AI as a Research Partner
in Byzantine Studies
Postgraduate Programme — Level 7
LEARNING OUTCOMES
Upon completion, students will be able to:
1)
Use AI tools as bounded research support partners while maintaining scholarly responsibility
2)
Design effective prompts for source exploration, summarisation, multilingual triage, and lesson planning
3)
Locate, compare, and verify scholarly material produced or retrieved with AI tools
4)
Create AI-assisted educational resources adapted to Byzantine Studies topics
5)
Evaluate AI outputs for accuracy, bias, anachronism, and hallucinated references
6)
Apply usability and learner-experience evaluation principles to AI-supported outputs
7)
Document AI use transparently through prompt logs and source-verification notes
8)
Design and present a complete AI-assisted research or teaching workflow
COURSE SYLLABUS
13 Modules
Week 01 | AI as a Research and Teaching Partner
Introduction to AI tools as assistants in Byzantine Studies, humanities research, and education. Students map research, teaching, and communication workflows, distinguishing support from automation, and begin a reflective AI-use log.
Week 02 | Prompt Literacy for Humanities Research
Prompt design as a scholarly skill. Practise prompts for explanation, summarisation, comparison, classification, research-question refinement, and cautious historical interpretation, with emphasis on iteration and evidence.
Week 03 | AI-Assisted Scholarly Search and Source Verification
Use of AI-assisted tools to locate literature, identify keywords, and navigate unfamiliar fields, compared with traditional databases. Emphasis on citation verification, fabricated references, and triangulation.
Week 04 | Reading, Summarising, and Navigating Multilingual Scholarship
Use of AI tools for first-pass reading, argument mapping, glossary building, and translation support. Students verify outputs against original sources, especially for Byzantine Greek, Latin, and specialist terminology.
Week 05 | Evaluating AI Output in Humanities Research
Introduction to precision, recall, hallucination, unsupported inference, and domain drift. Students apply a simple evaluation protocol to decide when AI outputs can be used, checked, or rejected.
Week 06 | When AI Gets Byzantine Studies Wrong
Guest session applying the evaluation framework to AI failures in texts, images, and structured reasoning — fabricated manuscripts, iconographic errors, invented artifact details, and implausible prosopographical links.
Week 07 | Designing Educational Content with AI Tools
AI-supported creation of lesson plans, learning objectives, 5E structures, rubrics, quizzes, worksheets, and explanatory texts for Byzantine history, art, archaeology, and cultural heritage.
Week 08 | Presentations, Posters, and Visual Communication
Use of AI-supported tools for teaching slides, conference-style presentations, outreach posters, and public-facing summaries for complex Byzantine Studies content.
Week 09 | Generative Media for Byzantine Heritage
Introductory use of text-to-image, text-to-speech, and text-to-video tools for cultural heritage communication, focusing on historical accuracy, anachronism, copyright, and responsible labelling.
Week 10 | Usability, Accessibility, and Learner Experience
Basic usability and accessibility principles for AI-supported educational resources. Students evaluate a learning object using a SUS-inspired checklist, inclusive design criteria, and peer feedback.
Week 11 | AI-Supported Research Design and Data Collection Planning
Use of AI tools to refine research questions, hypotheses, interview guides, questionnaires, coding categories, and analysis plans for humanities education projects.
Week 12 | Building a Documented AI-Assisted Research Dossier
Students assemble a research-support dossier including research question, verified bibliography, source summaries, glossary, prompt log, and tool-use statement on a Byzantine Studies topic.
Week 13 | Ethics, Law, Academic Integrity, and Responsible AI Use
Academic integrity, disclosure of AI assistance, copyright, licensing, privacy, bias, and EU/UNESCO policy context for AI in education and research. Students draft an AI-use statement.
ASSESSMENT
Student Evaluation
40%
Writtent Assignments
Weekly practical tasks & reflections
40%
Final Project
AI-assisted research dossier
20%
Oral Presentation
Final presentation & participation
Workload — ECTS Distribution
250 Hours Total
Lectures
39
Guided lab practice with web-based AI tools
36
Weekly practical assignments
45
40
55
35
Course Total
250
Recommended Bibliography
Suggested bibliography:
- Mitchell, M. Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux, 2019.
- Russell, S. Human Compatible: Artificial Intelligence and the Problem of Control. Viking, 2019.
- Kelleher, J. D. Deep Learning. MIT Press, 2019.
- Karsdorp, F., Kestemont, M., and Riddell, A. Humanities Data Analysis: Case Studies with Python. Princeton University Press, 2021.
- Flanders, J., and Jannidis, F. (eds.). The Shape of Data in Digital Humanities. Routledge, 2019.
- Baca, M. (ed.). Introduction to Metadata. Getty Research Institute, selected chapters.
- The Programming Historian. Selected beginner-friendly lessons on data literacy, OCR, text analysis, and digital humanities methods.
- European Commission. Ethics Guidelines for Trustworthy AI, 2019.
- UNESCO. Recommendation on the Ethics of Artificial Intelligence, 2021.
Related academic journals:
- International Journal of Digital Humanities (Springer)
- Journal on Computing and Cultural Heritage (ACM)
- Digital Scholarship in the Humanities (Oxford University Press)
- Journal of Open Humanities Data
- Journal of Cultural Heritage