The Future Internet aims to revolutionize digital interaction by integrating advanced technologies like AI and IoT, enabling a dynamic and resilient network. It envisions emotionally intelligent systems that can interpret and respond to human feelings, creating immersive, empathy-driven learning experiences. This evolution aspires to form a responsive digital ecosystem that seamlessly connects technology and human emotion. This paper presents a computational model aimed at enhancing the emotional aspect of learning experiences within museum environments. The model is designed to represent and manage affective and emotional feedback, with a focus on how emotions can significantly impact the learning process in a museum context. The proposed model seeks to identify and quantify emotions during a visitor’s engagement with museum exhibits. To achieve this goal, we primarily explored the following: (i) methods and techniques for assessing and recognizing emotional responses in museum visitors, (ii) feedback management strategies based on the detection of visitors’ emotional states. Then, the methodology was tested on 1000 cases via specific questionnaire forms, along with the presentation of images and short videos, and the results of data analysis are reported. The findings contribute toward establishing a comprehensive methodology for the identification and quantification of the emotional state of museum visitors.

The digital transformation in the treatment of mental health and emotional disharmony requires artificial intelligence architectures that overcome the limitations of purely neural approaches, such as temporal inconsistency, opacity, and lack of theoretical foundations. Assuming the existence and use of generalist LLMs currently used in clinical settings and considering the appropriate limitations indicated by experts, this article aims to offer clinicians an alternative Neuro-symbolic-Psychological multi-agent architecture (NSPA-AI), which integrates archetypal symbolic reasoning with neurobiological modelling, based on our established framework of artificial neurotransmitters for the modelling and analysis of affective-emotional stimuli to enable interpretable AI-assisted psychological intervention. The system implements a hub-and-spoke topology that coordinates five specialized agents (symbolic, psychological, neurofunctional, decision fusion, learning) that process heterogeneous information via SPADE protocols. Seven archetypal constructs from Jungian psychology and narrative identity theory provide stable symbolic frameworks for longitudinal therapeutic consistency. An empirical study of 156 university students demonstrated significant improvements in depression (Cohen’s d = 1.03), stress (d = 0.89), and narrative identity integration (d = 0.75), which were maintained at a 12-week follow-up and superior to GPT-4 controls (d = 0.34). Neurofunctional correlations—downregulation of cortisol (r = 0.71 with stress reduction), increase in serotonin (r = −0.68 with depression improvement)—validated the neurobiological basis of the entropy-energy framework. Qualitative analysis revealed the following four mechanisms of improvement: symbolic emotional support (93%), increased self-awareness through neurotransmitter visualization (84%), non-judgmental AI interaction (98%), and archetypal narrative organization (87%). The results establish that neuro-symbolic architectures are viable alternatives to large language models for digital mental health, providing the interpretability and clinical validity essential for adoption in the healthcare sector.

The present work discusses developing and applying interactive digital twins and their holographic rendering as new tools to enforce emotional engagement in learning environments and digital museum experiences. In this work, we will demonstrate how a digital twin—an avatar made extremely realistic through the learning of the speaker’s silhouette, facial expressions, gestures, and voice during the model’s learning and construction phase—provides the user with an exceptionally immersive, realistic, and captivating learning experience. The authors first discuss the developments in avatar and digital twin technologies, on the way toward ever more responsive data-driven holographic twins that would mimic human behaviors and reactions while being recorded with real-time emotional responses from museum visitors. The new framework now combines artificial intelligence, holography, and natural language processing to give a customized educational experience while emotionally engaging the viewer through a system that harmonizes third-party solutions, like HeyGen AI to create avatars, which are modeled on real people; the Holo Tube holographic projector to create a 3D digital twin; and Gamma AI to produce content presentations that are highly engaging and visually appealing. The novel methodology introduced can be applied in many domains and reveals the higher potential of digital twins than avatars, for improving educational environments while creating immersive emotionally engaging experiences in museums, thus opening new ways of engaging with cultural heritage.

