VizWiz grand challenge workshop at CVPR 2022

BACKGROUND: There is lack of understanding of “user experience”; i.e. how people with visual impairments use today’s emerging mainstream technologies along with assistive technologies, which would ultimately lead to less user-friendly designs. OBJECTIVE: The primary aim of this study is to advance knowledge of user needs and concerns about usability of mainstream and assistive technologies. METHODS: The archival research method was used to obtain data via online forum sites. After reviewing six forum sites, this study has found 92 messages that specifically discussed the use of technology, and reviewed the messages by comparing them with ten usability heuristics by Nielsen. RESULTS: The majority of usability problems were associated with flexibility and efficiency of use; help for users to recognize, diagnose, and recover from errors; and help documentation. CONCLUSIONS: It is imperative to focus on improving usability of both assistive and mainstream technologies in use by people with visual impairments.

Abstract: Introduction: Assistive technologies (AT) are products, equipment, devices, resources, methodologies and strategies that can be used in education to promote autonomous learning and academic success for students who have some type of disability. Objective: The objective was to analyze the AT available in medical courses in Brazil to support medical students with hearing and/or visual impairment. Methods: This is a descriptive study that used data from undergraduate medical courses participating in the Higher Education Census coordinated by Anísio Teixeira National Institute of Educational Studies and Research (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira, INEP), in 2018. AT for students with hearing and/or visual impairment that were available in medical courses were evaluated considering the characteristics of the institution, such as the type of administration (public versus private), location (capital versus interior) and the year the course started (from the year 2000 versus before year 2000). Results: In 2018, there were 323 active medical courses in Brazil. Most of them (90%) confirmed the offer of at least one type of AT. The discipline of Brazilian Sign Language (LIBRAS, Linguagem Brasileira de Sinais) was the most frequently offered AT (80%) and tactile material was the least offered AT (32%). There was greater comprehensiveness of AT supply for medical students in courses with a private administration, compared to the public ones. The location and the year the course started did not influence the provision of AT in Brazil. The courses showed better AT comprehensiveness to support students with hearing impairment than those with visual impairment. Conclusion: Most medical courses supply AT for people with visual and/or hearing impairments who are medical students, but incompletely. Although the location of the course and the length of time that the course has been active did not influence AT provision, the study revealed inequalities. Medical students with visual and/or hearing impairments have access to assistive technologies especially in courses with private administration, adding more barriers to their financial possibilities, which are already sufficiently affected by the fact that they have to meet the essential needs common to people living with disabilities.

Abstract: Introduction: Assistive technologies (AT) are products, equipment, devices, resources, methodologies and strategies that can be used in education to promote autonomous learning and academic success for students who have some type of disability. Objective: The objective was to analyze the AT available in medical courses in Brazil to support medical students with hearing and/or visual impairment. Methods: This is a descriptive study that used data from undergraduate medical courses participating in the Higher Education Census coordinated by Anísio Teixeira National Institute of Educational Studies and Research (Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira, INEP), in 2018. AT for students with hearing and/or visual impairment that were available in medical courses were evaluated considering the characteristics of the institution, such as the type of administration (public versus private), location (capital versus interior) and the year the course started (from the year 2000 versus before year 2000). Results: In 2018, there were 323 active medical courses in Brazil. Most of them (90%) confirmed the offer of at least one type of AT. The discipline of Brazilian Sign Language (LIBRAS, Linguagem Brasileira de Sinais) was the most frequently offered AT (80%) and tactile material was the least offered AT (32%). There was greater comprehensiveness of AT supply for medical students in courses with a private administration, compared to the public ones. The location and the year the course started did not influence the provision of AT in Brazil. The courses showed better AT comprehensiveness to support students with hearing impairment than those with visual impairment. Conclusion: Most medical courses supply AT for people with visual and/or hearing impairments who are medical students, but incompletely. Although the location of the course and the length of time that the course has been active did not influence AT provision, the study revealed inequalities. Medical students with visual and/or hearing impairments have access to assistive technologies especially in courses with private administration, adding more barriers to their financial possibilities, which are already sufficiently affected by the fact that they have to meet the essential needs common to people living with disabilities.

