This paper describes a corpus consisting of real-world dialogues in English between users and a task-oriented conversational agent, with interactions revolving around the description of finite state automata. The creation of this corpus is part of a larger research project aimed at developing tools for an easier access to educational content, especially in STEM fields, for users with visual impairments. The development of this corpus was precisely motivated by the aim of providing a useful resource to support the design of such tools. The core feature of this corpus is that its creation involved both sighted and visually impaired participants, thus allowing for a greater diversity of perspectives and giving the opportunity to identify possible differences in the way the two groups of participants interacted with the agent. The paper introduces this corpus, giving an account of the process that led to its creation, i.e. the methodology followed to obtain the data, the annotation scheme adopted, and the analysis of the results. Finally, the paper reports the results of a classification experiment on the annotated corpus, and an additional experiment to assess the annotation capabilities of three large language models, in view of a further expansion of the corpus.

People with sight impairments can access to a mathematical expression by using its LaTeX source. However, this mechanisms have several drawbacks: (1) it assumes the knowledge of the LaTeX, (2) it is slow, since LaTeX is verbose and (3) it is error-prone since LATEX is a typographical language. In this paper we study the design of a natural language generation system for producing a mathematical sentence, i.e. a natural language sentence expressing the semantics of a mathematical expression. Moreover, we describe the main results of a first human based evaluation experiment of the system for Italian language.