AI and the Future of Academic Research

Friends of the Fredrickson Family Innovation Lab,

You probably heard that, like it or not, AI has been increasingly incorporated into nearly all stages of academic research. Does it live up to all the hypes to “revolutionize research”, or has it created a technological bubble destined to burst? Will my AI-assisted research be appreciated or discriminated against by academic publications? How exactly is AI adopted in the scholarly world? What are its advantages, caveats, and potentials? 

If you are like me and have similar questions about AI and scholarship, you will benefit tremendously from the upcoming FFIL virtual talk by Dr. Laura Nelson, one of the leading experts on AI-assisted methodology in sociology and Director of Centre for Computational Social Science at the University of British Columbia. 

You may find more details about the FFIL event below, including the RSVP link.

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Future of AI and Research Series: The Symbiosis of AI and Sociology 

Dr. Laura Nelson, Director of Centre for Computational Social Science, University of British Columbia 

7 p.m., October 22, 2025, Virtual 

In an era where AI increasingly ingests and models massive amounts of social data—which are often both more diverse and still insufficiently so—there is growing recognition of the potential for a truly symbiotic relationship between AI and sociology. This talk explores how those developing AI models and those developing sociological theory can enhance one another, drawing upon Dr. Nelson’s own work, which spans computational methods (text analysis, NLP, machine learning, network analysis) applied to issues of gender, social movements, culture, organizations, and institutions. 

The talk will highlight four key intersections where sociology and AI can mutually inform and transform each other: 

1. Evaluating social theories: AI tools can provide evidence for—or challenge—existing sociological hypotheses about social structures, but only if AI is capable of sufficiently accurate modeling of human behavior. 

2. Refining sociological and AI frameworks: Machine learning methods can push sociologists to clarify conceptual categories, while sociologists can urge AI developers to embed more explicit social, cultural, and ethical evaluations in their models. 

3. Enhancing qualitative coding: Generative AI has great promise as a qualitative coding interlocutor—but that promise can be realized only if AI output is sensitive to cultural, historical, and intersectional context. 

4. Innovating data collection: Technologies like conversational AI can transform how we conduct interviews or surveys—but only if they’re designed with cultural sensitivity and awareness of power, context, and identity. 

Rather than seeing AI merely as a tool to scale sociological work—or viewing sociology as merely an ethical or normative check on AI—this talk argues for deeper integration. In this vision, sociological principles help reshape the goals, methods, and values of AI research. Achieving this, however, requires new institutional processes, transdisciplinary collaboration, and a radical rethinking of academic forms. With her background across computational and qualitative paradigms, and her substantive work in gender, intersectionality, and social movements, Dr. Nelson is uniquely positioned to map the terrain for this transformative partnership. 

This is an RSVP-only virtual event. A Zoom link will be sent to the registered participants on the day of the presentation. 

Sincerely, 

Lei “Tommy” Xie 

Director, Fredrickson Family Innovation Lab