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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 ↗6055 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
From answer machine to collaborative partner: impact of structured generative AI training on scientific problem solving in STEM education ↗
Abstract Background Generative AI (GenAI) holds great potential for STEM education, especially in the context of problem-solving tasks, but unguided use often leads to metacognitive laziness, where students treat the technology as an answer machine rather than a collaborative partner. On the basis of Zimmerman’s self-regulated learning (SRL) framework and cognitive load theory (CLT), our randomized controlled trial (RCT; N = 95) investigated whether a structured, SRL-aligned scaffolding training session could improve students’ AI-assisted revision performance in electromagnetism problems. The…
- OpenAlex · Published · First archived
Artificial Intelligence-Supported Case Discussions: Harnessing Artificial Intelligence for Critical Engagement ↗
The case method has been successfully used in operations management courses and is central to business education, yet its effectiveness depends on consistent student preparation. Many students engage only superficially with cases, and the rise of generative artificial intelligence (AI) tools, such as ChatGPT, further complicates the challenge—providing shortcuts that yield summaries without fostering deeper understanding of trade-offs. This study investigates whether AI can instead be used to strengthen the case method as a pedagogical tool. Partnering with an AI-based upskilling platform, we…
- OpenAlex · Published · First archived
Lithium-ion battery degradation mode quantification for 2nd life using half-cell data and machine learning techniques ↗
The reactions inside lithium-ion batteries make it complex to quantify the aging behavior, especially those to be used in second-life applications. The hidden mechanisms can suddenly overturn the observable aging mechanism, generating a sudden decrease in the battery performance rate. In this context, this study presents a machine learning model that observes the hidden aging mechanisms of lithium-ion batteries. To do so, anode- and cathode-level data have been obtained to artificially generate several aging paths defined by the loss of lithium inventory, loss of active material, and increment…
- OpenAlex · Published · First archived
Enhancing the Efficiency and Flexibility of AutoMeKin: Integrating ORCA and Machine-Learning Potentials ↗
Abstract Automated reaction discovery is often limited by the cost of high-level refinement of large reaction networks. Here, we extend AutoMeKin, an automated reaction-discovery program, by presenting a complete integration of ORCA 6 into its high-level refinement workflow, providing access to a broad range of electronic-structure methods through a package freely available to academic users. We also integrate the UMA and MACE-OMOL-0 machine-learning interatomic potentials and Δ-ML energy corrections, enabling automated refinement of reaction networks using conventional electronic-structure me…
- OpenAlex · Published · First archived
A New Metacognitive Confidence Calculation Approach Based on Large Language Models for Natural Language Explanations ↗
Traditional methods used to measure metacognitive confidence rely on superficial features such as word count and ambiguity and therefore fail to adequately reflect the level of metacognitive confidence in statements. In this study, in addition to traditional text-based confidence measures, we used a Large Language Model (LLM) to directly analyze confidence and its ratio in statements. LLM-based confidence scores were calculated on statements from the e-SNLI and CoS-E open-source datasets, and the prediction performance obtained with traditional features was compared. The results indicate that…
- OpenAlex · Published · First archived
Voice-enhanced image caption generation with vision transformers and Google Cloud TTS ↗
Abstract Today, when it comes to the digital world, engaging image captions are crucial to such industries as travel, where the strong visuals play a crucial role in attracting the audience. Nonetheless, manual captioning is both time-consuming and usually lacks the subtlety of a scene. The paper presents an AI-based model of automatic image captioning with contextual enrichment, based on deep learning models, which have been trained using the Flickr8k dataset. We combine a pretrained Vision Transformer (ViT) that retrieves granular visual features and global contextual features of images usin…
- OpenAlex · Published · First archived
Governing AI Outputs in a Fully Sensed Society: Is Output Governance Sufficient for Social Acceptance? ↗
Advances in sensing, the Internet of Things (IoT), and artificial intelligence (AI) are moving smart cities toward a Fully Sensed Society (FSS), in which human behavior and states can be continuously inferred. This paper presents the Fully Monitored Public Space (FMPS) as a scaled-down model of the FSS and a smart-city testbed, and examines two questions. First, to what extent can unauthorized external AI outputs be structurally deterred? The previously proposed Verifiable Record of AI Output (VRAIO) binds outputs to Rules, an independent Recorder, tamper-resistant records, audits, and sanctio…
- OpenAlex · Published · First archived
An open vision-language model for diverse medical applications ↗
Abstract Artificial intelligence has high potential for impact in healthcare applications, but its training and deployment are challenging due to diverse data, a complex spectrum of possible tasks and important privacy needs. High-performing foundation models that enable data-efficient fine-tuning for diverse downstream tasks can meaningfully accelerate development in this domain. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3. MedGemma demonstrates advanced medical understanding and reasoning across images and text and multiple medical imagin…
- OpenAlex · Published · First archived
A sustainable platform for federated health data access, AI innovation, and regulatory acceptance in alignment with the European Health Data Space principles ↗
The IDERHA ( I ntegration of Heterogeneous D ata and E vidence towards R egulatory and H TA A cceptance) project aims to enhance medical research by establishing one of Europe’s first pan-European, disease-agnostic health data spaces. Aligned with the European Health Data Space (EHDS) principles, IDERHA addresses critical challenges in data quality, standardization, and governance, ensuring compliance with GDPR, the AI Act, and emerging EHDS regulations. Its ambition is to enable secure, federated access and analysis of health data, fostering data-driven collaboration and innovation in healthc…
- OpenAlex · Published · First archived
Beyond the prompt for pedestrian traffic and crowd behavior management: PedAgent, a Large Language Model empowered Agent with an integrated simulating-analyzing-optimizing functional chain ↗
Pedestrian walking is one of the most fundamental modes of daily travel, yet recurrent pedestrian flow-related safety accidents, e.g., crowd crushes, highlight the urgent need for more intelligent management of high-density pedestrian facilities, for instance, urban subway stations. Existing pedestrian flow simulation models and empirically validated management policies provide valuable support, but they rarely satisfy the full-process requirements of crowd behavior management, i.e., simulation software can generate individual-level trajectories, but it usually lacks the ability to derive case…
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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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