Chapter 1. Assistive Technologies for Improving Quality of Life Giulio E. Lancioni, Nirbhay N. Singh.- Chapter 2. Assistive Technology for People with Acquired Brain Injury Mandy Rispoli, Wendy Machalicek, Russell Lang.- Chapter 3. Assistive Technology for Postsecondary Students with Disabilities Russell Lang, Sathiyaprakash Ramdoss, Jeff Sigafoos, Vanessa Green, Larah van der Meer, Amy Tostanoski, Allyson Lee, Mark O'Reilly.- Chapter 4. Assistive Technology for People with Communication Disorders Jeff Sigafoos, Ralf W. Schlosser, Giulio E. Lancioni, Mark F. O'Reilly, Vanessa A. Green, Nirbhay N. Singh.- Chapter 5. Assistive Technology for Students with Visual Impairments and Blindness Austin M. Mulloy, Cindy Gevarter, Megan Hopkins, Kevin S. Sutherland, Sathiyaprakash T. Ramdoss.- Chapter 6. Assistive Technology for People with Autism Spectrum Disorders Russell Lang, Sathiyaprakash Ramdoss, Tracy Raulston, Amarie Carnet, Jeff Sigafoos, Robert Didden, Dennis Moore, Sam DiGangi, Mark O'Reilly.- Chapter 7. Assistive Technology for People with Behavior Problems Mark O'Reilly, Giulio E. Lancioni, Jeff Sigafoos, Russell Lang, Olive Healy, Nirbhay N. Singh, Audrey Sorrells, Soyeon Kang, Heather Koch, Laura Rojeski, Cindy Gevarter.- Chapter 8. Assistive Technology For People With Alzheimer's Disease Nirbhay N. Singh, Giulio E. Lancioni, Jeff Sigafoos, Mark O'Reilly, Alan S.W. Winton Chapter 9. Assistive Technology for Individuals with Learning Disabilities Diane P. Bryant, Brian R. Bryant, Min Wook Ok.- Chapter 10. Assistive Technology for People with Severe/Profound Intellectual and Multiple Disabilities Giulio E. Lancioni, Nirbhay N. Singh, Mark F. O'Reilly, Jeff Sigafoos, Doretta Oliva.

We propose the popular board game Blood Bowl as a new challenge for Artificial Intelligence (AI). Blood Bowl is a fully-observable, stochastic, turn-based, modern-style board game with a grid-based game board. At first sight, the game ought to be approachable by numerous game-playing algorithms. However, as all pieces on the board belonging to a player can be moved several times each turn, the turn-wise branching factor becomes overwhelming for traditional algorithms. Additionally, scoring points in the game is rare and difficult, which makes it hard to design heuristics for search algorithms or apply reinforcement learning. We present the Fantasy Football AI (FFAI) framework that implements the core rules of Blood Bowl and includes a forward model, several OpenAI Gym environments for reinforcement learning, competition functionalities, and a web application that allows for human play. We also present Bot Bowl I, the first AI competition that will use FFAI along with baseline agents and preliminary reinforcement learning results. Additionally, we present a wealth of opportunities for future AI competitions based on FFAI.

Hazardous waste capacity assurance planning: Volumes for one‐time remediation

This chapter provides an overview of studies that have evaluated the use of assistive technology to improve the communication functioning of individuals with specific types of communication disorders. The studies are divided into three groups based on the nature of the presenting communication disorder. The first group concerns studies that targeted individuals with cerebral palsy. The second group concerns studies on using assistive technology for people with other motor speech disorders, such as apraxia, dysarthria, and amyotrophic lateral sclerosis (ALS). The third group consists of studies focused on the use of assistive communication technologies to improve spoken and written language expression in people with aphasia. The final part of this chapter analyzes the overall outcomes of the three groups of studies reviewed, considers the implications of these findings for overall communication and social functioning, and highlights directions for future research.

