NIM : Neuro-symbolic Ideographic Metalanguage for Inclusive Communication (2025)

Abstract: Digital communication has become the cornerstone of modern interaction, enabling rapid, accessible, and interactive exchanges. However, individuals with lower academic literacy often face significant barriers, exacerbating the “digital divide.” In this work, we introduce a novel, universal ideographic metalanguage designed as an innovative communication framework that transcends academic, linguistic, and cultural boundaries. Our approach leverages principles of Neuro-symbolic AI, combining neural-based large language models (LLMs) enriched with world knowledge and symbolic knowledge heuristics grounded in the linguistic theory of Natural Semantic Metalanguage (NSM). This enables the semantic decomposition of complex ideas into simpler, atomic concepts. Adopting a human-centric, collaborative methodology, we engaged over 200 semi-literate participants in defining the problem, selecting ideographs, and validating the system. With over 80% semantic comprehensibility, an accessible learning curve, and universal adaptability, our system effectively serves underprivileged populations with limited formal education.

## introduction communication, in its many forms, has always been the bedrock of human progress, shaping societies and driving innovation. in the modern interconnected world, digital communication has emerged as a pivotal force, revolutionizing how we communicate. we propose an ideographic communication metalanguage for semi-literates, promoting socio-digital inclusion, and a more interconnected society. we define semi-literates as individuals with limited formal education typically only up to the primary or early secondary level who face significant challenges in navigating dig-ital communication platforms due to constrained reading, writing, and digital literacy skills. visual communication is intuitive, easy to comprehend, language agnostic, containing spatial and directional attributes @xcite @xcite . while this can help in the facilitation of human-computer communication specially for people with limited academic education, however having a set of visual ideographs only lacks morphology and syntax, which is ingrained in orthographic forms of communication thus making pure visual ideographic set prone to misinterpretation. earlier studies on the use of visual communication for academically challenged individuals observe challenges with learnability (due to large inventory of ideographs), extensibility (non-universal design) along with ambiguity (lack of grammar) @xcite . figure 1 presents the conceptual foundation of our work, embodying the neuro-symbolic ai paradigm through a synthesis of symbolic reasoning and neural parametric learning. by augmenting and combining the strength of large language models (llms) enabling generation of new concepts, along with structured ontology as guiding principles for semantic decomposition of complex ideas into simpler and atomic concepts, we design a novel ideographic metalanguage for semi-literates. the pipeline goes through processing of input sentence s in into distinct parts of picturable (w p ) and textual (non picturable, w t ) sections. while w p gets broken down into ideographic elementary components (i e ) which comprises of semantic classes, templates, variables and molecules (sc/st /(sv, sm)), the non picturable sections are preserved as plain text coming together in an interactive user interface customized for semi-literate users. consequently, we call our method as nim (neuro-symbolic ideographic metalanguage). nim leverages the linguistic theory of natural semantic metalanguage (nsm) @xcite for symbolic reasoning module achieving semantic simplification of complex concepts into more elementary elements. this symbolic reasoning is integrated with generative artificial intelligence (gen-ai) and construct the design of a visual metalanguage (including bare-minimum text, we refer as binding text) for effective communication in semi-literate settings. we further enhance our work by making the binding text multilingual thereby making it universal for use. we adopt a collaborative human-centered design (hcd) methodology that actively involves semi-literate individuals across the development cycle, facilitating authentic problem discovery, participatory design, and rigorous empirical validation. the validation spans multiple dimensions, including comprehensibility, user engagement, ideographic effectiveness, and extensibility. beyond semi-literate populations, our approach has potential applicability to diverse user groups such as individuals with intellectual disabilities, multilingual teams, and children with dyslexia. in summary our contribution, is three-fold, (i) we build a novel metalanguage for semi-literates, enabling them for digital communication; (ii) our work facilitates seamless integration with digital ecosystem, for easy and intuitive human-computer interaction; and (iii) our work is universal, extensible for multiple domains, geographies and cultures. it can be argued that the speech-based systems and vision-language models (vlms) offer promis-ing modalities for accessibility and can be used to solve the problem of digital communication for semi-literate groups. however, despite their promise, these techniques exhibit important gaps that limit their effectiveness in the real-world, semiliterate contexts. the modality of speech is promising for accessibility, however for communication across communities with significant linguistic diversity (no common standard spoken language), sending voice messages does not help. for instance, in india, where languages and dialects change every few hundred kilometers, voice-based communication cannot reliably support broader accessibility or facilitate interaction across linguistically diverse communities. similarly vlms can possibly generate intermediate images, but it has been observed in earlier "text to image" systems that the choice of the icon for a particular situation/entity is not consistent and the number of icons and their interplay is also non deterministic. vlms typically produce monolithic visual outputs rather than structured representations. this lack of hierarchical organization limits opportunities for contextual disambiguation and, consequently, hampers the comprehensibility of the final output. ## problem discovery to the best of our knowledge, no prior work directly aligns with our approach of combining minimal textual elements for syntax with symbolic representations as the primary carriers of semantics. existing studies largely fall into three categories: purely symbolic systems, text-based approaches, or the use of emoticons and emojis, which are typically employed as paralinguistic cues (e.g., to signal emotions or attitudes). consequently, there is no directly comparable baseline in the literature, and we therefore adopt a first-principles approach to problem formulation, system design, and solution development. ## system design as illustrated in figure 1 , we use neuro-symbolic ai architecture having two separate components for defining guiding principles (symbolic structure) and pattern processing abilities (neural structure). ## implementation we conduct our experiment using python 3.10.1, pyqt 5 5.15.1, bert, gpt 3.5 turbo 16k and langchain @xcite . ## results and analysis to ensure a robust real-world validation of nim, we collaborate with a separate cohort of 200 participants, distinct from those involved in the design process to minimize bias and provided compensation for their participation. the testing procedure spawned over a period of one week where each day the participants interpret a set of sentences, and we survey their response on multiple parameters. there is actually no similar work which combines bare minimum elementary text (for syntax) with symbols doing heavy lifting for semantics. most existing work are either all symbols or text or emoticons (for emotional emphasis). some latest vlm work is on the other side very non deterministic and does not contain to the boundaries of culture and semantic hierarchy. our evaluation approach broadly encompasses three dimensions: cognitive accessibility and user engagement, assessing comprehensibility, learnability, and user appeal; semantic robustness, evaluating ideograph effectiveness, generalization to out of vocabulary concepts, and the adequacy of concept inventory; design rationale, using ablation studies to validate the contribution of individual components. these dimensions provide critical insights into the system's capabilities, detailed in the following sections.