About
The Full Story
CTextA at the Tel Aviv University Center for Artificial Intelligence and Data Science (TAD) was established to advance computational text analysis in the humanities and social sciences. By connecting disciplinary expertise with computational methods, CTextA aims to help researchers explore new questions, work with diverse textual sources, and develop rigorous approaches to research in the digital age.
Advances in computer science, statistics, computational linguistics, and related fields, together with the growing digitization of textual collections, have transformed the possibilities for text-based research. Computational approaches now support inquiry across history, literature, linguistics, philosophy, religious studies, political science, sociology, economics, and other disciplines, as well as education, social work, the arts, management, and law. These developments are expanding both the scale of research and the kinds of questions scholars can ask.
Researchers can increasingly explore sources that were previously difficult to access, search, or analyze, including historical documents, extensive archival collections, and large bodies of contemporary text. Computational methods enable scholars to move between “close reading”—the detailed interpretation of individual texts—and “distant reading,” which examines patterns across large collections. Applications include investigating authorship, comparing editions, tracing connections and influences across time and place, identifying themes and expressed attitudes, and supporting the analysis of interviews and other qualitative materials. These approaches bring new perspectives to textual evidence while retaining the importance of context and interpretation.
The emergence of large language models (LLMs) and generative artificial intelligence (GenAI) adds a new dimension to this landscape. Alongside established methods of text mining and natural language processing, these technologies can assist with tasks such as annotation, classification, information extraction, translation, and summarization. Natural-language interfaces can also make some computational workflows more accessible to researchers with limited programming experience. When carefully designed and evaluated, these tools can support the exploration of collections, the development of coding schemes, and the iterative refinement of research questions.
These opportunities also raise methodological questions. AI-generated outputs require verification against source materials, and model performance must be evaluated for the language, genre, historical context, and research task at hand. Responsible use calls for attention to bias, transparency, reproducibility, and the protection of sensitive research data. Scholarly expertise remains essential to deciding what to analyze, assessing the validity of computational results, and interpreting their significance. Generative AI is also becoming an object of inquiry in its own right, opening questions about authorship, knowledge production, cultural representation, and human–machine interaction.
This evolving landscape calls for sustained collaboration between researchers in the humanities and social sciences and specialists in AI and data science. Across the international research community, interdisciplinary centers, shared infrastructures, training programs, and scholarly networks support this exchange. Within this broader effort, CTextA at the Tel-Aviv University Center for Artificial Intelligence and Data Science seeks to serve Tel Aviv University’s research community and to combine methodological innovation with critical inquiry, helping scholars make informed and effective use of computational tools in the era of LLMs and generative AI.