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Literature reviews on artificial intelligence (AI) have focused on the different applications of AI in higher education, the AI techniques used, and the benefits/risks of the use of AI. One of the greatest potentials of AI is to personalize higher education to the needs of students and offer timely feedback. This could benefit students with disabilities tremendously if their needs are also considered in the development of new AI educational technologies (EdTech). However, current reviews have failed to address the perspective of students with disabilities, which prompts ethical concerns. For instance, AI could treat people with disabilities as outliers in the data and end up discriminating against them. For that reason, this systematic literature review raises the following two questions: To what extent are ethical concerns considered in articles presenting AI applications assessing students (with disabilities) in higher education? What are the potential risks of using AI that assess students with disabilities in higher education? This scoping review highlights the lack of ethical reflection on AI technologies and an absence of discussion and inclusion of people with disabilities. Moreover, it identifies eight risks associated with the use of AI EdTech for students with disabilities. The review concludes with suggestions on how to mitigate these potential risks. Specifically, it advocates for increased attention to ethics within the field, the involvement of people with disabilities in research and development, as well as careful adoption of AI EdTech in higher education.

Many literature reviews on artificial intelligence (AI) in higher education or in education in general have focused on the different applications of AI in this domain, the AI techniques used, and the benefits/risks of the use of AI. One of the greatest potentials of AI is to personalise higher education to the needs of students and offer timely feedback. This could benefit students with disabilities tremendously if their needs are also considered in the development of new AI educational technologies (Edtech). However, current reviews fail to address the perspective of students with disabilities. This perspective is essential because AI is likely to bring several ethical concerns for people with disabilities. For instance, AI can treat people with disabilities as outliers in the data and end up discriminating against them. For that reason, two questions were raised: To what extent are ethical concerns relevant for students with disabilities considered in articles presenting AI applications assessing students in higher education? What are the potential risks of using AI that assess students with disabilities in higher education? This extended abstract presents summarised results of a scoping review that will be published in a journal. The goal of this article is to start a discussion within the AI ethics community to raise awareness about the issues that students with disabilities may face and to collaboratively explore solutions. Results suggest that there is a lack of ethical reflection on AI technologies and an absence of discussion and inclusion of people with disabilities. Moreover, risks associated with utilising AI for students with disabilities relate to the choice of data, reliance on simplistic classification, face monitoring, and the low involvement of students.

Automated chart analysis has vast potential to improve the accessibility of charts for a wider audience, e.g., people with visual impairments or other disabilities, by generating captions for chart images that can quickly convey the information being represented. Additionally, it can improve the performance of automatic document analysis systems, by enabling them to extract valuable information from the documents with graphical/visual scientific content. Although recent advancements in modality translation and multi-modal learning have led to the development of more or less successful image captioning and visual question-answering methods, but most of them have been designed for general images, and cannot be successfully applied to specific areas such as medical images or scientific charts and graphs. Therefore, further research is necessary to develop automated chart analysis methods that can be effectively applied to these specific areas. In this paper, a comprehensive review of chart analysis methods is presented. The review covers a wide range of chart types, including line charts, bar charts, scatter plots, and includes an in-depth analysis of each method. Additionally, this paper provides a more extensive coverage of chart analysis methods compared to previous studies, making it a valuable resource for researchers and practitioners in the field. Various techniques can be categorized from different aspects, such as chart type, model architecture, learning algorithm, visual feature space, and language modeling. In this paper, different methods are classified from a more technical viewpoint, by considering the approach used for modeling the problem. A taxonomy is proposed which divides the methods into three major categories: rule-based, chart captioning, and chart question-answering approaches. The rule-based approach uses the classical knowledge representation methods for reasoning, which has been diminished by the emergence of deep learning models. Chart capti

Loop closure is an inseparable part of any accurate and reliable visual simultaneous localization and mapping (SLAM) algorithm for autonomous vehicles and mobile robots. Loop closure potentially decreases the impact of the cumulative drift while generating the map of the traversed environment. In this paper, a heuristic similarity and location-based approach for loop closure in unexplored environments is introduced. The current SLAM implementation on average requires 0.295 seconds per frame from which only 0.0270 seconds are the runtime latencies of the similarity and location-based real-time loop closure (SNAPS), which includes trajectory correction. The proposed approach results in a 65% decrease in the mean deviation from the ground truth. In the conducted study, neither conventional bag-of-words models, nor computationally expensive deep neural networks have been used to detect and perform loop closure, which makes the proposed approach both interpretable and efficient. In fact, we propose a method which tries to find loop closure candidates based on the location and also an interpretable similarity score attained from the generated thumbnails of the read frames instead of the local descriptors. Additionally, the employed discount factor applied on the pose trajectory update rule guarantees a consistent and accurate map. Lastly, the KITTI dataset is used to demonstrate the efficiency and accuracy of SNAPS for SLAM.

This paper describes a new algorithm for the automatic detection and tagging of paragraphs in PDF documents. This is an important feature of the PDF Accessibility Validation Engine (PAVE), which is an open-source web application for the analysis and semi-automatic correction of accessibility issues in PDF documents. The tool is currently used by a large number of users, and their feedback is collected and evaluated. The evaluation so far revealed some major usability issues mainly due to the missing paragraph detection functionality. After an introduction in PDF accessibility this paper discusses the current usability issues with PAVE and describes the newly proposed algorithm to alleviate them. A first evaluation and conclusion of the results will be provided in the final paper.

Collecting accurate accessibility data systematically for pathways is a time-consuming task that typically requires expert knowledge. However, it is a prerequisite to enable reliable and trustworthy accessible routing. The development of Capture & Go, a mobile application to report barriers for people with mobility impairments, facilitates the on-site collection of crowdsourced accessibility data. Several other mapping tools contain accessibility data, although they have not been developed explicitly for this purpose. In contrast to Capture & Go, they allow data collection to be performed remotely. Using quantitative and qualitative approaches, we analyzed several such applications and examined their efficiency in capturing barriers in a case study of District 1 in Zurich. The remotely collected data was compared to the data of the barriers captured on-site using Capture & Go. Overall, the remote tools were less efficient than Capture & Go in terms of effort, coverage, and accuracy of the barriers, as well as usability.