A Call for a Values-Guided Agenda for Artificial Intelligence-Mediated Information Systems Scholarship

This editorial calls for a values-guided approach to AI-mediated IS scholarship. By AI-mediated IS scholarship, we refer to IS scholars’ use of AI applications in their research activities. We first describe AI-mediated IS scholarship and its link to scholarly values. Next, we briefly describe the ethical, epistemic, relational, and disciplinary values that have traditionally informed the work of IS scholars. Then, we explain how AI-mediated scholarship can strengthen and weaken these values, stressing the importance of not only espoused values but also enacted values and values work. We conclude by offering a values-guided agenda for our field.

Togelius: Advice for young researchers

I’m currently in the process of recruiting a whole new research lab, as I’m moving to a new university. I’m sifting through several hundred ...

Restructuring Education in the Age of Artificial Intelligence from the Perspective of Humanistic Scientific Methodology

The development of the education market, academic credentials, digital data, and artificial intelligence is widening the gap between knowledge and actual competence, as well as between technical efficiency and human development. This study aims to clarify the humanistic foundation of education, analyze the mechanism of the instrumentalization of knowledge, and establish principles for restructuring education in the context of digital technology. Humanistic scientific methodology is employed in conjunction with the operations of analysis and synthesis, logic and history, abstraction and concretization, criticism and construction. The conceptual material of this study comprises the concepts of life, needs, capabilities, creativity, scientific knowledge, norms, power, the market, qualifications, actual competence, and artificial intelligence. The analysis reveals that education loses its humanistic function when norms, profit, academic credentials, or algorithms occupy the position of the end. Education attains legitimacy when it develops cognitive, creative, self-learning, reflective, and responsible capacities. The study concludes that human beings must retain their position as subjects, while science, the market, and technology must remain means serving life. 

AI Red Penning - undisciplinary

Adding in-person event value to AI-assisted research

Claude-shaped science

Matthew Schwartz describes what happened when he stopped fighting Claude and allowed Claude to find “Claude-shaped” problems. This led him to build BootLoops, a toolkit for exact calculations in quantitative science, which he has been applying across scientific fields alongside experts.

Agentic Laboratories of the Future: Towards World Models for Scientific Discovery

Scientific discovery is fundamentally a problem-solving process involving distributed intelligence. Human intuition, computational reasoning, and experimental execution are distributed across people, instruments, and software systems, limiting the speed and scale of discovery. Although automation, high-throughput experimentation, foundation models, and cloud infrastructure have accelerated individual stages of the scientific workflow, they have not unified the discovery process. We hypothesize that the next generation of laboratories will be agentic: environments in which scientists, AI systems, and robotic platforms operate as collaborative discovery partners, with humans contributing the parts of discovery that remain hardest to make explicit: asking the right questions and holding provisional mechanistic models of how a system works. The key missing layer is an agentic harnessing layer that continuously integrates hypothesis, literature-derived evidence, experimental data, uncertainty, and experimental state into a shared “laboratory world model”—a dynamic representation of the scientific system and its evolving context. By maintaining and updating this lab world model, the agentic harnessing layer enables coordinated decision-making, adaptive planning, and increasingly autonomous scientific workflows across humans and machines. A central challenge is that much of the scientific research process remains inaccessible to machines, including tacit knowledge, human observations, adaptive decision-making, and evolving experimental context. Advances in multimodal AI and immersive interfaces may help bridge this gap, allowing humans, agents, and robotic systems to collaborate seamlessly in scientific discovery rather than simply automating isolated tasks. Agentic laboratories could provide a new architecture for science, integrating human, artificial, and physical intelligence into a unified discovery system.

On the Value of Doing a PhD in the Age of AI

Many students who are just now embarking on a PhD in AI are understandably anxious. It feels like our field is zooming forward ever faster. Our clever methods are often surpassed before they are published. Our deep insights seem less critical to frontier lab progress. AI is automating aspects of the research process that used to require years of specialized training.

Applied Pure Mathematics

Some thoughts about mathematics as a cultural and social technology.

What Next for Academia?

As research agents autonomously synthesize prior work, who gets the credit -- and what keeps talented people in academia when its incentives run on credit?

The Age of Wonders and Terrors

Twenty years ago, when the idea of AI taking over the world in our lifetimes still struck most of us as the unconstrained fantasy of those who knew too much science fiction and too little science, …

How Do We Talk about LLMs?

By Jim Clifford It is never fun to watch friends argue. Generative AI has created multiple fractures across our universities and the community of historians. The stakes are high and we are all deal…

Culture Becomes a Dark Forest

“Wallfacing” to solve Navier-Stokes

AI as the Catalyst of the New Paradigm of Science? | Cadmus Journal

The rapid advancement of Artificial Intelligence (AI) is reshaping the landscape of scientific inquiry. This article examines the challenges confronting modern science, including knowledge fragmentation, diminishing returns of current paradigms, and the disconnect between science and society. It explores how AI offers solutions enhancing collaboration and integrating knowledge—as well as considers the possible “darker side” of using AI in research. However, the advent of AI in science not only enhances scientific research but also redefines the role of human researchers and fosters a new paradigm of human understanding. By embracing these transformations, a more cohesive and responsive scientific enterprise can emerge.

Shut Up and Prove - Hyperproblems

TLDR; We are at an asteroid hitting the earth level of crisis of intellectual life. My first love was the foundations of mathematics. Or maybe the second lo…