Michigan State University (MSU) was recently awarded a $600,000 research grant to help teachers in the classroom further understand children’s interactions with one another; this research aimed specifically at children with developmental disabilities. The research is done by using Wearable Human Interaction Trackers, or WHIT, and machine learning, both of which will be further explained in this article. This research is expected to be completed by July 2026.
This research is headed by Dr. Subir Biswas, an Electrical Engineering Professor at MSU, and Dr. Sarah Douglas, an Associate Professor in the Department of Human Development and Family Studies at MSU. Dr. Biswas had 10 years of experience in the engineering field before becoming a professor. Dr. Biswas has expertise in software, machine learning, and assistive technology. Dr. Douglas has a background in special education and assistive technology and is also the Director of the Research in Autism and Developmental Disabilities (RADD) Lab at MSU. I had the opportunity to interview both Dr. Biswas and Dr. Douglas about this research project and their vision for this research in the future.
Question: What drove your interest in this specific avenue of research for this project?
Dr. Douglas: My background in special Education teaching and assistive technology including low tech, motor/AT for IDD. Dr. Biswas reached out to me about WHIT to see if there might be a need for the technology. Knowing teachers have limited time to make observations, it seemed like a good solution. The idea of the project is to measure interactions and create feedback mechanisms for teachers.The technology can inform teachers if t students initiate or end interactions, which peers to match them with for interventions, etc. Essentially, WHIT supports teachers so they can better support kids.
Question: What’s the process like to apply for and receive a research grant as large as the one you received?
Dr. Biswas: It took a year or so. The process is to provide a research proposal, specifically proposing research that needs to be solved. The National Science Foundation [where the grant was received from] has a review process that rates how likely to solve research problems. They rank proposals, one out of ten proposals get funding. The fun part is to play with the system and see initial data. We got internal funding at MSU to get initial data prior to proposal.
Dr. Douglas: We also looked for other funding beforehand. By the time we got this fundingAI was mainstream enough there was more confidence in it.
Question: Can you please further explain the WHIT devices?
Dr. Biswas: The core idea is how individuals interact, measured in a quantitative way versus qualitative. An example being one person interacting with another for 10 minutes. We broke down interactions by proximity: one with another individual, vocalization, how often they’re talking when they’re in the presence of someone else, movement, how active one is in a setting. The WHIT device is all in a sensor, which is put inside a pocket of the person. Looking at hard numbers, what you can infer. Sensor component, apply modern state of the art technology to analyze data.
Dr. Douglas: The technology infer different things and share these with the teacher. Such as when a child with autism is less connected with peers, who the child spends the most time with, and who initiates interactions. It can also inform the teacher about the location the child spends time in such as a child gravitating towards the science table. All of this can provide the teacher with a level of detail that they can’t get on their own from the time they can spend on classroom observations.
Question: Can you please further explain how the machine-interpreted algorithms work in analyzing the data?
Dr. Biswas: When I was learning English, I was constantly observing what’s going on. You teach an algorithm to do something or let it learn what to do. With sensor data, you write the rules. Present lots of scenarios, and the algorithm itself finds out data and thresholds. This is moving away from fundamental ways of inferring. High level of machine learning works.
Question: What is the long term vision?
Dr. Douglas: For teachers to use this technology in classrooms to support a variety of children including even those who may be shy or timid. Long term, to be able to intervene sooner, especially for children with mental health challenges. When my youngest was in school, she was very anxious. As a parent, I would want to know. This can be used for mental health broadly and to help better support children to develop friendships.
Questions: How many classrooms have you been in so far?
Dr. Douglas: A few classrooms so far; 3 year old and 4 year old classrooms. The classes vary in tuition, state funded, special ed, head start, and MSU’s on campus preschool. Partnerships are building up with school districts in the area.
Question: Are there any last comments that you would like to add regarding this research project?
Dr. Douglas: With Machine Learning and AI, people get nervous. AI will be an intrical part of our world. Using it as a way to support children and teacher shortages. The future harnessing this technology is important. Let’s embrace it and use it for what matters most.
In conclusion, AI can be a very useful tool to utilize in helping teachers understand how students with developmental disabilities interact with one another, as well as being used in other components of researching individuals with developmental disabilities. The research being done by Dr. Biswas and Dr. Douglas is invaluable in understanding how young children with developmental disabilities interact and what interventions and supports are needed for each child based on WHIT and machine learning. WHIT and machine learning is opening the door to new opportunities in using AI in a positive and productive way to further delve into enhancing research in the area of developmental disabilities. Last, but not least, I would like to thank Dr. Biswas and Dr. Douglas for taking the time to discuss their research as well as thanking them for the amazing work that they are doing.