Gen AI Tools for Humanities Research
Artificial intelligence has become a tool across the humanities, enhancing traditional research methods in disciplines such as history, literature, linguistics, philosophy, art history, archaeology, and cultural studies.
Disclaimer: ALWAYS consider the data and materials you are feeding into GenAI. Is the material under copyright? Unpublished? Behind FERPA or HIPAA protections? Assume all data that you are entering is being accessed and kept by GenAI, even if the application says otherwise. One way around this is to install a Local LLM.
AI-driven text analysis has opened new avenues in the field of literature and literary studies:
Text mining and distant reading: Tools like Voyant or large language models allow researchers to examine thousands of texts at once, identifying trends in themes, genres, or linguistic styles that would be impossible to see through close reading alone.
Sentiment and topic modeling: Natural Language Processing (NLP) techniques help scholars trace how emotional tone or dominant topics evolve across time periods, genres, or authors.
For history and archival research, AI can help support historians in preserving, analyzing, and interpreting historical data:
Digitization and OCR (Optical Character Recognition): AI helps clean and transcribe old manuscripts, newspapers, and handwritten documents, making archives searchable and accessible.
Pattern detection in large datasets: Machine learning can identify social, political, or migration trends by analyzing census records, maps, and letters, or map the movement of people or ideas across regions using geographical data and text analysis.
Image recognition for visual archives: AI can categorize and identify faces, objects, or locations in historical photographs. This can also be applied to audio archives, where AI can help transcribe audio recordings of speeches or interviews.
Linguistics has offered a lot to AI, particularly with regards to Large Language Models, but there has been greater exploration into what AI can offer linguistics and language studies in return:
Corpus linguistics: Machine learning helps analyze billions of words from digital corpora to study language change, dialect variation, or semantic evolution.
Speech analysis: AI models analyze phonetic data to study accents, tone, and language acquisition using Automatic Speech Recognition (ASR) tools like Whisper.
Endangered languages: AI tools assist in documenting, transcribing, and revitalizing languages at risk of extinction.
AI offers new methods for analyzing and preserving art and visual heritage:
Restoration and reconstruction: AI assists in digitally reconstructing damaged artworks or lost architectural structures.
AI also helps researchers uncover and interpret material culture in the fields of archaeology and anthropology:
Predictive modeling: Machine learning predicts where archaeological sites may be located based on terrain, climate, and historical data.
Remote sensing and satellite imagery: AI analyzes aerial or Light Detection and Ranging (LIDAR) data to detect buried structures or landscape changes.
Artifact classification: Computer vision automates the sorting and categorization of pottery, tools, or inscriptions.
AI has also reshaped how scholars interpret modern culture and media:
Social media and cultural trends: NLP and sentiment analysis reveal how ideas, memes, or ideologies spread across digital platforms.
Bias and representation studies: AI helps analyze patterns of inclusion, exclusion, and stereotype across texts, films, and digital spaces.
AI also provokes new theoretical questions within the humanities:
Ethics of automation and creativity: Philosophers explore questions about AI’s moral status, accountability, and the nature of intelligence.
Critical AI studies: Researchers analyze how bias, representation, and power are embedded in AI systems, connecting digital technologies to broader social and cultural critique.
Across the humanities, AI functions as both a research tool and an object of study. It enables large-scale analysis of texts, images, and artifacts, but also raises profound questions about meaning, interpretation, and the nature of human creativity. The most successful uses of AI in the humanities are collaborative—combining computational power with the nuanced critical judgment that defines humanistic inquiry.
Further resources:
https://infoguides.gmu.edu/digitalhumanities/artificialintelligence from George Mason University Libraries
https://libguides.usc.edu/c.php?g=1444299&p=10730256 from USC Libraries
https://cdh.princeton.edu/programs/humanities-for-ai/ from the Center for Digital Humanities @ Princeton