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A Simple AI-Powered Workflow for Literature Review

Written by Theophilus Aidoo Doing a literature review can be quite painful , I think many students can relate. Over time, I ha ve learned a few things that I woul d like to share here. Often, we do n o t struggle because we do no t know what to do, but because we do no t have a procedure or system for finding relevant research. Below is a simple workflow showing how I use AI tools to support my literature review process. 1. Clarifying the Research Direction I start by giving my research idea (even if it’s rough) to Perplexity AI. I ask it to help identify: The main research topic Related subtopics Possible research directions based on the text This helps me better frame my problem and understand how it fits into existing research. 2. Generating Keywords and Search Queries Once I have these topic areas, I pass them to ChatGPT (or any free chat-based AI tool). I ask it to: Generate relevant keywords Construct advanced search queries These queries are tailored for academic d...
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Wednesday Team Presentation: Brief Intro to Causal Analysis

  Hey everyone, it is Wednesday again and since a few people in the team and many students are actually dealing with some kind of impact analysis, I thought it would good to get a brief introduction to causal analysis. Taking a sniff. Below the slides. Some elephant missing in the room? Let me know. Best, Till
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I2SC Lecture Series (Recording): Ruta Binkyte (AI Fairness & Privacy, CISPA Helmholtz Center for Information Security) From Humans to Machines and Back: Fairness, Causality, and the Role of Social Science

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Date : January 9, 2026 Abstract : Fairness in machine learning is not only a technical property of algorithms but a deeply social question: who benefits, who is harmed, and whose perspectives are embedded in our systems. While computational methods can surface biases and propose adjustments, addressing fairness requires more than optimization. It demands an understanding of social structures, power dynamics, and human behavior—areas where social science offers critical background knowledge, especially when applying causal approaches that aim to explain and intervene. As we move into the era of large language models (LLMs) and increasingly agentic AI systems, these challenges grow more complex. Fairness concerns now span not just data and predictions, but dialogue, reasoning, and decision-making processes in systems that simulate human-like behavior. Traditional methods fall short, and novel approaches—bridging mechanistic insights from computer science with behavioral perspectives from...
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Blog: Navigating the New Frontlines: A Personal Journey Into Information Warfare

By Jhon Raza, Interdisciplinary Institute for Social Computing (I2SC)   When I first picked “Information Warfare and the New Geopolitics” as my presentation topic, I thought I knew what I was getting myself into. After all, I work with data every day. I spend my time analyzing patterns, untangling networks, and trying to understand how information moves through digital spaces. But somewhere along the research journey, the topic stopped being an academic exercise and started feeling... personal. The Moment It Hit Me It happened late one night, while I was tracing how a piece of disinformation spread across multiple platforms in a matter of hours. I watched the nodes bloom on my screen — hundreds, then thousands — each one representing a share, a like, a retweet, a comment. But what struck me wasn’t the speed. It was the intention . Real people were interacting with fabricated content, absorbing it, passing it forward, weaving it into their worldview without ever realizing it was c...
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Blog: The Politics of Digital Forest Restoration

In the urgent race to heal our planet, digital tools from satellite mapping to one-click tree-planting apps are hailed as game-changers. They promise the efficiency and scale needed to meet colossal goals like the UN Decade on Ecosystem Restoration. However, a critical examination reveals a more complex story. A recent study in Environmental Politics (Urzedo et al., 2023) argues that these platforms are not neutral tools ; they are power-laden processes that actively reshape restoration, often deepening global inequalities. The digitalization of restoration is a profound struggle over who controls knowledge, money, and story. The "View from Above": When Maps Erase People The first driver uses scientific expertise to optimize land selection. Platforms like the Atlas of Forest and Landscape Restoration Opportunities use satellite data to label sparsely populated areas as prime "restoration opportunities." But this "view from above" is dangerously simplis...
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Blog: Reappropriation and when is it appropriate?

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You know those words you can barely say out loud without people shooting you dirty looks? Ever wonder how they got that way? At some point, they were thrown around with openly hostile intent, and over time, their meaning either shifted or the words themselves became socially off-limits. This process is called reappropriation , reclamation, or resignification, where a group reclaims words or artifacts that were previously used in a way disparaging of that group. This can happen through Value Reversal (changing the meaning from pejorative to positive), Neutralization (changing the meaning from pejorative to neutral), or Stigma Exploitation (retaining the derogatory nature as a reminder of the unfair treatment). Well-known examples of this include the N-word (in African-American communities) and the F-word (in LGBTQIA+ spaces). But this isn’t a new phenomenon. One of the earliest famous examples is actually Impressionism: when Monet and others first developed the style, “Impressionist” wa...
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I2SC Lecture Series (Recording): Christof Schöch (Digital Humanities, University of Trier) Machine Learning and Linked Open Data for Literary History

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Date : November 28, 2025 Abstract : This talk presents an innovative approach to literary history that integrates machine learning (ML) with linked open data (LOD), demonstrating how computational methods can expand the scope of traditional humanities research. The approach was developed in the Mining and Modeling Text project at the Trier Center for Digital Humanities and leverages three complementary data sources: bibliographic metadata from the Bibliographie du genre romanesque français, a corpus of 200 XML-TEI encoded French novels (1750–1800), and secondary scholarship on the Enlightenment novel. Using ML techniques such as topic modeling and named entity recognition, we extract structured information and represent it as LOD triples in a public Wikibase instance. The resulting semantic knowledge graph enables complex querying across heterogeneous data, uncovering large-scale patterns and trends in 18th-century French literature. Beyond the case study, the talk highlights the broad...
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