24 papers · ranked by Valyu relevance
Thomas Michael Pruyn, Amro Aswad, Sartaaj Takrim Khan, Ju Huang + 2 more
Large Language Models for Metal–Organic Framework Research Authors: Thomas Michael Pruyn, Amro Aswad, Sartaaj Takrim Khan, Ju Huang, Robert Black, Seyed Mohamad Moosavi Artificial intelligence (AI) is transforming research in metal-organic frameworks (MOFs), where models trained on structured computational data…
Beomsu Baek, Eunyoung Jang, Youngsoon Kim, Mingon Kang
Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can…
Lei Cao, Yuntain Li, Hua Qin, Yanbang Shang + 13 more
While AI has automated bioinformatic workflows, biological interpretation remains fragmented and often disconnected from mechanistic insights. Existing AI is bifurcated between statistical “black-box” models that lack logical grounding and simple agents restricted to shallow knowledge retrieval. To bridge this divide…
Patricia Skowronek, Anant Nawalgaria, Matthias Mann
We present a multimodal AI laboratory agent that captures and shares tacit experimental practice by linking written instructions with hands-on laboratory work through the analysis of video, speech, and text. While current AI tools have proven effective in literature analysis and code generation, they do not address the…
Dibakar Datta
Artificial intelligence (AI) is transforming scientific discovery, but its effectiveness is fundamentally limited by the availability of structured scientific knowledge. Although existing databases have accelerated data-driven materials research, much of the knowledge needed for predictive modeling and inverse design…
Yanlin Zhang, Jing Zhao
We formalize scientific methodology—the end-to-end process from question formulation to evidence-grounded writing—as a phase-gated research protocol with explicit return paths and persistent constraints, and instantiate it for general-purpose language models as executable protocol specifications. The formalization…
Elija Perrier
Corporate responsibility turns on notions of corporate mens rea, traditionally imputed from human agents. Yet these assumptions are under challenge as generative AI increasingly mediates enterprise decision-making. Building on the theory of extended cognition, we argue that in response corporate knowledge may be…
Brady D. Lund, Zoë Abbie Teel
As generative artificial intelligence becomes one of the most significant systems of knowledge production in our society today, questions relating to who can access and shape that production grow increasingly important in our discourse. This paper argues that the existing frameworks for AI literacy, which are dominated…
Lucian Pârvulescu, David Livadariu, Victor I. Bâcu, Constantin I. Nandra + 1 more
Species occurrence records represent the backbone of biodiversity science, yet their utility is often limited to spatial analyses, coarse distribution maps, or presence-absence models. Current biodiversity infrastructures rarely provide computational formats directly usable by modern artificial intelligence (AI)…
A Hila
In this paper we interrogate the epistemological implications of human-LLM interaction with a specific focus on epistemological threats. We develop a theory of epistemic justification that synthesizes internalist and externalist conceptions of epistemic warrant termed collective epistemology. Collective epistemology…
Carlota Delgado Vera, Andrea Sinche-Guzmán
Artificial intelligence (AI) has achieved extraordinary progress in recent years, yet this progress reveals a deep educational and epistemic imbalance. Neural architectures have mastered prediction but often obscure the grounds of their outputs. This Perspective argues that knowledge graphs (KGs) are more than a…
Christian R. Klein, Reinhard Klein
Generative artificial intelligence (AI) presents a fundamental duality for education: it simultaneously offers powerful cognitive extension while posing a significant risk of cognitive atrophy. This paper introduces the ‘hollowed mind’ as a conceptual framework to understand this risk-a state of dependency where the…
Xiang Li, Cara Li, Emily Kuang, Can Liu + 1 more
Knowledge workers face increasing challenges in synthesizing information from multiple documents into structured conceptual understanding. This process is inherently iterative: users explore content, identify relationships between concepts, and continuously reorganize their mental models. However, current approaches…
Nicola Luigi Bragazzi, Stefania Monica, Federico Bergenti, Francesca Scazzina + 2 more
Understanding the core principles of nutrition is essential in the contemporary context of abundant and often contradictory dietary advice, to empower individuals to make informed dietary choices and manage diet-related non-communicable diseases. The role of Artificial Intelligence (AI) in providing nutritional…
Authors not listed
Agentic artificial intelligence (AI) is poised to redefine how science is conducted, automating not just data analysis but the entire research lifecycle, from hypothesis generation to validation. Yet most current AI agents remain domain-bound, tailored to specific applications such as materials synthesis or quantum…
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Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision- making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that…
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As the utilization of artificial intelligence (AI) and generative AI (GenAI) is expanding in the educational field, presenting significant implications for STEM disciplines, it is bringing opportunities to enhance how chemistry and chemical engineering are taught and learned. This perspective critically explores the…
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Artificial intelligence (AI) is reshaping chemical engineering. Still, its role in safety-critical operations is limited because we rarely see tools that link physical models with data-driven methods. This study brings together three elements: physics-constrained neural networks, uncertainty quantification, and a…
Xiaohui Shao, Weizheng Jiang, Khairul Nizam Osman
Ongoing debates in higher education regarding whether artificial intelligence should be further integrated or deliberately constrained call for empirical research that offers a more explanatory analytical framework. However, existing studies on the human-AI collaboration (HAC) paradox are largely grounded in a binary…
Zhaoyang Liu, Changchun Gao, Chenhui Yu
Generative AI is reshaping knowledge work, yet its influence on employee knowledge behavior remains theoretically fragmented. Drawing on the Automation-Augmentation Paradox and Cognitive Appraisal Theory, this study constructs a dual-pathway moderated mediation model to examine how generative AI usage simultaneously…
Shakked Dabran-Zivan, Inbal Klein-Avraham, Ayelet Baram-Tsabari, Janet E Rosenbaum
Generative artificial intelligence (GenAI) blurs the boundaries between expert and non-expert sources, as it increasingly distributes and creates scientific content. This study examines how individuals adapt evaluation strategies, including content and source evaluation, and corroboration, when using GenAI versus a…
Authors not listed
Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
Shreyansh Agrawal, Harsh B. Anadkat, Kiran K. Athimoolam, Harsh Bhardwaj + 8 more
Recent advances in artificial intelligence (AI) have prompted claims about autonomous “AI scientists,” yet systematic evaluations of these capabilities remain scarce. This exploratory study investigates whether current AI frameworks can execute scientific research tasks beyond isolated demonstrations. We tested eight…
Yiqun Chen, Stephanie C. Hicks
Scientific coding agents are difficult to benchmark because many research tasks require executable work yet produce ambiguous or hard-to-verify outputs. Because benchmark construction requires substantial time and resources, automation offers a path to accelerating methods evaluation. We introduce an interactive…