With the rapid advancements in artificial intelligence (AI) and large language models (LLMs), their integration into higher education is becoming increasingly relevant. This systematic review aims to analyze the current applications of AI and LLMs in the training of psychologists and psychotherapists, focusing on their benefits, challenges, and existing research gaps. By synthesizing findings from relevant academic databases, this review seeks to provide a comprehensive overview of how AI-driven technologie...
Additionally, this review aligns with the **Special Thematic Session (STS) on Artificial Intelligence in Higher Education: Advancing Inclusive Environments, Pedagogical Approaches, and Assistive Technologies** by emphasizing AI’s role in fostering inclusive learning environments and enhancing pedagogical methods. AI-powered assistive technologies, such as adaptive learning systems and virtual patient simulations, play a crucial role in expanding accessibility and creating more equitable learning opportunities...

The paper discusses both artificial intelligence (AI)’s potential in assistive technology and to improve accessibility and its use in control and surveillance and the disadvantages to disabled people from the bias in AI algorithms. This makes the involvement of disabled people in AI research particularly important. The study presented here is based on a literature overview, as page length prevented a full literature review. There is some literature on equity, diversity and inclusion in AI, with gender the main characteristic investigated and very limited reference to disability, and a few papers on AI co-design research with non-academic disabled researchers. Several approaches to designing databases to avoid digital discrimination against disabled people were also identified. The work highlights the importance of involving disabled researchers in assistive technology, but unfortunately does not suggest mechanisms for achieving this and overcoming any barriers to their participation.

Artificial intelligence (AI) is transforming educational publishing, particularly in improving accessibility through the generation of alternative text (alt text) for images. Alt text is crucial for visually impaired users to access digital content (AbilityNet, 2021). The European Accessibility Act (EAA) mandates that all digital publications include alt text by June 2025, highlighting its significance for inclusive education (European Parliament, 2019). Although AI-generated alt text has been explored for certain image types, research on its use for diverse educational content like diagrams, illustrations, and multimedia remains limited. This study, in collaboration with Zanichelli and the University of Rome “Tor Vergata,” evaluates AI models like Chat GPT-4, GPT-4S, GPT-4V, GPT-4o, Gemini, and Claude. Findings reveal both strengths and weaknesses, stressing the importance of precise prompting, model refinement, and human oversight to ensure reliable outcomes. Integrating AI into editorial workflows holds promise for creating accessible educational materials that empower learners.

This study investigates the potential of Large Multimodal Models (LMMs) in generating educational materials tailored to students with learning disorders, specifically dyslexia and dyscalculia, in higher education mathematics courses. By analyzing outputs from ChatGPT o1, Llama 3.1, Phi4, DeepSeek r1, and Claude Sonnet 3.5, this research evaluates the effectiveness of AI-generated learning resources in maintaining both mathematical correctness and pedagogical usability. Using a teacher-centered prompting approach, we request model-generated adaptations for problem-solving lessons on limits and derivatives. Expert evaluators assess the outputs through a Likert scale to determine their alignment with inclusive teaching strategies. The findings provide insights into the strengths and limitations of LMMs in fostering accessible and pedagogically sound mathematics education while emphasizing the need for AI-human collaboration in instructional design.

The rapid advancements in Large Multimodal Models (LMMs) and Generative AI (GenAI) have opened new possibilities for enhancing accessibility in higher education, particularly in STEM disciplines. However, current AI applications in education often prioritize direct problem-solving over fostering deep learning experiences, potentially bypassing essential cognitive struggles that are critical to mastering complex concepts. This study proposes a pedagogical framework for leveraging LMMs to support inclusive learning without replacing the cognitive engagement necessary for students' conceptual development. The research is structured around a two-fold approach, where three interdisciplinary working groups—special pedagogy, mathematics, and engineering—collaborate to design and test an AI-driven methodology that supports both professors in creating accessible lectures and students in developing customized learning pathways. The framework will be evaluated through two pilot studies, focusing on mathematics and electronics university courses, to assess its impact on both teaching practices and student learning experiences. By integrating AI-driven adaptations with evidence-based pedagogical strategies, the project aims to strike a balance between leveraging AI for accessibility and preserving the cognitive challenges necessary for deep learning in STEM disciplines.