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 Is the material copyrighted, unpublished, or protected by FERPA or HIPAA? Assume all entered data is accessed and retained by the AI provider, even if the application states otherwise. One effective mitigation is to install a Local LLM (Large Language Model).
Literature and Literary Studies
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 LLMs 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.
History and Archival Research
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, transforming archives into searchable and accessible databases.
Pattern detection in large datasets: Machine learning can identify social, political, or migration trends by analyzing census records, maps, and correspondences, 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 and Language Studies
While linguistics provided the foundation for LLMs, AI now offers significant reciprocal benefits to the field:
Corpus linguistics: Machine learning helps analyze billions of words from digital corpora to study language change, dialect variation, or semantic evolution.
Speech analysis: Models analyze phonetic data to study accents, tone, and language acquisition using Automatic Speech Recognition (ASR) tools like OpenAI’s Whisper.
Endangered languages: AI tools assist in documenting, transcribing, and revitalizing languages at risk of extinction.
Art History and Visual Heritage
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.
Style and attribution: Algorithms can help identify artistic hands or influences by analyzing brushstrokes and compositional patterns.
Archaeology and Anthropology
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.
Modern Culture and Media Studies
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.
Theoretical and Philosophical Inquiry
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 while also raising 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