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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 ↗6043 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
Sustainable Education: STEM Students’ Use of Generative AI in Education: Perceived Usefulness and Intentions to Persist to Degree Completion ↗
This article highlights the sustainability of STEM education and the role of emerging technology, specifically Generative Artificial Intelligence (GenAI), in supporting students’ continued educational progress. GenAI is rapidly becoming part of undergraduate education, yet little is known about how students’ use of GenAI functions within the social-cognitive and contextual processes associated with academic persistence. This study examined patterns of GenAI use and tested GenAI use as a proximal affordance within the Social Cognitive Career Theory Performance/Persistence Model among a national…
- OpenAlex · Published · First archived
An Automated Framework for Legacy Java Modernization Integrating Software Quality, Business Value, and GPT-4-Assisted Re-Engineering: An Empirical Evaluation ↗
Legacy software systems remain operational in many organizations because of their long-standing business value, yet structural degradation and outdated documentation make them increasingly difficult to understand and modernize. Existing reverse-engineering and modernization approaches often address program comprehension, quality assessment, decision support, and code transformation separately, leaving a gap between understanding legacy code and prioritizing components for modernization. To address this gap, this study introduces a seven-step automated framework integrating program comprehensio…
- OpenAlex · Published · First archived
Numerical Recovery of Pore-Air Pressure and Effective-Stress Reduction in Cover Soils Using a Two-Phase Inverse Physics-Informed Neural Network ↗
Transient pore-air pressure in gas-loaded cover soils is difficult to observe between monitoring depths. We develop a coupled water–air inverse physics-informed neural network as a numerical proof of concept. Under a pre-calibrated constitutive model, three numerically sampled depths supply pore-air pressure, pore-water pressure, and saturation targets. An independent air-pressure field represents overpressure; the coupled balances constrain the joint state. On a one-dimensional same-equation benchmark, the full-window gas-pressure error is 0.0463. The data-only ablation reaches 0.0043, while…
- OpenAlex · Published · First archived
Multimodal LLM Pipeline for Automated Analysis of Technical Graphical Documents ↗
Automating the evaluation of technical documents, such as laboratory reports, requires balancing accurate multimodal information extraction with efficient downstream processing. This paper proposes a vision–language model (VLM) processing pipeline that extracts textual and graphical content into structured intermediate representations (JSON), enabling subsequent document assessment without redundant image retransmission. We evaluate the approach across a corpus of laboratory reports, investigating the impact of prompting strategies on text extraction and document analysis. The evaluation revea…
- OpenAlex · Published · First archived
Pedagogical Scaffolding for Generative AI-Supported Self-Directed Learning: A Qualitative Study of the CRAFT Framework ↗
This study investigates how generative Artificial Intelligence (AI) can scaffold self-directed learning (SDL) in secondary classrooms. Focusing on the interaction between teacher facilitation and AI affordances, it examines and refines the Content–Response–AI&peers–Fusion–reflecT (CRAFT) framework as a practitioner-informed pedagogical scaffold for operationalizing aspects of the Process dimension of the Person–Process–Context (PPC) model. The study addresses a persistent gap between SDL theory and classroom practice by examining how learner agency can be cultivated within AI-supported learnin…
- OpenAlex · Published · First archived
Agentic Generative AI in Higher Education: Perceived Benefits, Risks, and Implications for Learning ↗
Generative artificial intelligence (GAI) is increasingly used in higher education for explanation, content generation, feedback, and personalized learning. However, conventional prompt–response chatbots may provide insufficient support for complex academic tasks requiring planning, evidence organization, comparison, justification, verification, and reflection. This study proposes the Agentic GAI-Supported Learning Framework for Higher Education (AGAI-HE), which positions GAI agents as bounded, human-supervised learning partners rather than substitutes for students or instructors. Following an…
- OpenAlex · Published · First archived
Student-Reported Cognitive Engagement with Structured Generative-AI Questioning: Differentiated Associations with AI, Teacher, and Peer Questions ↗
Generative AI can sustain follow-up questioning, but questions do not constitute engagement by themselves. This qualitative-dominant mixed-methods study examined student-reported cognitive engagement around AI, teacher, and peer questions in a Chinese university critical-thinking course. Seven reflection points produced 1034 matched submissions, including 648 substantive texts. A structured source item provided an implementation check for 399 responses at two common reflection points, and two human coders independently analyzed a stratified sample of 120 reflections. Sampling-weighted and refl…
- OpenAlex · Published · First archived
D-MambaFormer: integrating Mamba and transformer for long-term energy time series forecasting ↗
Time series forecasting in the context of energy necessitates balancing short-term and long-term dependencies for accurate predictions. As the forecast horizon extends, it becomes increasingly challenging for existing methods to model both types of relationships effectively over long sequences. Transformers excel in modeling short-to mid-term dependencies but are often criticized for their quadratic computational cost. On the other hand, Mamba, with its near-linear complexity through a selective state-space mechanism, provides an advantage for long-term forecasting. To combine both effectivene…
- OpenAlex · Published · First archived
Enhancing Specific Stiffness of Nano‐Architected Materials Through Generative AI and Active Learning ↗
ABSTRACT Architected materials derive their high performance from geometry as much as from intrinsic material properties, yet most optimization frameworks search within predefined topology families, limiting the discovery of genuinely new morphologies. Here, we investigate whether superior architectures exist beyond the reach of these parameterized families, and demonstrate that they can be systematically discovered. We present a framework that searches directly in voxelated structural space. Cubic‐symmetric unit cells drawn from strut‐based, TPMS‐based, and hybrid families are encoded into a…
- OpenAlex · Published · First archived
“Word‐By‐Word” or “Line‐By‐Line” Presentation? How Response Generation Dynamics in Generative AI Influence User Acceptance ↗
ABSTRACT Generative AI systems are increasingly integrated into various user support and decision‐making contexts. While extensive research has explored user acceptance of generative AI across domains, less is known about how its inherent presentation features influence such acceptance. This study investigates how the presentation format of generative AI responses—high‐dynamism formats (word‐by‐word) versus low‐dynamism formats (line‐by‐line)—affects users' willingness to accept its output. Three experiments, across various contexts, including daily question‐answering, online shopping, and cro…
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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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