Jered Mcclain, Erydir Ceisiwr, SEXA Institute of Technology and Interdomain Galactic Advisors, United States of America
Autonomousaero space and mech anical systems require supervisory architecture scapable of determining whether candidate operating states remain mutually consistent before execution. This paper evaluates a hardware-verifiable recursive admissibility architecture derived from the SEXA computationalframework using a controlled mechanical digital twin. Ten state variables are evaluated through six sequential Γadmissibility stages against nominal trajectories and ten controlled fault classes. Parameters were calibrated on separate nominal trajectories and frozen before held-out testing. Thefinal benchmark comprised 1,230,000 mechanical-state evaluations and achieved 97.57% classification accuracy, 95.70% fault sensitivity, 99.59% nominal-state acceptance, and a 4.30% false-admission rate. An independent scalar-threshold baseline produced an 85.55% false-admission rate. The results support recursive admissibility as a reproducible computational engineering constraint architecture under the synthetic conditions tested while preserving explicit boundaries between computational validation and physical hardware or flight validation.
Recursive Admissibility, Mechanical Digital Twin, Fault Detection, Autonomous Aerospace Systems, Hardware Verification.
Hansell Solís Ramírez and Gustavo López, University of Costa Rica, Costa Rica
In programming, large language models that deliver complete solutions endanger students’ reasoning instead of supporting it. This paper reports the human-centered design and integrated evaluation of a Socratic conversational agent for introductory programming at the University of Costa Rica. Twenty students participated by solving a timed Python task and five evaluations were triangulated: perceived usability (SUS), cognitive load (NASA-TLX), interaction logs(411 prompts), an audit of fidelity to the system prompt (374 responses), and a focus group. The agent proved learnable and accepted (SUS = 55.75; moderate load).
Socratic tutoring, Pedagogical scaffolding, Large language models, Usability, Cognitive load.
Olajide Jolugbo, United Kingdom
Teacher professional capability is increasingly exercised under conditions that change faster than conventional professional development cycles. Existing digital and AI competence frameworks specify contemporary expectations, but provide less guidance on whether evidence of previously demonstrated capability remains sufficient when technologies, regulation, assessment and professional contexts change. This conceptual paper develops capability currency, defined as the contemporary sufficiency of a specified professional capability claim in relation to current requirements, available evidence, context and conditions of enactment. Through an integrative conceptual synthesis of teacher competence, adaptive expertise, professional learning, professional digital competence, continuing competence and skill obsolescence, the paper develops the Teacher Capability Operating System (T-COS). T-COS links currency-trigger sensing, materiality triage, review, diagnosis, targeted renewal, demonstration where warranted, proportionate revalidation and evidence updating. Six propositions identify relationships for empirical testing. The model distinguishes changed professional conditions from demonstrated capability gaps and places professional agency, proportionality, privacy and contestability across the lifecycle.
capability currency, teacher professional capability, artificial intelligence, adaptive expertise, professional learning.
Ashuin Sharma and Gustavo Lopez, University of Costa Rica, San Jose, Costa Rica
Digitized whole-slide images (WSI) have expanded remote access to histopathology teaching material, yet many academic units still lack a locally owned, web-based platform for storing, cataloguing, and analyzing these images at high resolution. In this paper, we describe and evaluate Histovis, an extensible web platform for visualization, curation, and AI-assisted analysis of high-resolution WSI, and report a classroom-based usability evaluation conducted at a public university. We built a three-tier, multi-language microservice system combining a tile-rendered viewer with an asynchronous, plugin-based mechanism for attaching machine-learning analysis tools. Evaluation combined the standardized User Experience Questionnaire Plus (UEQ+) with qualitative observation during an authentic histology class. Forty-six participants completed the questionnaire. All ten retained UEQ+ scales scored above +2.5 on a −3-to-+3 range; the importance-weighted global user-experience indicator reached 2.80 (SD 0.40). Cronbach’s alpha exceeded 0.79 for nine of ten scales. Ethnographic observation identified rendering speed and annotation as the most valued features. Combining tile-based high-resolution rendering with an extensible, queue-based AI-plugin mechanism produced a platform that was well received in an authentic teaching context and generalizes as a reusable architectural pattern for institutions building similar digital-pathology teaching infrastructure.
Whole slide imaging, Digital pathology, Medical education, Usability evaluation, Extensible software architecture, Immunohistochemistry.
