Human–AI Delegation in Social Science Codifiable Tasks vs. Tacit Knowledge, Theoretical Originality, and Field Judgment In an age where everyone is talking about AI, social science research finds itself at a delicate crossroads. Recently, Yongjun Zhang (2026) from Stony Brook University raised a provocative question in his latest paper, "Vibe Researching as Wolf Coming": what parts of your research can only you do, and what can the machines never replace? Is it running tediou

The rapid development of generative artificial intelligence is redefining how consumer and marketing researches are conducted. When a model can replicate the consumer research processes of 35 top-tier journal articles within minutes, are we still engaging in “human-led science”? A study by Yoo, Haenlein, and Hewett, published in the Journal of the Academy of Marketing Science, addresses precisely this question: Is AI merely transforming research tools, or is it reshaping rese

The contrast between human interpretive work and computational code reflects the tension between qualitative meaning-making and generative AI’s promise of efficiency Generative AI is increasingly used in qualitative research, yet its promise of efficiency raises fundamental questions about interpretation, meaning-making, and the role of the researcher. This article is based on the academic presentation "Interpreting Qualitative Data with AI: Pitfalls and Potential" by Duc Ngu

Economics in the Age of AI Agents Over the past two years, the way economists conduct research has undergone a profound structural transformation. In his 2025 NBER Working Paper AI Agents for Economic Research, Anton Korinek argues that the research community is moving from simple conversational systems such as ChatGPT to a new paradigm centered on autonomous AI agents. These systems integrate text generation with planning, memory, and tool use, enabling them to execute multi

AI and qualitative research As generative artificial intelligence (GenAI) rapidly transforms the landscape of scientific research, qualitative scholars are confronted with an unprecedented technological challenge: Can this emerging tool—powered by large language models (LLMs)—truly fulfill the humanistic task of qualitative data analysis, which is fundamentally rooted in interpretive work? In their recent publication, Nguyen and Welch (2025) offer a systematic and incisive re

AI creativity compared to human imagination: while large language models generate patterns based on training data, human creativity draws from unique experiences and emotions. Setting the Stage: AI and Creativity Large language models (LLMs) are advancing at a remarkable speed. From writing stories and poetry to brainstorming ideas, their presence in creative work is undeniable. But here lies a fundamental question: can these models genuinely support collective creativity, or

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