| human-computer-collaborative-learning-in-citizen-science |
organization |
human-computer-collaborative-learning-in-citizen-science |
| human-computer-collaborative-learning-in-citizen-science |
organization |
human-computer-collaborative-learning-in-citizen-science |
| human-computer-collaborative-learning-in-citizen-science |
organization |
human-computer-collaborative-learning-in-citizen-science |
| human-computer-collaborative-learning-in-citizen-science |
organization |
human-computer-collaborative-learning-in-citizen-science |
| human-computer-collaborative-learning-in-citizen-science |
endDate |
2022-10-31 |
| human-computer-collaborative-learning-in-citizen-science |
hasPrincipalInvestigator |
8eb9378b0e3dcd225dfc47fcdc9b35f4 |
| human-computer-collaborative-learning-in-citizen-science |
startDate |
2019-11-01 |
| human-computer-collaborative-learning-in-citizen-science |
type |
Project |
| human-computer-collaborative-learning-in-citizen-science |
comment |
This project explores the potential for collaborative learning between humans and
machines within the framework of environmental citizen science. |
| human-computer-collaborative-learning-in-citizen-science |
label |
Human-Computer Collaborative Learning in Citizen Science |
| human-computer-collaborative-learning-in-citizen-science |
depiction |
CSAI.png |
| human-computer-collaborative-learning-in-citizen-science |
depiction |
default.gif |
| human-computer-collaborative-learning-in-citizen-science |
homepage |
xpollination.org |
| human-computer-collaborative-learning-in-citizen-science |
name |
Human-Computer Collaborative Learning in Citizen Science |
| human-computer-collaborative-learning-in-citizen-science |
page |
human-computer-collaborative-learning-in-citizen-science |
| human-computer-collaborative-learning-in-citizen-science |
Description |
This project explores the potential for collaborative learning between humans and
machines within the framework of environmental citizen science. The term `citizen
science' encompasses public participation in science and scientific communication
to the public. Although not new, citizen science has gained renewed attention because
of the opportunities arising from citizens' access to digital technologies in terms
of data collection and annotation. While the vast majority of citizen science projects
are aimed at data gathering, we instead propose a transformational shift to a new
citizen science in which the public and technology are regarded not just as sensors
or data recorders, but as a collective and empowered human--artificial intelligence
that can help each other in science learning.
We will focus on the task of species identification from images. Citizen science projects
such as iSpot invite the public to submit photos of wildlife. These are identified
to species level and verified before being contributed to science. We will explore
artificial intelligence as a means to automatically identify species in images. While
this can save human effort, we are concerned about impact this might have on nature
lovers. The introduction of technology is often associated with concerns of de-skilling.
For naturalists, the honing of species identification skills is a key motivator of
the recording activity. Hence, designing technology that provides opportunities for
learning for both citizens and machines is essential, as is co-creating the technology
to ensure that it is not only user friendly but responds to their motivations. Our
approach will involve citizens collaborating with AI to arrive at
a species identification. AI will narrow down the choices and inform the citizen about
how to distinguish the options. The citizen in turn will through providing an identification
help the machine in its learning. We will study this learning interplay with respect
to collaborative species identification, but will also explore technologies that foster
wider science learning, environmental consciousness and data literacy through better
communication of complex citizen science data. For this we will develop technology
for Natural Language Generation that can communicate complex data through language.
Our proposed work programme seeks to bring about quantifiable benefits to (a) science,
e.g., through the production of new knowledge and through monitoring key scientific
processes at challenging temporal-spatial scales; (b) diverse stakeholders including
the citizens themselves, e.g., through meaningful science learning for sustainability
in formal and informal education contexts; and (c) wider society, e.g., through better
societal understanding of current sustainability issues, leading to individual and
societal action in support of the environment. <br><br><b>Funder:</b> EPSRC |
| human-computer-collaborative-learning-in-citizen-science |
Description |
This project explores the potential for collaborative learning between humans and
machines within the framework of environmental citizen science. The term `citizen
science' encompasses public participation in science and scientific communication
to the public. Although not new, citizen science has gained renewed attention because
of the opportunities arising from citizens' access to digital technologies in terms
of data collection and annotation. While the vast majority of citizen science projects
are aimed at data gathering, we instead propose a transformational shift to a new
citizen science in which the public and technology are regarded not just as sensors
or data recorders, but as a collective and empowered human--artificial intelligence
that can help each other in science learning.
We will focus on the task of species identification from images. Citizen science projects
such as iSpot invite the public to submit photos of wildlife. These are identified
to species level and verified before being contributed to science. We will explore
artificial intelligence as a means to automatically identify species in images. While
this can save human effort, we are concerned about impact this might have on nature
lovers. The introduction of technology is often associated with concerns of de-skilling.
For naturalists, the honing of species identification skills is a key motivator of
the recording activity. Hence, designing technology that provides opportunities for
learning for both citizens and machines is essential, as is co-creating the technology
to ensure that it is not only user friendly but responds to their motivations. Our
approach will involve citizens collaborating with AI to arrive at
a species identification. AI will narrow down the choices and inform the citizen about
how to distinguish the options. The citizen in turn will through providing an identification
help the machine in its learning. We will study this learning interplay with respect
to collaborative species identification, but will also explore technologies that foster
wider science learning, environmental consciousness and data literacy through better
communication of complex citizen science data. For this we will develop technology
for Natural Language Generation that can communicate complex data through language.
Our proposed work programme seeks to bring about quantifiable benefits to (a) science,
e.g., through the production of new knowledge and through monitoring key scientific
processes at challenging temporal-spatial scales; (b) diverse stakeholders including
the citizens themselves, e.g., through meaningful science learning for sustainability
in formal and informal education contexts; and (c) wider society, e.g., through better
societal understanding of current sustainability issues, leading to individual and
societal action in support of the environment. |
| human-computer-collaborative-learning-in-citizen-science |
in dataset |
kmifoaf |
