Sessions
What’s Coming
Associate Professor, Keough School of Global Affairs, University of Notre Dame
Using Agentic AI to Improve Your Public Engagement as a Researcher
Most academics want their research to matter beyond the academy, but few have time to translate their work for broader audiences. The incentives reward peer-reviewed publications, not public engagement. In this session, I describe my experience using agentic AI to bridge that gap: building a multilingual public-facing academic website, writing pitches, prepping for media interviews and podcasts, drafting policy briefs and social media posts from academic papers, evaluating public engagement opportunities, and brainstorming how to frame research for different audiences. I will focus on practical workflows, and share what worked and what didn't.
Postdoctoral Researcher, Nonviolent Action Lab, Harvard Kennedy School
Learning with AI
In the AI era, you can outsource coding, writing, or even part of thinking, but not the deeper work of understanding. As academia continues to define what responsible AI-powered research should look like, one principle should remain essential: researchers must understand the products, processes, and limits of the tools they use. Correspondingly, the ability to expand one's knowledge boundaries and learn effectively with AI becomes an important skill for modern researchers. In this talk, Jason will share a few examples from his own ongoing experiments with AI-assisted workflows. Through these examples, he hopes to reflect on what research and learning with AI might look like in practice, including the opportunities it creates, the limits it reveals, and the questions it raises along the way.
Assistant Professor, International Relations and Korean Studies, Leiden University
From Pixels to Patterns: Vision-Language OCR and LLM-Based Text Analysis
Drawing on ongoing research, Steven will walk through a practical pipeline for turning images of text into analyzable data — first using multi-modal and vision-language models to perform OCR, then applying LLM-based reading and classification to extract structure and meaning. The session will cover when vision-language OCR outperforms traditional approaches, how to design prompts and validation steps for downstream text analysis, and what these methods open up for researchers working with archival, multilingual, or otherwise difficult corpora.
Associate Professor, Department of Economics and Department of International Affairs, University of Georgia
AI Is Not an Algorithm: What That Means for Research Practice
Generative AI is not an algorithm. Identical inputs produce slightly different outputs across sessions, which makes AI supervision in research qualitatively different from (and harder than) code review. It also reframes what gets called “AI slop” as miscalibration on a consistency–creativity dial: AI was either too creative where faithful protocol execution was needed, or not creative enough to add anything beyond the obvious. The real research skill is deciding, task by task, where on that dial you want the tool to operate and configuring it accordingly. Laura will present this framework through four concrete research experiences: a 19-volume biographical OCR pipeline where AI would not consistently self-apply its own quality protocols; a two-hour methods discussion with a PhD student reprocessed into a working memo; a dictation-plus-vault-grounding-plus-Claude-Cowork loop in Obsidian that neither LLM Wiki patterns nor standard agentic workflows quite capture; and an R standard-error episode where an unusual package choice produced the wrong statistical conclusion. The talk complements the opening DAAF session: where DAAF formalizes the algorithmic end of the dial for data analysis, Laura argues for the fuller taxonomy research work can also benefit from.
University of Southern California
Tracing AI Assistance and AI Agents in Survey Research
Identifying traces of AI in survey research, and using AI agents to test vulnerabilities of surveys before fielding experiment.
Toronto Metropolitan University
Ethical, Effective, and Transparent Workflows: How to Set Clear Guidelines Surrounding AI Usage for Academic Research
AI poses existential questions for professionals in all fields to a large extent because clear rules and guidelines surrounding ethical AI have not been created yet. How do we set such rules and guidelines? Concerns and controversies about AI usage are rooted in ambiguities about the relationship between human principals and AI agents, and the key is to encourage transparent usage that clarifies this principal-agent relationship. Like data-manipulation computational tools such as R or Stata, AI agents are research tools that require training to use ethically and effectively by human researchers who direct the research process. By proactively establishing this principal-agent relationship through best practices devised through transparent experimentation, academic institutions can effectively mitigate the risks as well as seize opportunities offered by AI tools.
Dina Pisareva
Nazarbayev University
AI as a Cognitive Partner in Research and Teaching
What changes when AI is treated not as a shortcut or a tool, but as a genuine cognitive partner — something scholars and students think with rather than merely through? Dina is building a practice around AI-augmented social science and developing some of the first AI-integrated methods courses in the field. In this session, she will share what that partnership looks like across research workflows and the classroom: how it reshapes the questions we ask, what it demands of graduate training, and why she sees the terrain as wide open, with most of the interesting questions still unasked.