## 1 Introduction  
The correct use of grammar is generally well understood intuitively and applied correctly by speakers in their native language. At the age of 18-24 months, when a child is able to use its first 50 words, it begins to recognize the grammar in spoken sentences and starts a productive grammatical development [1]. The acquisition of the case system continues beyond the first years of primary school [2]. It is, therefore essential, from the very beginning of schooling, that lessons and textbooks teach the grammatical rules on which a language is based and structured. In conversations, sloppy and sometimes incorrect grammar is often tolerated or not even noticed. Dialects also sometimes use grammar that can be flawed or quite specific. Nevertheless, in written communication, the grammar should be correct. Grammatically correct writing is essential in professional life, which requires the correct use of grammar in all written communications. It is evident that the correct use of grammar, in general, is a challenge for everyone and requires a long learning process. It is entirely expected that grammar development proceeds differently for children with learning disabilities [3]. Children of this group have particular difficulties in acquiring the grammatical rules of a language or developing the reading skills necessary to understand the semantics of sentences, which also affects their lives when they grow up. In order to reduce these barriers, a comprehensible standard for language was established, the so-called \"Easy Language\", which uses solely simple grammatical constructions and only the basic vocabulary of the language. There is also a less formal standard for the learning of foreign languages, the so-called \"Simple Language\", which is closer to native written language but recommends a simpler sentence structure. Nowadays, it is common to use digital applications to learn a second language or to improve writing abilities in daily work [4; 5]. Also, grammar correction software is widely used in academia and is recommended for improving students' writing skills [6]. Artificial intelligence (AI) is now a part of everyday life and science and impacts the use of language and human language skills. It is possible to use AI to analyze language, translate texts, and provide instant feedback for writers [7]. Given the latest developments, GPT [8], which is based on neural networks, has gained tremendous popularity in linguistics for formulating sentences and writing entire texts. However, large language models (LLMs) [9], such as GPT, are generally statistical models of language, and their answers are always statistically likely but not necessarily correct. Rule-based systems grounded in logic ensure complete correctness but are constrained by the inherent ambiguities of natural language. Consequently, state-of-the-art parsers, such as the dependency parser from SpaCy [10] and the constituent parser from Stanford [11], incorporate statistical components to address these ambiguities. The SpaCy and Stanford parsers are based on the Robinson Axioms [12], which result in specific tree structures. However, these trees do not capture the grammatical information required for our grammar analysis. In the present paper, we introduce a new rule-based parser. The theoretical foundation and principles underlying the algorithm employed in our approach were detailed in an earlier publication by [PERSON] et al. 2023 [13]. Our parser employs bubbles, a concept introduced in a prior paper, which group multiple words into a grammatical unit, such as noun phrases or clauses. This approach yields parse structures that provide richer grammatical information. Moreover, our parser aligns with the language grammar typically taught in educational settings, making our approach particularly valuable for instructional purposes. It tackles ambiguity through a brute-force approach, systematically analyzing all possible combinations of ambiguities while discarding implausible ones.