The proliferation of Generative Artificial Intelligence (GenAI) tools has brought a critical shift in how people approach information retrieval and content creation in diverse contexts. Yet, we have limited understanding of how blind people use and make sense of GenAI systems. To bridge this gap, we report findings from interviews with 19 blind individuals who incorporate mainstream GenAI tools like ChatGPT and Be My AI in their everyday practices. Our findings reveal how blind users navigate accessibility issues, inaccuracies, hallucinations, and idiosyncracies associated with GenAI and develop interesting (but often flawed) mental models of how these tools work. We discuss key considerations for rethinking access and information verification in GenAI tools, unpacking erroneous mental models among blind users, and reconciling harms and benefits of GenAI from an accessibility perspective.

Online learning is playing a significant role, especially during the COVID-19 pandemic. In this study, we perform an interview study through in-depth interviews with 22 high school students of a developing country (Bangladesh) to find out about their experience and practices with online learning during the pandemic. Our findings reveal several usage strategies, challenges of the conventional usage of online learning, workarounds students adopt to address those challenges. Through the adaptability lens, we find that many students are adapting to online learning despite being in favor of it.

The recent Covid-19 pandemic elucidates the need for a better disease outbreak analysis and surveillance system, which can harness state-of-the-art data mining and machine learning techniques to produce better forecasting. In this regard, understanding the correlation between disease outbreaks and socioeconomic factors should pave the way for such systems by providing useful indicators, which are yet to be explored in the literature to the best of our knowledge. Therefore, in this study, we accumulated data on 72 infectious diseases and their outbreaks all over the globe over a period of 23 years as well as corresponding different socioeconomic data. We, then, performed point-biserial and spearman correlation analysis over the collected data. Our analysis of the obtained correlations demonstrates that various disease outbreak attributes are positively and negatively correlated with different socioeconomic indicators. For example, indicators such as lifetime risk of maternal death, adolescent fertility rate, etc., are positively correlated, while indicators such as life expectancy at birth, measles immunization, etc., are negatively correlated, with disease outbreaks that affect the digestive organ system. In this paper, we find and summarize the correlations between 126 outbreak attributes derived from the characteristics of the 72 diseases in consideration and 192 socioeconomic factors which is a novel contribution to the field of disease outbreak analysis and prediction.