materials to explore students’ questions about human impact on animals’ habitats and discover the importance of biodiversity in ecosystems. As demonstrated by Penny’s and Amy’s stories, the TL4CS materials can transform mere data collection for CS projects into opportunities for real-world connections and sensemaking in science classrooms.
more »
« less
This content will become publicly available on December 9, 2025
Artificial Intelligence and the Future of Citizen Science
Artificial Intelligence (AI) and citizen science (CS) are two approaches to tackling data challenges related to scale and complexity. CS by its very definition relies on the joint effort of typically a distributed group of non-expert people to solve problems in a manner that relies on human intelligence. As AI capabilities increasingly augment or complement human intelligence, if not replicate it, there is a growing effort to understand the role that AI can play in CS and vice versa. With this growing interest as context, this special collection, The Future of AI and Citizen Science, illustrates the many ways that CS practitioners are integrating AI into their efforts, as well as identifies current limitations. In this spirit, our editorial briefly introduces the special collection papers to demonstrate and assess some uses of AI in CS; then, we contextualize these uses in terms of key challenges; and conclude with future directions that use AI with CS in both innovative and ethical ways.
more »
« less
- PAR ID:
- 10569030
- Publisher / Repository:
- Ubiquity Press
- Date Published:
- Journal Name:
- Citizen Science: Theory and Practice
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2057-4991
- Page Range / eLocation ID:
- 32
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Citizen science and artificial intelligence (AI) complement each other by harnessing the strengths of both human and machine capabilities. Citizen science generates terabytes of raw numerical, text, and image data, the analysis of which requires automated techniques to process in an efficient manner. Conversely, AI computer vision technology can require tens of thousands of images during the training process, and citizen science projects are well suited to provide large libraries of data. Herein, we describe how AI tools are being applied across the GLOBE Observer citizen science data ecosystem, where image recognition algorithms are supporting data ingest processes, protecting user privacy and improving data fidelity. GLOBE citizen science data has been used to develop automated data classification routines that enable information discovery of mosquito larvae and land cover labels. These advances position GLOBE citizen scientist data for discovery and use in environmental and health research, as well as by machine learning scientists working in the general field of GeoAI.more » « less
-
Abstract As artificial intelligence (AI) grows in popularity and importance—both as a domain within broader computing research and in society at large—increasing focus will need to be paid to the ethical governance of this emerging technology. The attitudes and competencies with respect to AI ethics and policy among post-secondary students studying computer science (CS) are of particular interest, as many of these students will go on to play key roles in the development and deployment of future AI innovations. Despite this population of computer scientists being at the forefront of learning about and using AI tools, their attitudes towards AI remain understudied in the literature. In an effort to begin to close this gap, in fall 2024 we fielded a survey ($$n=117$$) to undergraduate and graduate students enrolled in CS courses at a large public university in the United States to assess their attitudes towards the nascent fields of AI ethics and policy. Additionally, we conducted one-on-one follow-up interviews with 13 students to elicit more in-depth responses on topics such as the use of AI tools in the classroom, ethical impacts of AI, and government regulation of AI. In this paper, we describe the findings of our exploratory study, drawing parallels and contrasts to broader public opinion polling in the United States. We conclude by evaluating the implications of CS student attitudes on the future of AI education and governance.more » « less
-
Analogy—the ability to find and apply deep structural patterns across domains—has been fundamental to human innovation in science and technology. Today there is a growing opportunity to accelerate innovation by moving analogy out of a single person’s mind and distributing it across many information processors, both human and machine. Doing so has the potential to overcome cognitive fixation, scale to large idea repositories, and support complex problems with multiple constraints. Here we lay out a perspective on the future of scalable analogical innovation and first steps using crowds and artificial intelligence (AI) to augment creativity that quantitatively demonstrate the promise of the approach, as well as core challenges critical to realizing this vision.more » « less
-
As we enter an era where the synergy between AI technologies and human effort is paramount, the Future of Work is undergoing a radical transformation. Emerging AI tools will profoundly influence how we work, the tools we use, and the very nature of work itself. The ’Human Computation, Equitable, and Innovative Future of Work AI Tools’ workshop at HCOMP’24 aims to explore groundbreaking solutions for developing fair and inclusive AI tools that shape how we will work. This workshop will delve into the collaborative potential of human computation and artificial intelligence in crafting equitable Future of Work AI tools. Participants will critically examine the current challenges in designing fair and innovative AI systems for the evolving workplace, as well as strategies for effectively integrating human insights into these tools. The primary objective is to foster a rich discourse on scalable, sustainable solutions that promote equitable Future of Work tools for all, with a particular focus on empowering marginalized communities. By bringing together experts from diverse fields, we aim to catalyze ideas that bridge the gap between technological advancement and social equity.more » « less