Background: Artificial intelligence (AI) has emerged as a transformative force in nursing education, offering benefits such as adaptive learning, simulations, and intelligent assessment. However, challenges related to ethics, privacy, and technical limitations may influence students’ ability to engage confidently with AI. Aim: To examine the mediating role of AI challenges in the relationship between nursing students’ attitudes toward AI and their self-efficacy. Methods: A cross-sectional study was conducted with a convenience sample of 342 undergraduate nursing students across all four academic levels at Sohag University. Data were collected using three validated instruments: the AI Challenges Assessment Questionnaire, the AI Attitude Scale, and the General Self-Efficacy Scale. Statistical analyses included descriptive statistics and path analysis to assess direct, indirect, and total effects. Results: Most participants were female (50.3%), from rural areas (63.5%), and had not received formal AI training (89.5%). However, 91.2% reported using AI applications, with ChatGPT being the most common (77%). High levels of AI challenges (80.4%), AI attitude (67.5%), and self-efficacy (76.6%) were observed. Path analysis revealed a significant direct effect of AI attitude on self-efficacy (β = 0.343, p < 0.001), an indirect effect through AI challenges (β = 0.266, p < 0.001), and a total effect of β = 0.609 (p < 0.001). Conclusion: AI challenges partially mediate the relationship between students’ attitudes toward AI and their self-efficacy. Positive attitudes enhance self-efficacy, but overcoming AI-related barriers is essential to maximize this impact. Recommendation: Provide supportive guidance from faculty members how model effective AI use, address student concerns, and foster a positive learning environment.

Introduction For practicing teachers of students with visual impairments, assistive technology has assumed an important role in the education of their students’ assessment and learning of content. Little research has addressed this area; therefore, the purpose of the study presented here was to identify the teachers’ self-reported possession of knowledge of and skills in assistive technology. Method The participants completed an online survey rating their level of expertise in assistive technology. The researchers used descriptive statistics and Pearson r correlation coefficients. Results In total, 840 teachers of students with visual impairments in the United States, Palau, and the Virgin Islands completed an online survey to identify their perceived level of mastery of assistive technology competencies. Regarding the participants’ confidence in teaching and supporting the use of assistive technology for students with visual impairments, 40.7% of the participants were confident or very confident, whereas 59.3% reported no to some confidence. In addition, the younger teachers were relatively more confident in teaching assistive technology than were the older teachers. Specifically, the participants felt the least confident with the foundations domain of assistive technology and the most confident with collaboration. Discussion The results describe teachers’ self-perceptions and pinpoint areas for further intervention and dialogue. Personnel preparation programs and in-service training programs can concentrate on these specific areas of assistive technology to improve teachers’ levels of confidence with assistive technology domains. Collaborating to create systemic, national interventions is crucial for improving educational and vocational outcomes for all individuals with visual impairments. Implications for practitioners First, given the findings of the study, it is recommended that all university programs develop a course on assistive technology, as well as embed assistive technology competencies in their training curricula. Second, research is needed to explore further what universities are currently doing to address assistive technology in their curricula. Finally, professional organizations should provide ongoing in-service training in assistive technology for practicing teachers of students with visual impairments.

Assistive technology (AT) helps make the curriculum accessible to students with visual impairments. Studies have shown that half of these students are using assistive technologies. The purpose of this study was to seek a better understanding of the various factors related to assistive technology use by students with visual impairments in the Commonwealth of Kentucky. Through the use of the online survey provider, Survey Monkey™, an invitation to participate in the Assistive Technology Use by Students with Visual Impairments (ATSVI) survey was sent to a list of all TVIs teaching in Kentucky. Of 117 invited participants, 71% responded and 62% of the questionnaires met the criteria of inclusion. Demographic data were gathered on the TVIs, including years of experience, degrees obtained, caseload size, size and type of employing district (residential or non-residential) and the extent and areas of AT training. Teachers also provided data about their student AT use, including the extent of low and high-tech use according to the student's primary learning media. Additionally, TVIs provided AT funding source data. Significant correlation was not found between the size of employing district, years of teaching experience, level of education, specific areas of AT training and the extent of assistive technology use. Significant negative correlation was found between TVI caseload size and the extent of AT use. Significant positive correlation was found between the amount of overall AT training and the extent of AT use. Several conclusions were made from the study's results. To increase assistive technology use by students with visual impairments, 1) TVIs should be encouraged to seek more AT training and AT providers should consider developing more on-line training, 2) training should be developed in specific AT areas according to TVIs surveyed needs, 3) TVI caseload sizes need to be smaller, and 4) TVIs need to be familiar with the large array of funding sources available for AT.