In an era where artificial intelligence is rapidly expanding into creative domains, the challenge of modeling human-like cognition and emotion in generative processes becomes increasingly central. The present study was made in connection with the exhibition of Munch’s works held in Rome from February to June 2025. Indeed, the paper introduces the concept of a Cognitive Digital Twin grounded in the Super Time-Cognitive Neural Network (STCNN) framework and applies it to the case of Edvard Munch, the iconic Norwegian expressionist. The proposed system—Munch Digital Twin—goes beyond static generative models by integrating temporal, emotional, and cognitive dimensions through a complex-valued time representation t = a + i·b, where a denotes chronological time and b encodes imagination, memory, and creativity. We define Edge Knowledge as an output-stage re-ranking criterion that admits a generated response only where corpus evidence, knowledge-graph constraints and the LLM surface jointly agree (the boundary, or ‘edge’, between documented identity and machine inference). STCNN allows this twin to process real inputs (text, visual prompts, emotional cues) and generate outputs that reflect both the rational and expressive styles of Munch. The imaginary components of the network enable speculative and affective expansions of known artworks—such as reinterpreting The Scream under new emotional or social contexts. This paper presents the theoretical underpinnings of cognitive digital twins, the architecture of the STCNN-based model, and a prototype implementation trained on Munch’s paintings, letters, and critical essays. The system—comprising a GPT-4-Turbo cloud profile and a 4-bit LLaMA-2-13B edge profile for language, Stable Diffusion 1.5 + LoRA for image generation, a Neo4j knowledge graph, and FAISS retrieval—is trained on approximately 600 letters, 100 artworks, and Munch’s diaries and criticism, and evaluated across 100 interactive sessions with 14 students and expert raters. Headline results against an unconditioned baseline include CLIPScore +13.8%, FID −25.5% (small-sample, indicative), and emotion-cosine similarity +44.9%. Ethical implications surrounding posthumous digital emulation, authorship, and emotional manipulation are also discussed. The Munch Digital Twin represents a new paradigm in AI-driven art, where machines do not merely replicate, but collaborate across time with human legacies, enabling an anticipatory and emotionally intelligent form of computational creativity. This work is primarily a conceptual and architectural contribution, supported by a proof-of-concept prototype and a preliminary, non-controlled user study; the quantitative results are indicative and not yet confirmatory.

In the context of managing museums, historical, artistic, and archaeological heritage, an advanced decision support system (DSS) can serve as the engine for a business game platform, optimizing decision paths and management strategies. In complex, multi-parameter scenarios, the final decision is often only part of the process; it is equally essential to follow the decision-making path, that is, the sequence of actions necessary to reach the objective. The DSS presented here simplifies the problem by transforming the initial n-dimensional space, defined by the critical success factors (CSFs) selected by experts, into a two-dimensional space. Indeed, thanks to this approach, the computational complexity is reduced to the point that the technological solution can be used even on standard desktop computers and not only on high-performance computing systems. Moreover, the user does not necessarily need to be an IT expert but rather a specialist in the cultural domain. Through grid-based motion algorithms and a hierarchy of CSF priorities, the system quickly identifies optimal solutions in the 2D plane and then maps them back to the n-dimensional space to maintain consistency with the original context. Since the correspondence between n-dimensional micro-states and two-dimensional macro-states is not one-to-one, the DSS returns the specific micro-state of interest from the optimal macro-state, selecting the most effective path. This research aims to develop algorithms that by minimizing entropy and optimizing the system’s dynamics, build optimal paths in the 2D plane, with algorithms capable of restoring the solution in the initial space. Several use cases in the form of business games have been conducted, demonstrating the value of the proposed solution. The result of this work is a simulation environment useful for museum experts to analyze the impact of their management strategies. Thanks to the ability to assign weights to each of the critical success factors (CSFs), the system can display both qualitative and quantitative simulations of museum dynamics as the weights associated with different CSFs vary. Given the system’s generality, it is applicable to various fields where complex business games are required, such as cultural heritage management, logistics, transportation, healthcare systems, and, more broadly, any context where strategic business analysis is needed for the economic enhancement of resources and their optimization.