Hae-Na Lee

The growing demand for computer professionals, driven by the expanding Information Technology industry, has led to numerous inclusive computing education efforts. These efforts have even included blind or visually-impaired (BVI) students, who are being increasingly encouraged to pursue education and a career in computing, despite the visually-oriented nature of the discipline. Extant literature has predominantly focused on identifying and addressing the accessibility barriers faced by BVI students to promote more inclusive learning environments. While few studies have also investigated the accessibility of computing education from the perspectives of BVI learners and instructors, these have been primarily situated in the Global North contexts; there is still a knowledge gap regarding the teaching and learning experiences of instructors and BVI students, respectively, in resource-constrained Global South contexts, where accessibility awareness and inclusion efforts are at nascent stages. To fill this gap, we conducted an interview study with 15 participants in India, where we inquired with BVI students, instructors, and BVI professionals, regarding their challenges, experiences, and needs pertaining to computing education. The study revealed that BVI students face significant difficulty in comprehending the instructional materials, the instructors often deal with courses not progressing as planned despite meticulous preparation, the students heavily depend on peer learning for grasping computing concepts, and they need additional support for managing the cognitively-burdensome task of simultaneously learning computing concepts and screen readers. Informed by the findings, we offer recommendations to improve computing curricula for BVI students and discuss self-learning assistive tools to supplement accessible computing education.

Significant changes in the digital employment landscape, driven by rapid technological advancements and the COVID-19 pandemic, have introduced new opportunities for blind and visually impaired (BVI) individuals in developing countries like India. However, a significant portion of the BVI population in India remains unemployed despite extensive accessibility advancements and job search interventions. Therefore, we conducted semi-structured interviews with 20 BVI persons who were either pursuing or recently sought employment in the digital industry. Our findings reveal that despite gaining digital literacy and extensive training, BVI individuals struggle to meet industry requirements for fulfilling job openings. While they engage in self-reflection to identify shortcomings in their approach and skills, they lack constructive feedback from peers and recruiters. Moreover, the numerous job intervention tools are limited in their ability to meet the unique needs of BVI job seekers. Our results, therefore, provide key insights that inform the design of future collaborative intervention systems that offer personalized feedback for BVI individuals, effectively guiding their self-reflection process and subsequent job search behaviors, and potentially leading to improved employment outcomes.

Online reviews have become an integral aspect of consumer decision-making on e-commerce websites, especially in the restaurant industry. Unlike sighted users who can visually skim through the reviews, perusing reviews remains challenging for blind users, who rely on screen reader assistive technology that supports predominantly one-dimensional narration of content via keyboard shortcuts. In an interview study, we uncovered numerous pain points of blind screen reader users with online restaurant reviews, notably, the listening fatigue and frustration after going through only the first few reviews. To address these issues, we developed QuickCue assistive tool that performs aspect-focused sentiment-driven summarization to reorganize the information in the reviews into an alternative, thematically-organized presentation that is conveniently perusable with a screen reader. At its core, QuickCue utilizes a large language model to perform aspect-based joint classification for grouping reviews, followed by focused summarizations within the groups to generate concise representations of reviewers’ opinions, which are then presented to the screen reader users via an accessible interface. Evaluation of QuickCue in a user study with 10 participants showed significant improvements in overall usability and task workload compared to the status quo screen reader.