Conversational agents are increasingly used in education, yet most educational chatbots remain content-centric and rely on learner models that represent knowledge states while leaving the learning process itself implicit. This paper introduces a process-oriented learner model that embeds principles from Agile methodology directly into the internal state of an AI-driven educational chatbot to support students' independent learning. In this model, learning objectives are represented as epics, study cycles as competence-driven sprints, and micro-level goals as user stories governed by explicit definitions of done. We implement the approach in a multi-agent Telegram chatbot used in an introductory university programming course. An exploratory feasibility study with 16 university students and recent graduates (N=16) provides preliminary evidence of the approach's pedagogical promise. Participants reported that the Agile-structured interaction improved goal clarity, step-by-step progressio n, and reflective engagement, though technical limitations in response latency and explanation depth were identified. This paper shows the feasibility of embedding explicit process models within conversational AI for education and motivates further investigation with larger samples and controlled evaluations.

Artificial intelligence promises to revolutionize our life, bringing significant advances in any fields: health, education, work and leisure time. This paper analyzes the last 5-year literature concerning the use of AI for supporting people with disabilities in education. The aim is to investigate the current state of art of accessible applications in the STEM (Science, Technology, Engineering, Mathematics) field and understand if contents and tools are accessible for all, regardless of personal need and abilities. Personalization and adaptation emerge as fundamental factors when designing for people with disabilities. Privacy and ethics aspects often neglected are very relevant. The analysis suggests that the STEM field still suffers from accessibility gaps, and current tools need to evolve and be increased to be exploited by different disabilities and ensure the same opportunities for every student, engaging, motivating, and empowering them..

While access to quality health services is a basic human right, it is not guaranteed for everyone, especially for people with neurodevelopmental disorder such as Autism Spectrum Disorder (ASD). For people with ASD, emotions can play an important role in the success of therapy. Severe anxiety before and during visits to specialists often leads to behavioral problems and poor cooperation, which can make treatment impossible. Using digital tools to support people with ASD in their daily lives can help manage anxiety and stress when accessing healthcare paths. However, the adaptation of these tools to ASD is still very rare. In this context, within the framework of the ACCESS project, we aim at developing a technological solution for personalized access to healthcare for individuals with ASD (including users of any age), and to demonstrate the developed methodology in the use case of the specialised Ear, Nose, and Throat (ENT) treatments for people with ASD. The main challenges of this study is the development of a system that can support people with ASD through customizable features tailored to their needs. We will conduct two experimental studies to validate the effectiveness and usability of our solution.

Alternative descriptions of digital images have always been an accessibility issue for screen reader users. Over time, numerous guidelines have been proposed in the literature, but the problem still exists. Recently, artificial intelligence (AI) has been introduced in digital applications to support visually impaired people in getting information about the world around them. In this way, such applications become a digital assistant for people with visual impairments. Increasingly, generative AI is being exploited to create accessible content for visually impaired people. In the education field, image description can play a crucial role in understanding even scientific content. For this reason, alternative descriptions should be accurate and educational-oriented. In this work, we investigate whether existing AI-based tools on the market are mature for describing images related to scientific content. Five AI-based tools were used to test the generated descriptions of four STEM images chosen for this preliminary study. Results indicate that answers are prompt and context dependent, and this technology can certainly support blind people in everyday tasks; but for STEM educational content more effort is required for delivering accessible and effective descriptions, supporting students in satisfying and accurate image exploration.

The paper presents a comparative mixed methods study of the accessibility and usability for disabled people of four video conferencing tools, Zoom, MS Teams, Google Meet and Skype. Useable responses were obtained from 81 disabled people with diverse characteristics, mainly in the UK, though some groups had low representation. None of the tools was considered fully accessible and useable. Zoom was both the most commonly used and the most frequently preferred (56.1%) tool, with MS Teams second in usage and a trailing second in preferences (15.9%). It was considered to have better captioning, but otherwise to generally be a poor second to Zoom. Skype was the most commonly used before lockdown, but was considered dated and its limited use was mainly social, whereas the other tools were also used in work and education. The results were used to draw up separate lists of recommendations for developers and meeting organisers and hosts, as the study also identified actions for organisers and hosts to improve meeting accessibility. Developer recommendations include several easy to set customisation and user friendly interface features, involving disabled people and specific accessibility features, including compatibility with assistive technology, keyboard shortcuts for all functions and automatically-on high quality captions.