Artificial Intelligence (AI) is revolutionizing reproductive medicine by enhancing fertility treatments, childbirth monitoring, and postnatal care. AI-driven technologies, including machine learning (ML), natural language processing (NLP), and robotic-assisted surgery, improve diagnostic accuracy, optimize treatment plans, and support clinical decision-making in assisted reproductive technology (ART). While AI has demonstrated significant potential, challenges such as ethical concerns, data privacy risks, algorithmic biases, and regulatory uncertainties remain critical barriers to widespread adoption in reproductive healthcare. This chapter explores the role, advancements, and challenges of AI in reproductive medicine, highlighting its impact on fertility, pregnancy, and postnatal care. A comprehensive analysis of AI applications in reproductive healthcare, including embryo selection, sperm and oocyte quality assessment, ovulation tracking, prenatal diagnostics, labor monitoring, and postpartum depression detection. AI aids in early detection of obstetric complications, optimizes neonatal care in intensive care units (NICUs), and enhances fertility treatments through predictive analytics. AI-driven virtual assistants support maternal health, while machine learning-based tools improve diagnostic precision and clinical decision-making. Addressing ethical and regulatory concerns is essential for equitable AI integration in reproductive medicine. Future advancements require interdisciplinary collaboration, standardized validation through randomized controlled trials, and responsible AI deployment to enhance patient outcomes while maintaining clinical expertise and ethical integrity.

Background Assistive Technology (AT) helps individuals with multiple sclerosis (MS) to overcome limitations and enhance their social engagement by providing support in areas that would otherwise be difficult or unachievable. Objective The aim of this study is to determine the level of AT usage among individuals with MS and to address various questions related to the sociological implications of (non)use of AT devices, (un)awareness, and logistical barriers that prevent the full utilization of AT devices in the daily activities. Method 121 participants with previously diagnosed MS (26 AT users, 95 non-users), aged 18–65, were surveyed using a modified The Rapid Assistive Technology Assessment tool (rATA) covering: demographics, needs, supply, financing, satisfaction, and recommendations. Results Totally 21.5% of MS individuals used AT, dominantly mobility devices. Insecurity prevents desirable usage of AT devices. Affordability is a factor relevant for the device ownership. Both MS individuals and society lacked sufficient AT informedness. Conclusion Based on the results and current guidelines, it’s essential to improve policies, supplying, AT device enhancements, and education to fully utilize AT’s potential in people with MS.

This paper revisits Vygotsky’s cultural-historical theory to address key epistemological challenges in artificial intelligence (AI), with a particular focus on the question of AI’s subject matter and research methodology. It critically engages with the long-standing debate between symbolic and connectionist approaches, examining AI’s epistemological underpinnings through the lens of Vygotsky’s insights. Until recently, the cultural-historical nature of human cognitive processes and the socio-historical embeddedness of AI technologies have remained largely unexplored areas in AI research, representing a terra incognita for the field. By addressing these neglected dimensions, this paper argues that Vygotsky’s theory provides valuable resources for rethinking AI’s epistemological and methodological foundations.

A holistic understanding of challenges faced by people with low vision

People with disabilities, specifically, those that are having a problem associated with sight or hearing, which falls under the categories of blind and deaf, require technological assistance to move freely. The technological area that is concerned with “Obstacle Detection and Feedback System” can be utilised to address the specific issues of dealing with the abovementioned problems. The growing demand for improved obstacle detection methods has pushed major developments in assistive technologies for people with visual impairments. The information needed and interrelationships among important criteria impacting the efficacy of obstacle detection systems are investigated in this work using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. Nine critical criteria, including Safety (SF), Accuracy of Detection (AD), Response Time (RT), Ease of Use (EU), Portability (PT), Battery Life (BL), Environmental Adaptability (EA), Cost (CT) and Durability (DD), are used. Data was collected from 21 expert participants through structured questionnaires, and the influence of each criterion was analysed using a total relation matrix. The most influential criteria identified were SF with a total influence score of 42.07, AD (41.08), RT (41.01) and Ease of EU (40.83). Factors such as BL (37.51) and CT (38.54) were found to be dependent on improvements in primary criteria. This study provides a structured decision-making framework to enhance obstacle detection systems by prioritising safety, real-time response and usability. The findings offer practical insights for developing AI-driven assistive technologies, ensuring improved accessibility, efficiency and integration into smart city environments.