Large Language Models (LLMs) for Humanities Research Training Course

Large Language Models (LLMs) for Humanities Research Training Course

Introduction

The rapid evolution of Artificial Intelligence (AI) and Large Language Models (LLMs) is transforming the landscape of humanities research, enabling scholars to analyze texts, generate insights, and uncover patterns at unprecedented scales. Leveraging state-of-the-art LLMs, such as GPT, BERT, and RoBERTa, researchers can enhance text mining, sentiment analysis, and semantic interpretation, bridging traditional humanities methods with cutting-edge computational linguistics. Large Language Models (LLMs) for Humanities Research Training Course empowers participants to integrate AI-driven methodologies into historical analysis, literary studies, philosophy, linguistics, and cultural research, creating innovative, data-informed scholarship.

Participants will gain hands-on expertise in applying LLMs for tasks including digital archiving, thematic mapping, authorship attribution, and cross-cultural textual analysis. By combining practical coding exercises, case studies, and collaborative workshops, this training ensures learners can deploy AI solutions ethically and effectively, while exploring emerging trends in NLP, knowledge representation, and data-driven humanities research. This course positions researchers at the forefront of AI-enhanced scholarship, enhancing productivity, creativity, and analytical depth in the humanities.

Course Duration

5 days

Course Objectives

1. Understand the fundamentals of Large Language Models (LLMs) and their applications in humanities research.
2. Apply Natural Language Processing (NLP) techniques to literary, historical, and cultural texts.
3. Perform text mining and semantic analysis for research insights.
4. Implement AI-driven authorship attribution in literary studies.
5. Conduct historical trend analysis using large text corpora.
6. Explore cross-cultural and multilingual text analysis with LLMs.
7. Integrate digital humanities tools with AI-based workflows.
8. Develop interactive dashboards and visualizations for textual data.
9. Evaluate LLM outputs ethically and understand bias and fairness in AI.
10. Build custom NLP pipelines for humanities research projects.
11. Apply sentiment analysis and emotion detection to historical and literary texts.
12. Analyze networked knowledge structures through graph-based AI models.
13. Produce research-ready reports combining AI outputs with humanistic interpretation.

Target Audience

1. Humanities researchers and scholars
2. Graduate students in literature, history, or philosophy
3. Digital humanities practitioners
4. Linguists and language researchers
5. Archivists and librarians
6. Cultural analysts and social historians
7. Data scientists with interest in humanities applications
8. AI enthusiasts exploring interdisciplinary applications

Course Modules

Module 1: Introduction to LLMs and NLP in Humanities

• Overview of LLMs
• Fundamentals of Natural Language Processing (NLP)
• AI-driven approaches in textual analysis
• Ethical considerations in AI for humanities
• Case Study: AI-assisted literary analysis of Shakespearean texts

Module 2: Text Mining and Semantic Analysis

• Tokenization, embedding, and vectorization of texts
• Topic modeling and semantic clustering
• Detecting patterns in large historical archives
• Evaluating semantic similarity and coherence
• Case Study: Semantic mapping of 19th-century newspapers

Module 3: Authorship Attribution and Stylometry

• Introduction to computational authorship analysis
• Feature extraction from literary works
• LLM-based style and pattern recognition
• Comparative analysis across multiple authors
• Case Study: Determining authorship of disputed manuscripts

Module 4: Multilingual and Cross-Cultural Analysis

• NLP for multilingual corpora
• Cultural and contextual text interpretation
• Translation models and semantic preservation
• Cross-lingual topic extraction
• Case Study: Mapping global literary trends using AI

Module 5: Sentiment and Emotion Analysis

• Sentiment detection in historical texts
• Emotion classification using LLMs
• Analyzing societal trends via text sentiment
• Visualizing emotional trajectories in narratives
• Case Study: Analyzing public sentiment in historical letters

Module 6: Digital Humanities and AI Integration

• Digital archiving and text digitization
• AI-assisted metadata creation
• Combining databases with NLP models
• Interactive dashboards for textual exploration
• Case Study: Digital reconstruction of ancient manuscripts

Module 7: Ethical AI and Bias in Humanities Research

• Recognizing bias in AI-generated content
• Fairness and transparency in LLM applications
• Responsible use of AI in cultural studies
• Human-in-the-loop approaches for validation
• Case Study: Mitigating bias in historical dataset analysis

Module 8: Practical Applications and Research Project

• Designing custom NLP pipelines
• Integrating LLM outputs with humanistic interpretation
• Collaborative project-based learning
• Writing research-ready AI-assisted reports
• Case Study: Comprehensive analysis of literary movements using AI

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.