Research design and experiments

A Strategic Entrepreneurship Journal study turns venture ideas into text an LLM can refine and recombine, and finds AI may make rival firms imagine alike.

A Stanford HAI panel on how AI is changing the practice of science, from productivity gains and narrowing research questions to AI agents that design experiments and review papers.

AI finds solutions across 22 fields and cracks an 85-year-old Enigma message, why GenAI may erode advantage built on neglect, AI-made ads that don't look like AI, and a Science study on AI diagnosis.

Two new PNAS studies on telling humans from machines in your data, AI-written PhD dissertations, ChatGPT versus Google Translate in the labour market, and why competition may curb AI homogenisation.

Lookahead bias in LLM-based social science, hidden leakage inside autonomous AI scientists, AI that resolves open Erdős problems, and what ChatGPT did to firm values.

Anthropic's agent-run biolab reports CRISPR-like repeats in giant viruses, a new standard lets AI agents drive lab instruments, and a PNAS reanalysis says human–AI teams underperform because we never let humans learn.

Generative AI embeddings bring 50,000 real product descriptions into one experiment, AI-generated headshots expose bias against femininity, and PNAS asks whether AI-generated science has a validity problem.

An APA convention session on AI trends and predictions for psychology, from fear and personalised care to predictive modelling.

A Strategic Management Journal framework for running experiments with LLM-powered agents, a PNAS essay on whether research stays a human enterprise, and store cameras turned into an 'offline clickstream'.

GPT-4 forecast the results of 70 real survey experiments almost as well as running them — and a new Science agent now runs biomedical studies on its own. What autonomous research instruments mean for your lab.

New AI systems from Google DeepMind and FutureHouse generate hypotheses, design experiments and interpret results — but the evidence still comes from the lab bench. Plus deepfake face detection, deep learning for company dynamics, and the evolvable-AI threat debate.

AI Agents in Online Surveys If you run online surveys today, there is a good chance your respondents come from an online recruitment platform. These platforms now sit behind a large share of behavioral research, shaping academic work in psychology, public health, economics, and political science. They also support consequential commercial decisions in product design and public communication. But the rise of large language models (LLMs) has made an old concern feel newly urg

Generative AI democratization in consumer research: broader participation brings new voices into the data ecosystem. Today, a consumer researcher beginning a new project may start not with a database, but with a generative AI system. Within seconds, a structured framework emerges. Literature streams are synthesized, research questions are articulated, and potential hypotheses are outlined. The efficiency is undeniable. Yet Huang and Rust argue that efficiency is not the centr

As large language models (LLMs) become widely embedded in various decision-making scenarios, people increasingly rely on them to offer moral advice or even directly participate in moral decision-making. But a critical question must be answered: Can these AI systems really make moral judgments that align with human values? Cheung et al. (2025) systematically studied how LLMs respond when facing realistic moral dilemmas through four experiments and compared their responses to

Introduction Generative AI is profoundly reshaping consumer research, enhancing creative ideation, supporting qualitative analysis, and redefining the field’s trajectory. Three empirical studies and one review from the Journal of Consumer Research, Oxford Academic, offer complementary frameworks and practical guidance, revealing the vast potential and pitfalls of human–AI collaboration. Creative Ideation: The Double-Edged Sword of LLMs As a cornerstone of consumer research, c

Artificial intelligence (AI), particularly generative AI as applied in content creation, has become a research topic of significant interest. While discussions about its quality, integrity, and copyright issues in research have arisen, the potential of generative AI to assist researchers in developing experimental stimuli is often overlooked. van Berlo et al. (2024) introduced the MADE (Mapping, Assembling, Demonstrating, Executing) framework, aiming to provide a comprehensiv

Since its emergence in December 2022, ChatGPT has sparked widespread discussion, leading to the rapid adoption of Large Language Models (LLMs) across various industries. These models now assist users in handling a broad spectrum of open-ended tasks, many of which demand a high level of creativity. Last year, Zenan Chen and Jason Chan published a groundbreaking study in the top journal Management Science, titled "Large Language Models in Creative Work: The Role of Collaborat

Imagine if we could automate the discovery of groundbreaking scientific hypotheses, unlocking new possibilities faster than ever before. Three research teams have taken a significant step in that direction, unveiling a machine learning framework designed to generate and evaluate scientific hypotheses with remarkable efficiency. The new framework is built around three essential stages: extracting, generating, and evaluating hypotheses. Initially, the system extracts existing h

Christopher A. Baila, aDepartment of Sociology, Duke University https://doi.org/10.1073/pnas.2314021121 This perspective article by Christopher A. Bail examines the potential impact of generative AI on social science research. The paper discusses both opportunities and limitations of using generative AI tools in social science methodologies. Key Points: Potential Applications: Survey research: Generative AI could be used to pretest surveys, impute missing data, and potential

Peiyao Li, Noah Castelo, Zsolt Katona, Miklos Sarvary Published Online:25 Jan 2024 https://doi.org/10.1287/mksc.2023.0454 Abstract: This paper explores the potential of large language models (LLMs) to substitute for human participants in market research. Such LLMs can be used to generate text given a prompt. We argue that perceptual analysis is a particularly promising use case for such automated market research for certain product categories. The proposed new method generate