Bedtime stories are a familiar ritual of childhood, but what if it was a robot saying “Once upon a time …?” Indiana University robotics experts Selma Sabanovic, Karl MacDorman, and colleagues conducted a recent study exploring parental acceptance of storytelling robots. As the COVID-19 pandemic dramatically increased the use of technology for education, entertainment, and more, the researchers say it made sense to examine the potential of robots in parenting tasks such as telling a bedtime story. Overall, they found that although parents had some concerns, they were generally willing to accept children’s storytelling robots as a “parent double”, and actually preferred robots over screen-based technologies. In the study, parents were given the choice of one of two robots to use for their child’s story time. Afterward, the researchers conducted interviews with the parents about the bedtime story experiences. Parents liked the robots for their ability to do things such as read the same book repeatedly. However, they were ambivalent about a robot that closely simulated human autonomy and intelligence, which often elicits a creepy, eerie feeling that researchers call the “uncanny valley”. Still, the researchers say that there are clear values associated with a storytelling robot--for example, in situations where caregivers may be lacking or in remote schooling where a human caregiver is not always available. The researchers say future directions for designing children’s storytelling robots include research on how to create educational and affective experiences for at-risk young children, how to promote well-being and quality of life of parents, and design principles for healthy human–robot relationships.
In other news, Alzheimer’s disease affects over 5 million Americans, yet there is still no definitive reason to explain why it occurs. To gain a better understanding of complex diseases such as Alzheimer’s, IUPUI researcher Jingwen Yan is developing machine learning models to make predictions and explore insights about how the disease functions. Studying big data, Yan says, can reveal patterns and help identify the reasons and causes why some people develop Alzheimer’s in the first place. For instance, she says, researchers can look at different subgroups of patient data and may be able to say that a person develops Alzheimer’s because of a genetic variation or an incorrect chemical reaction. That would enable patients to receive a personalized treatment plan based on their specific cause. Yan and colleagues use brain scans of both Alzheimer’s patients and healthy individuals to collect information for their machine learning models and look at what part of the DNA is responsible for brain changes. Yan also uses brain imaging and genetic associations to study the impact of opioid use by pregnant women and whether babies with moms who used opioids will have fewer brain connections than babies born to moms who did not use opioids. Yan says the application of artificial intelligence, and specifically machine learning, in brain imaging is a frontier application of AI, one with great potential to address many data-intensive biomedical problems.