In this paper, the aim is to present a project report of a method, based on which a small natural language lexicon is designed, or otherwise a small language model, for robotic applications. In this direction, a reference is made to Controlled Natural Languages, and their characteristics are determined for application in operational robotic vehicles. In addition, criteria are established for the selection of the nationality of the language, which constitutes the basis of the lexicon. A small introspection is made in the natural language unit of a human–robot interface, and a method is presented that examines the adequacy of the selected language in relation to the purpose of use of the system and the control criteria. The use of the lexicon’s method is presented, and an example of the analysis and inclusion of selected Greek language words is given. The reproduction of words from the lexicon’s stored data is also explained, as the reverse process that functions as a confirmation of the method. Received: 22 August 2025 | Revised: 20 April 2026 | Accepted: 12 October 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Ioannis Giachos: Conceptualization, Methodology, Software, Investigation, Resources, Writing – original draft, Visualization. Evangelos Papakitsos: Conceptualization, Methodology, Investigation, Resources, Writing – original draft. Nikoletta Samaridi: Investigation, Resources, Data curation. Michail Papoutsidakis: Validation, Formal analysis. Nikolaos Laskaris: Writing – review & editing, Supervision.
This editorial highlights the importance of digital ontologies and ontology-like Data-Information-Knowledge-Wisdom (DIKW) artifacts in advancing AI. We invited original research and reviews on the role of ontologies in AI development, enhancement, and applications.
The agriculture sector in Ecuador faces significant challenges in its digital transformation process, primarily due to the lack of accessible tools for the capture and management of georeferenced crop data. In this context, this study proposes Imagro, a hybrid platform comprised of a mobile application and a web application, designed to capture, store, visualize, and classify crop images with integrated spatial metadata. This solution incorporates lightweight machine learning models, specifically MobileNet V1 with TensorFlow.js, which enables real-time automatic crop classification from web browsers, without the need for specialized infrastructure. The research employs a hybrid methodological approach that combines documentary techniques, such as systematic literature reviews and comparative analysis of tools. with experimental techniques including evolutionary prototyping, functional tests, and usability assessment. The inductive method was used to analyze user experience through direct observation and the application of the System Usability Scale (SUS) questionnaire, and analytical methods were used to study the system architecture, the performance of the model, and its main functionalities. The dataset used was built from open repositories and expert contributions, and the Imagro platform has been evaluated by users in agricultural and academic environments. The results show an SUS score of 78.2 and a 98% success rate in task execution, confirming the potential of Imagro as an accessible and functional digital tool to strengthen precision agriculture, boost applied research, and support agricultural education in rural contexts with limited connectivity. Received: 10 August 2025 | Revised: 31 December 2025 | Accepted: 4 February 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available on GitHub at https://github.com/Darling-P11/imagro_web. Author Contribution Statement Lucrecia Llerena: Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Nancy Rodríguez: Software, Supervision, Project administration, Validation, Investigation. Kevin Ponce: Methodology, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Software, Supervision, Project administration, Validation, Investigation.
We study Xingye AI (星野·AI皆你所见), a generative emotional companion used by Chinese Gen Z. This research took advantage of a quantitative research approach by employing survey as the data collection method and descriptive statistics as the data analysis method. With survey data from N = 402 active users, we tested a dual-path model: two risk factors (bedtime procrastination and problematic use) and two protection factors (anthropomorphism and perceived empathy). We also used hierarchical regression models. Risks align with higher depression, anxiety, loneliness, and sleep disturbance; protections show the opposite pattern. Adding protections improves model fit across outcomes (ΔR² ≈ 0.06–0.09). Beyond these behavioral results, we turn them into design. The coefficients set parameters for an adaptive pacing mechanism and a lightweight bandit/RL updater that tunes empathy and timing per user. The loop keeps a supportive tone by day and discourages over-engagement after 23:00. This links measurement to implementation and points to well-being-oriented affective computing. Received: 21 September 2025 | Revised: 12 December 2025 | Accepted: 21 January 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available on Zenodo at https://doi.org/10.5281/zenodo.18279792. Author Contribution Statement Haiyang Li: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Jing Liu: Conceptualization, Writing – review & editing, Supervision.
The paper examines the cognitive dilemma that exists between the embodied human cognition and disembodied nature of artificial intelligence (AI) in English as a foreign language education. While most current studies in computer-assisted language learning have been quantitative in nature, this study takes a qualitative, phenomenological perspective to examine the processes that learners go through when interpreting and parsing the AI-driven digital space. The study, based on metaphor-led discourse analysis of the students’ narratives at the university, shows that three main image schemas exist in the conceptual system of the students as follows: Container, Path, and Force. A descriptive categorical analysis was used to explore the distribution of image schemas in each coded interaction state (Flow and Stagnation). Due to the nested design of multiple-metaphor mentions within one participant, the findings indicated patterns of association between metaphoric schemas and learners’ reported interaction experiences, but their interpretations were made with caution. The results also indicate that learners experience cognitive re-embodiment in mapping abstract algorithmic interactions onto the concrete constraints of space, motion, and experiences of effort and resistance. These metaphorical representations can be important in bridging the embodied experience of humans and the virtual experience mediated by AI. The paper suggests a cognitively congruent way of designing AI that sets a new trend in the development of more natural and humanistic methods of language learning that are not only more cognitively compatible with the human learner but also more functional. Received: 8 May 2026 | Revised: 3 August 2026 | Accepted: 30 August 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Zein Bassam Bani Younes: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Project administration. Majid Mohammad Al-Khataybeh: Conceptualization, Validation, Resources, Writing – review & editing, Supervision. Nesreen Bassam Friehat: Methodology, Formal analysis, Investigation, Data curation, Writing – review & editing. Abeer Hadi Salih: Validation, Investigation, Writing – review & editing, Visualization. KiranGul: Investigation, Writing – review & editing.