Abstract Breast cancer (BC) is known as the most prevalent form of cancer among women. Recent research has demonstrated the potential of Machine Learning (ML) techniques in predicting the five-year BC risk using personal health data. Support Vector Machine (SVM), Random Forest, K-NN (K-Nearest Neighbour), Naive Bayes, Neural Network, Decision Tree (DT), Logistic Regression (LR), Discriminant Analysis, and their variants are commonly employed in ML for BC analysis. This study investigates the factors influencing the performance of ML techniques in the domain of BC prevention, with a focus on dataset size and feature selection. The study's goal is to examine the effect of dataset cardinality, feature selection, and model selection on analytical performance in terms of Accuracy and Area Under the Curve (AUC). To this aim, 3917 papers were automatically selected from Scopus and PubMed, considering all publications from the previous 5 years, and, after inclusion and exclusion criteria, 54 articles were selected for the analysis. Our findings highlight how a good cardinality of the dataset and effective feature selection have a higher impact on the model's performance than the selected model, as corroborated by one of the studies, which gets extremely good results with all of the models employed.

Oral health care can be a challenging experience for children with autism, for their parents and for dentists. Recently, some technology-enhanced systems have been proposed to help people with autism to cope with distressing situations originated by unknown social-life contexts, such as dental care settings with intense sound-visual stimulations. Results were positive in mitigating anxiety at the dental clinic but seem to fail in supporting proper oral hygiene at home. Thanks to the increasing spread of household Intelligent Personal Assistants (IPA) and Vocal Conversational Agents (VCAs), we envisage new opportunities considering the Voice-enabled IPAs not only as support on daily activities but also to enrich and simplify access to healthcare procedures from home. This work attempts to extend the use of technology-enhanced systems for dental care by exploiting the potential of the Vocal User Interface, Amazon Alexa, as an instructional agent with children on the spectrum. To this purpose, we developed a personalized Alexa Skill with two different functionalities: (i) support the child during the routine transition toward the target activity: move to the bathroom to brush their teeth; (ii) act as a persuader and a timer to guide the child during the procedure observing the proper brushing time. We conducted a three-week preliminary study with three children of different autistic profiles. The goal was to collect opportunities and issues deriving from the device introduction in the home context and test the device usage to favour dental care. Results and feedback were encouraging and gave insights to improve this approach.

Panic disorder (PD) is an anxiety disorder that in recent years has spread worldwide. PD is diagnosed when a person has recurring panic attacks, characterized by physical symptoms and disturbing thoughts and feelings that arise rapidly, reach their peak in a few minutes and soon disappear. Panic attacks, despite being harmless and relatively short, are highly distressing and deeply affect the lives of patients, who very often develop agoraphobia, an anxiety disorder that leads to systematic avoidance of places where previous attacks have occurred. PD is often a chronic condition that does not respond well to pharmacological treatment. However, psychotherapeutic approaches such as mindfulness have proved to be quite effective and their delivery through self-care eHealth tools has been encouraged by the World Health Organization.

This work aims to promote musical learning in order to ease the inclusion of students with autism in the classroom and during musical performances. To this end, a web application was developed using a user-centered design approach involving professionals experienced in training people with autism, to help end users learn the basic concepts of music. The app allows teachers or caregivers to set up a customized learning environment according to each student's needs, so that these students can play songs in collaboration with classmates. Preliminary results suggest a key role for technology in facilitating music teaching and in supporting the learning process for students with autism.

Home Automation Systems (HAS) -also referred to as smart homesexploit multiple components such as sensors, RFID readers, wireless devices, and remote control systems to enable easy interaction with smart appliances and devices, and to automate performing sequences of tasks to make human-device interaction simpler and life more comfortable.For people with vision impairment, especially those who are unable to see at all, smart homes can be a powerful tool for enhancing personal autonomy, provided that the system offers suitable device integration and accessible interfaces with a simple interaction via keyboard, assistive technology and other modalities such as voice and gestures.This paper investigates the accessibility of web interfaces when interacting with HAS components via screen reader assistive technology, in order to propose potential suggestions to developers.

Last-decade great advances in technology have contributed to make home smarter and more comfortable, especially for people with disabilities. A lot of low cost solutions are available on the market, which can be controlled remotely by a Home Automation System (HAS). Unfortunately, the user interfaces are usually designed to be visually oriented which can exclude some user categories, like those who are blind. This paper focuses on the design of usable Web user interfaces for Home Automation Systems, with a special attention to the functions as well as the interface arrangement in order to enhance the interaction via screen reader. The proposed indications could inspire other designers to make the user experience more satisfying and effective for people who interact via screen reader.

Dental health in children with autism presents many challenges, due to their different perception of sensory experience and difficulty accepting unknown social contexts. The dental care setting presents strong sound-visual stimulations that can upset a patient with autism, often forcing dentists to administer chemical sedation in order to deliver dental care. In recent years, several technology-enhanced systems and apps have been proposed to help people with autism adapt to new contexts and cope with distressing social situations. Our study explores the potential of personalized digital tools for familiarizing these children with dental procedures and environments, and teaching them how to perform proper oral hygiene at home. A 3-month study to test ICT tools created to control children's anxiety and avoid sedation was carried out involving researchers, developers, dentists, psychologists, parents and ten children with autism observed under natural conditions during their first dental care cycle. The results appear to confirm the potential of personalized technology to reduce anxiety in professional settings, increasing children's wellbeing and safety and encouraging oral hygiene as part of their daily routine.

