Massive Open Online Courses (MOOCs) have broadened educational access but often overlook the diverse needs of learners, especially those with disabilities. This paper introduces the RECMOOC4ALL conversational chatbot, a Generative AI-powered tool crafted to enhance personalization, engagement, and accessibility within online learning environments. Unlike general-purpose chatbots, it collects user preferences, accessibility needs, and interaction patterns to support other components of the RECMOOC4ALL system such as the recommendation system and accessibility checker in delivering inclusive learning experiences. Leveraging technologies such as Speech-to-Text (STT), Text-to-Speech (TTS), multilingual support, and advanced AI models like GPT-3 and Whisper, the system fosters adaptive learning experiences tailored to individual requirements. This paper highlights the transformative potential of Generative AI in mitigating accessibility challenges and outlines the system's technical architecture and capabilities. Preliminary findings regarding user satisfaction and interaction efficacy, detailed in subsequent sections, underscore the tool's potential to significantly improve engagement and learning outcomes for students with disabilities.

In the dynamically evolving of Massive Open Online Courses (MOOCs), the imperative for real-time and the efficient evaluation of learner engagement has never been more pronounced. Traditional methodologies, while providing foundational insights, often fall short in terms of objectivity and immediacy. This paper introduces an innovative system that uses advanced computer vision and machine learning algorithms to dynamically detect and analyze learner emotions and engagement levels during MOOC sessions. Additionally, this system facilitates the identification of areas for improvement and supports the design of personalized and engaging learning experiences, particularly for learners with disabilities. Our findings reveal that this system not only monitors the duration and intensity of learner engagement but also actively identifies moments of peak engagement and discerns learning patterns. This information enables the personalization of educational paths to suit individual learning styles, significantly enhancing engagement and overall MOOC effectiveness. Powered by affective computing, this technology seeks to make a difference in the field of education technology, transforming MOOCs into personalized learning environments that match the specific interests, goals and needs of each user.

Massive Open Online Courses (MOOCs) providers deliver courses to all students in a largely uniform manner, regardless of their individual needs. This ‘massiveness’ characteristic presents a number of challenges, most of which are connected to the lack of accessibility, inclusiveness, and personalization factors, particularly for learners with impairments and the elderly. Specifically, bridging the gap between learners' skills and individual variance owing to the lack of personalization and digital accessibility compliance, still remains burdensome. For this reason, incorporating accessibility and Universal Design for Learning (UDL) into online learning in general and MOOCs in particular might help establishing flexible learning experiences for all, taking into consideration learner variability and educational requirements by design. This paper sheds light on UDL and digital accessibility considerations within MOOCs and summarizes a literature review on MOOCs accessibility, paving the way to present our ongoing research aiming at enhancing MOOC accessibility harnessing artificial intelligence.

Information and Communication Technology ICT is increasingly becoming a cornerstone of both work and daily lives for many people. Everyone should then be able to access and use ICT at the same level as everyone else, including individuals with disabilities and the elderly. Even so, many forms of technologies and services are still not being designed with accessibility in mind. Several factors are yet hindering ICT accessibility including, among others, the scarcity of adequate ICT accessibility learning and training programs, as well as the lack of integration of ICT accessibility courses into curricula, which means that most of graduate students do not acquire knowledge and skills on how to make technology accessible. This paper presents a novel open ICT accessibility and inclusive design ICT-AID competency framework with a view to support individuals, and education and training institutions on delimiting the required relevant ICT accessibility competencies, and to fostering accordingly the integration of accessibility into curricula. The proposed framework, as a new standard featured on the open educational resources OER Commons platform, can also be availed for locating, aligning and sharing accessible OER serving globally learners and educators, toward closing the ICT accessibility training and knowledge gap.

This paper presents a systematic review intending to summarize the state of the art in the field of teachers' information and communication technology ICT competency assessment by identifying and analyzing existing tools and approaches for assessing teachers' ICT competency level. By conducting a systematic mapping study, a total of 11 papers has been selected and reviewed. Our results deliver some important findings focusing on the following key features: the objectives addressed by the assessment tools, the approaches used to assess teachers' ICT competencies, the ICT competency framework that the tool relies on, and the related technical environment. This review demonstrates that there is a lack in terms of assessing teachers' competencies required to mainstream effectively ICT in education. Designing and developing an assessment tool intended for identifying and measuring Teachers' ICT Competencies pursuant an ICT competency framework would be the cornerstone towards recommending the most appropriate materials according to the teacher training needs and requirements.

In this paper, we describe a fully automatic learner modeling approach in learning management systems, taking into account learners' educational preferences including learning styles. We propose a learner model with three components: the learner's profile, learner's knowledge, and learner's educational preferences. The learner's profile represents the learner's general information such as identification data, the learner's knowledge implies the learner's interests on visited learning objects, and the learner's educational preferences are composed of the learner's preferences among visited learning objects and his/her learning style. In the proposed approach, all learner model components are automatically detected, without requiring explicit feedback. Indeed, all the basic learners' information is inferred from the learners' online activities and usage data, based on web usage mining techniques and a literature-based approach for the automatic detection of learning styles in learning management systems. Once learner models are built, we apply a hierarchical multi-level model based collaborative filtering approach, in order to gather learners with similar preferences and interests in the same groups.

In order to automatically provide the most appropriate learning objects to e-learners, a special interest should be given to the process of building the learnerspsila models. First, we need to identify which relevant information to include in the learnerpsilas model, while taking into account various pedagogical considerations, and second how to accurately infer the learnerspsila preferences and characteristics from their online behavior and activities. This paper presents a preliminary study of the possibility of integrating educational preferences in the learnerpsilas model and detecting them automatically within e-learning systems.

The World Wide Web (WWW) is becoming one of the most preferred and widespread mediums of learning. Unfortunately, most of the current Web-based learning systems are still delivering the same educational resources in the same way to learners with different profiles. A number of past efforts have dealt with e-learning personalization, generally, relying on explicit information. In this paper, we aim to compute on-line automatic recommendations to an active learner based on his/her recent navigation history, as well as exploiting similarities and dissimilarities among user preferences and among the contents of the learning resources. First we start by mining learner profiles using Web usage mining techniques and content-based profiles using information retrieval techniques. Then, we use these profiles to compute relevant links to recommend for an active learner by applying a number of different recommendation strategies.