Assistive Technology for Neurodivergent Space Travel
At The Neuroverse Initiative, we believe that space should be accessible to everyone, and what's more, if space is accessible to neurodivergent people, then it will be accessible to everyone! We are currently in the process of beginning research into assistive technologies that can help future neurodivergent astronauts to travel to, live, and work in space.
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Neurodiversity in STEM
We recognize that many space science jobs begin in college, with at least some necessitating advanced degrees. Unfortunately, the research regarding the experiences of neurodivergent undergraduates and graduate students in college is lacking. But at The Neuroverse Initiative, we are dedicated to the understanding that autistic children grow into autistic adults and need a diverse set of accommodations throughout their studies. Alongside our training arm, specializing in training for neuroinclusion in graduate departments, we work to bring neuroinclusion into spaces from a research-based perspective. But what do we do if the research isn't there? Well, we perform it.
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Current Work
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The Experiences of Neurodivergent STEM Undergraduate Students and How it Relates to their Feelings of Belonging
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​Dr. Jessica Schonhut-Stasik and Jiaxin "Jessie" Wang
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In this work we are conducting a literature review of current research related to the feelings of belonging for neurodivergent undergraduates in STEM courses within the US. We are interested in collating all existing understanding of this work so that we can begin to understand how to frame neuroinclusion training and to work towards our own focused research in the area. This work is funded by a generous grant from the Vanderbilt Lacy-Fischer Grant Program.​​​​​​​​​​​
Neurodiversity Adjacent Research
Much of the current work in the area of disability studies overlaps with supporting the neurodivergent community, and our team works with adjacent research teams to perform research into other disability sectors, in turn supporting those on the spectrum and with cooccurring disabilities.
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Current Work
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TNI Collaboration with the NASA Chandra Team​​​​​
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Dr. Jessica Schonhut-Stasik works directly with Chandra's Communication and Public Engagement lead Dr. Kimberly Arcand, who is a leading visualization scientist. So far they have worked on collaborations that enlist the use of sonification to improve the experience of blind and low-vision (BLV) individuals in their access of astronomy, and are currently working together to investigate the use of AI to create alternative text for astronomical images, allowing those from the BLV communities to understand visual data.
How Does This Relate to Neurodiversity?
Many of the methods that are being developed to support the BLV community directly relate to supporting the neurodivergent community, as a direct consequence of the theory of Universal Design for Learning. For example, sonifications can be used alongside visual data to support the learning of neurodivergent individuals who need the data to be demonstrated in a different way. Much of the research this team are conducting is planned to be researched further with the neurodivergent space in mind.
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​Current Work​
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A Universe of Sound: Processing NASA Data into Sonifications to Explore Participant Response​​
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​Dr. Kimberly Arcand and Dr. Jessica Schonhut-Stasik along with: Sarah Kane, Gwynn Sturdevant, Matt Russo, Megan Watzke, Brian Hsu, and Lisa F. Smith​​
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Introduction: Historically, astronomy has prioritized visuals to present information, with scientists and communicators overlooking the critical need to communicate astrophysics with blind or low-vision audiences and provide novel channels for sighted audiences to process scientific information.
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Methods: This study sonified NASA data of three astronomical objects presented as aural visualizations, then surveyed blind or low-vision and sighted individuals to elicit feedback on the experience of these pieces as it relates to enjoyment, education, and trust of the scientific data.
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Results: Data analyses from 3,184 sighted or blind or low-vision survey participants yielded significant self-reported learning gains and positive experiential responses.
Discussion: Results showed that astrophysical data engaging multiple senses could establish additional avenues of trust, increase access, and promote awareness of accessibility in sighted and blind or low-vision communities.
This work utilized the sonifications from NASA’s Universe of Sound.
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Status: Published
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Journal: Frontiers of Communciation
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Link to PDF: https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2024.1288896/full (then hit download)
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Bibtex Citation:
@ARTICLE{10.3389/fcomm.2024.1288896,
AUTHOR={Arcand, Kimberly Kowal and Schonhut-Stasik, Jessica Sarah and Kane, Sarah G. and Sturdevant, Gwynn and Russo, Matt and Watzke, Megan and Hsu, Brian and Smith, Lisa F. },
TITLE={A Universe of Sound: processing NASA data into sonifications to explore participant response},
JOURNAL={Frontiers in Communication},
VOLUME={Volume 9 - 2024},
YEAR={2024},
URL={https://www.frontiersin.org/journals/communication/articles/10.3389/fcomm.2024.1288896},
DOI={10.3389/fcomm.2024.1288896},
ISSN={2297-900X}}
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Accessing the Universe via Algorithm: Automating the alt-text Generation with NASA Data and AI​
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​Dr. Kimberly Arcand and Dr. Jessica Schonhut-Stasik along with Areshva Aisha Mir, Tianze Hua, Anna Ohrt, Sarah Kane, Nancy Wolk, and Tom Sgouros
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''Alt-text'' is a textual substitute for communicating visual information on a digital platform, accessed and used primarily by screen reader users, but also by others. This pilot study aimed to develop a reliable method for automating alt-text generation for publicly released science images from NASA's Chandra X-ray Observatory. Using pre-trained vision-language models and image metadata, generating texts was straightforward, but the results were not reliable. We used fact-checking and evaluation filters to improve output, including a two-tier system of “exogenous” and “endogenous” checks to ensure that the information presented in the alt-text is accurate according to the information provided in the caption and the visual components present in the image itself. The evaluation step used a "rubric", a commonly used technique in education, as an evaluation model to encourage consistency in the output. This multistage approach is congruent with current AI research directions, though here it is a way to achieve real-world results rather than simply a promising avenue for inquiry. Since the evaluation step yields a distance metric, our strategy enables adaptability to automated corrections or improvements in training.​​​
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Status: In Submission
