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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗5484 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Personal and Academic Factors in Teachers’ Perceptions of Educational Artificial Intelligence ↗
(1) Background: The aim of this study was to analyze how teacher trainees and teachers relate to Artificial Intelligence (AI) in education across socio-emotional, attitudinal, competency-based, and ethical dimensions, considering the influence of personal, academic, and professional variables. The study was conceptually framed by the UNESCO AI Competency Framework for Teachers (AI CFT), complemented by DigCompEdu as a broader reference for teacher digital competence. (2) Methods: A quantitative, non-experimental, descriptive, cross-sectional design was developed with a sample of 1292 participa…
- OpenAlex · Published · First archived
A graph neural network-based system for evaluating dance training effectiveness and dynamically adjusting training plans ↗
The integration of digital technologies in dance education provides opportunities to enhance training effectiveness. Traditional motion-based systems primarily focus on static recognition and fail to model complex movement relationships or adapt training plans in real time. This research aims to develop an intelligent framework for evaluating the effectiveness of dance training and dynamically adjusting training plans using a Bobcat optimization-tuned graph neural network with autoencoder (BO-GNN-AE). A Python-implemented system is designed, integrating structured pose keypoints, motion trajec…
- OpenAlex · Published · First archived
Lower peaks, similar growth: Isolating the value and limits of artificial-intelligence-driven thermal sensation modeling for building-grid resilience under future climates ↗
Evaluations of artificial intelligence (AI) in building energy control often conflate supervisory policy and occupant prediction. We present a control-isolated EnergyPlus benchmark in which three predictor-driven arms share the same supervisory logic: analytical predicted mean vote (PMV), gradient-boosted trees implemented with the Light Gradient Boosting Machine (LightGBM), and a physics-informed neural-network variational autoencoder (PINN–VAE); a fixed-setpoint controller serves as a separate operational reference. Experiments cover five American Society of Heating, Refrigerating and Air-Co…
- OpenAlex · Published · First archived
Single event transient effects and variability in hybrid RRAM-CMOS spiking neural network ↗
The spiking neural network (SNN) uses discrete spikes exploiting parallel, event-driven computations to process large amount of digital data, replicating the behavior of human brain. The SNN circuit has a leaky-integrate and fire (LIF) neuron and resistive RAM (RRAM) synaptic array with in-situ weight update through spike timing-dependent plasticity (STDP) mechanism. The single event transients (SET) due to particle strikes are major reliability threat for high speed integrated circuits. The effect of single event transients (SET) in 1T-1R RRAM synaptic array and CMOS LIF neuron circuit are an…
- OpenAlex · Published · First archived
Machine learning-based pathomics signature as a novel digital marker for C-X-C motif chemokine ligand 9 expression and prognostic assessment in breast cancer ↗
Objectives: C-X-C motif chemokine ligand 9 (CXCL9) is a critical mediator of tumor-infiltrating lymphocyte recruitment and a potential biomarker in breast cancer (BC). This study aimed to develop a machine learning-based handcrafted pathomics model to forecast survival outcomes by characterizing CXCL9-related morphological signatures from routine hematoxylin and eosin (H&E) slides. The biological relevance and clinical utility of the derived pathomics score (PS) were subsequently evaluated to provide an objective framework for risk stratification. Material and Methods: We developed a pathomics…
- OpenAlex · Published · First archived
Quantum Sociodynamics for AI-Supported Sustainable City Transitions: A Gauge-Covariant Method for Modelling Symbolic Mediation and Public Trust ↗
Quantum Sociodynamics (QSD) is introduced as a computational method for modelling symbolic influence and trust in artificial-intelligence-supported sustainable city transitions. Its central object, the Symbolic Mediation Field, represents how actor orientations, source–audience relations and communication histories shape signal effects. Actor states are encoded by complex variables ψi, symbolic transport by U(1) link variables Uij, signed coupling by qi and external interventions by Ji. Gauge-invariant measures capture local alignment, path transport and loop holonomy. Formal analysis establis…
- OpenAlex · Published · First archived
Review of AI agents in engineering design workflows ↗
Technology related to artificial intelligence (AI), especially agentic systems, has attracted significant interest and rapid advancement in recent years. With this growth comes an increasing potential for integrating agents into engineering design workflows. This paper examines the current state of AI agent technology from an engineering design perspective. Descriptive bibliometric analysis characterizes recent research trends, showing rapid growth in engineering-related research despite comparatively limited publication volume. A consistent boundary for agentic behavior is established to dist…
- OpenAlex · Published · First archived
AI Tutor, Online Course, or Both? A Randomized Experimental Pilot Study for Evaluation Capacity Building ↗
Extension lacks literature comparing standalone generative artificial intelligence (AI) with structured professional development for capacity building. Guided by Social Cognitive Theory and Cognitive Load Theory, this study evaluated the effects of an asynchronous online course and a custom AI tutor on evaluation knowledge and self-efficacy among Cooperative Extension professionals. We conducted a randomized 2 × 2 factorial pilot study crossing access to an evaluation certificate course with access to a custom Generative Pre-trained Transformer (GPT). Of 144 registrants, 78 provided matched pr…
- OpenAlex · Published · First archived
How Underdog Brand Biography Mitigates Consumer Skepticism Toward AIGC: Evidence from AI-Generated Agricultural-Product Advertisements ↗
While AIGC can improve operational efficiency in marketing, it may also erode consumer trust owing to widespread skepticism toward AIGC. Therefore, this study examines agricultural product advertisements to explore the impact of Underdog Brand Biography (UBB) disclosure on consumers’ purchase intentions. It also investigates the mediating roles of cognitive trust and affective trust, as well as whether these effects differ across product-price levels and production-background conditions. We conducted three experiments to investigate these relationships. The data were analyzed using t-tests, an…
- OpenAlex · Published · First archived
Trust in AI-based Educational Technologies as a Predictor of Generative AI Acceptance in Science Teacher Education: A Cross-Sectional Survey Study ↗
As the integration of artificial intelligence (AI)–based educational technologies into teaching and learning processes accelerates, trust has emerged as a key determinant of whether preservice teachers are willing to adopt these technologies. The present study aimed to examine the relationship between the perceived trust of pre-service science teachers in AI-based educational technologies and generative AI acceptance and to determine the predictive power of trust on acceptance. Grounded in the TAM, the study employed a cross-sectional correlational survey design to investigate the role of trus…
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How to assess evidence about Artificial Intelligence
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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