This study investigates the use of social networks as a scientific tool for gathering medical data from young subjects while promoting healthier habits. Our first hypothesis is that social networks can facilitate epidemiological studies, reducing time and cost. The second question is whether social networks can enable the collection of data from young and healthy subjects who are otherwise beyond the reach of conventional social health polls. A Facebook application was created to collect data concerning adherence to the Mediterranean diet, considering the significant risk of cardiovascular and neurological degenerative diseases in subjects with poor adherence to a healthy diet. More than 1400 users were recruited in a short time without any promotional action. Collected data indicate that adherence to the Mediterranean diet is in general greater in older users vs young (p <0.01) and Italian vs Other Countries (mainly participants from the US) (p <0.03), while no statistical differences were found concerning gender. Results show that the proposed approach offers advantages in terms of reduced cost, faster data gathering and processing, and improved efficiency compared to a form-based epidemiology campaign. However, the initial network may influence the sample constitution in age and geographical location, especially if the spread does not become viral and autonomous. Based on the case study, we provide designers of Facebook apps with some simple guideline suggestions aimed at maximizing the heterogeneity of the sample, in order to collect significant data. The proposed scenario, suitable for collecting health data, can easily be extended to other fields.

In this report, we share our experience and observations on the challenges blind people face with text entry on touch-based mobile phones, particularly from the perspective of one of the authors, who is blind. To better understand these issues we developed and tested Multimodal Text Input Touchscreen Keypad (MTITK), an audio-tactile text entry prototype based on multitap, which relies on a telephone keypad layout organized into five key groups with distinct audio-tactile feedback. Users explore the screen to identify the current selected key, tap to enter text, and gesture to edit it, while receiving the corresponding voice, audio, and tactile feedback; no additional equipment is necessary in our software-only approach. We implemented a prototype on Android and tested its usability with visually impaired participants; they welcomed its multimodality and the familiar layout, but also expressed the need to increase vibration pattern differentiation and refine the character selection mechanism.

Social interaction is important for everyone, and vital for the differently-abled and elderly. Social network applications are causing us to change our ways of communicating and working. We can now make friends all over the world, share ideas and knowledge, search for others' opinions, and more. Facebook and Twitter are examples of these applications. However, these opportunities are usually meant for people who can walk, see, and talk without difficulty. In this paper we show how blind persons, using screen readers and voice synthesizers to access the Web, can interact with social network applications. Our case study focuses on Facebook, analyzing basic features, explaining electronic barriers and suggesting solutions.

Audio podcasting is increasingly present in the educational field and is especially appreciated as an ubiquitous/pervasive tool ("anywhere, anytime, at any pace") for acquiring or expanding knowledge. We designed and implemented a Web-based Text To Speech (TTS) system for automatic generation of a set of structured audio podcasts from a single text document. The system receives a document in input (doc, rtf, or txt), and in output provides a set of audio files that reflect the document's internal structure (one mp3 file for each document section), ready to be downloaded on portable mp3 players. Structured audio files are useful for everyone but are especially appreciated by blind users, who must explore content audially. Fully accessible for the blind, our system offers WAI-ARIA-based Web interfaces for easy navigation and interaction via screen reader and voice synthesizer, and produces a set of accessible audio files for Rockbox mp3 players (mp3 and talk files), allowing blind users to also listen to naturally spoken file names (instead of their spelled-out strings).

In this study we present a SW prototype developed within the framework of a research project aiming at improving the usability of search engines for blind users who interact via screen reader and voice synthesizer. Following the eight specific guidelines we proposed for simplifying interaction with search engines using assistive technology, we redesigned Google user interfaces (i.e. simple search and result pages) by using XSL Transformations, Google APIs and PERL technologies. A remote test with 12 totally blind users was carried out in order to evaluate the proposed prototype. Collected results highlight ways in which Google interfaces could be modified in order to improve usability for the blind. In our demo we will show how interaction with the modified Google UIs is simplified and how the time for reaching the most important elements (i.e. first query result, next result page, etc.) is shortened in comparison to interaction with the original Google UIs. The demo uses the JAWS screen reader for announcing the UI contents.

We present the results of a research project focus on improving the usability of web search tools for blind users who interact via screen reader and voice synthesizer. In the first stage of our study, we proposed eight specific guidelines for simplifying this interaction with search engines. Next, we evaluated these criteria by applying them to Google UIs, re-implementing the simple search and the result page. Finally, we prepared the environment for a remote test with 12 totally blind users. The results highlight how Google interfaces could be improved in order to simplify interaction for the blind.