Who Are You Asking?: Qualitative Methods for Involving AAC Users as Primary Research Participants
When trying to understand people's perspectives, qualitative researchers in HCI often use methods which assume participants can easily communicate verbally. There are few dedicated resources in HCI which provide an overview of qualitative methods to effectively gather the perspectives of people who cannot easily communicate verbally; specifically, people who use Augmentative and Alternative Communication (AAC). As a result, AAC users might be excluded from studies using methods such as interviews or focus groups, even if they fit the researcher's target population. To address this problem, I review literature from both HCI and therapeutic AAC research fields to discuss methods used with AAC users. In addition, I present relevant case examples from my own qualitative research and propose a framework to guide HCI researchers on choosing appropriate methods when involving AAC users as central research participants. I also identify design opportunities for HCI researchers to innovate on the tools and methods used for qualitative research with AAC users. This paper provides an easily accessible overview of qualitative methods HCI researchers can use with AAC users as participants.
Erin Beneteau
Projects in Human-Computer Interaction
- Leveraging Collaborative Filtering for Personalized Behavior Modeling: A Case Study on Depression Detection among College Students
- On the Steppe: Plain Talk Imagining Technology Used Wisely
- Using Everyday Routines for Understanding Health Behaviors
- When Screen Time Isn’t Screen Time: Tensions and Needs Between Tweens and Their Parents During Nature-based Exploration
- Falx: Synthesis-Powered Visualization Authoring
- What Makes People Join Conspiracy Communities? Role of Social Factors in Conspiracy Engagement
- Visually Encoding Personal Data for Vulnerable Populations
- Who Are You Asking?: Qualitative Methods for Involving AAC Users as Primary Research Participants
- Where Are My Parents?: Information Needs of Hospitalized Children
- Parenting with Alexa: Exploring the Introduction of Smart Speakers on Family Dynamics
- “Eavesdropping”: An Information Source for Inpatients
- Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection
- Mobile Assessment of Acute Effects of Marijuana on Cognitive Functioning in Young Adults: Observational Study
- Telling Stories: On Culturally Responsive Artificial Intelligence
- What Makes People Join Conspiracy Communities?: Role of Social Factors in Conspiracy Engagement
- Early adopters of a low vision head-mounted assistive technology
- Being (In)Visible: Privacy, Transparency, and Disclosure in the Self-Management of Bipolar Disorder
- Visualizing Personal Rhythms: A Critical Visual Analysis of Mental Health in Flux