Selecting reading materials that are appropriate for adults with low literacy skills remains a central challenge in Adult Learning contexts. This challenge becomes particularly acute when the unit of analysis is not a short passage but a full book or long-form text, where internal heterogeneity in lexical, syntactic, and discourse-level properties make global readability estimation non-trivial. In practice, librarians, educators, and publishers often need to make decisions about the suitability of books for specific learner populations without access to complete texts, relying instead on partial excerpts or limited samples, and personal intuition.

This paper presents a preliminary study on proficiency-controlled text simplification in European Portuguese using multiple prompting strategies. We focus on the iRead4Skills dataset, which defines four complexity levels targeted at adult native speakers with low literacy. Specifically, we simplify 40 texts from the highest complexity level into three easier levels (plain, easy, and very easy), corresponding approximately to Common European Framework of Reference for Languages (CEFR) levels B1, A2, and A1. We evaluate zero-shot and few-shot prompting configurations, exploring the impact of CEFR anchoring, explicit meaning-preservation instructions, and example-based guidance. Automatic evaluation relies on a fine-tuned proficiency classifier and semantic similarity metrics, including BERTScore and document embeddings. The results show that while exact target-level accuracy remains below 40%, target-or-below accuracy reaches up to 61.39%, indicating that the model generally simplifies texts but struggles to consistently match precise proficiency targets. Human evaluation confirms the overall trends observed automatically, while highlighting the subjectivity inherent to proficiency assessment and meaning preservation. Our findings suggest that prompt engineering alone is insufficient for robust proficiency control in European Portuguese, motivating future work on model adaptation and improved evaluation protocols.

This demo showcases a web-based interface that provides open, interactive access to a large-scale grammatical database of European Portuguese verbal constructions. Through a unified search and exploration environment, users can query, inspect, and compare more than 7,000 distributionally free verbal constructions and over 2,700 verbal idioms (frozen constructions), grounded in long-standing Lexicon–Grammar descriptions. For each construction, the interface exposes core linguistic properties such as argument structure, distributional constraints, semantic roles, major syntactic transformations, and curated usage examples with English translations. The demo illustrates how detailed, manually validated grammatical knowledge can be explored dynamically via the web, supporting linguistic research, language teaching, and NLP development. To the best of our knowledge, this is the largest publicly accessible, web-based grammatical resource dedicated to European Portuguese verbal constructions.

Adult Learning (AL) programmes need short, trustworthy texts that match learners’ reading abilities, but educators rarely have time, tools, or evidence-based guidelines to select and adapt materials consistently.We present a live demo of iRead4Skills for European Portuguese: a web-based system that (i) estimates readability/complexity for AL-oriented levels aligned with CEFR, (ii) highlights where complexity concentrates (lexical, grammatical, semantic), and (iii) supports rewriting by offering actionable, level-aware suggestions and curated lexical resources.The demo emphasises transparency and “trainer-first” workflows: users see why a text is complex and how to revise it without losing meaning.

Orthographic neighbors (ONs) play a central role in models of visual word recognition and have been shown to influence reading speed, lexical access, and literacy development. Despite their importance, resources providing detailed and flexible ON information remain scarce for European Portuguese. This paper introduces Portho, a corpus-based lexical resource that provides multiple ON metrics for over 43,000 word forms, using several ON definitions. In addition to classical neighborhood size measures, Portho provides frequency-based statistics and graded orthographic distance (OD) features. We analyze the statistical properties of the resource and evaluate its empirical utility in automatic text complexity assessment using the iRead4Skills corpus. Results show that while ON features alone are insufficient to predict readability, they contribute complementary information and compare favorably with existing resources for Portuguese. Portho is made publicly available in different formats to support research in psycholinguistics, readability modeling, and Natural Language Processing (NLP) for Portuguese.

This paper presents the construction of VIDiom-PT, a corpus in European Portuguese annotated for verbal idioms (e.g. O Rui bateu a bota, lit.: Rui hit the boot ‘Rui died’). This linguistic resource aims to support the development of systems capable of processing such constructions in this language variety. To assist in the annotation effort, two tools were built. The first allows for the detection of possible instances of verbal idioms in texts, while the second provides a graphical interface for annotating them. This effort culminated in the annotation of a total of 5,178 instances of 747 different verbal idioms in more than 200,000 sentences in European Portuguese. A highly reliable inter-annotator agreement was achieved, using Krippendorff’s alpha for nominal data (0.869) with 5% of the data independently annotated by 3 experts. Part of the annotated corpus is also made publicly available.

We present the iRead4Skills Intelligent Complexity Analyzer, an open-access platform specifically designed to assist educators and content developers in addressing the needs of low-literacy adults by analyzing and diagnosing text complexity. This multilingual system integrates a range of Natural Language Processing (NLP) components to assess input texts along multiple levels of granularity and linguistic dimensions in Portuguese, Spanish, and French. It assigns four tailored difficulty levels using state-of-the-art models, and introduces four diagnostic yardsticks—textual structure, lexicon, syntax, and semantics—offering users actionable feedback on specific dimensions of textual complexity. Each component of the system is supported by experiments comparing alternative models on manually annotated data.