AI Resources for Humanities Academics¶

Especially classics, ancient history, archaeology, religion & theology

This list is for "regular" humanities academics (teaching + research), not primarily digital humanists. It prioritises: (1) what you need for next term, (2) research integrity and disclosure, (3) source-facing scholarship where AI actually changes things, and only then (4) deeper reading. It is deliberately short. A longer appendix is a different document with a different job.

1. If you teach next term: sector guidance and usable policy¶

UNESCO, Guidance for Generative AI in Education and Research (2023) --- Global baseline; useful in policy discussions and for framing institutional rationale. unesco.org/en/articles/guidance-generative-ai-education-and-research
QAA, Generative AI resources hub --- Advice on generative AI and academic standards: assessment redesign, integrity, quality assurance framing. Start with Maintaining Quality and Standards in the ChatGPT Era and Reconsidering Assessment for the ChatGPT Era. qaa.ac.uk/membership/membership-areas-of-work/academic-integrity/generative-artificial-intelligence
Jisc, Generative AI --- a Primer (updated quarterly) --- Practical briefing on generative AI in tertiary education. Good for "what do we actually need to know?" Pair with Jisc's AI in Tertiary Education report for use cases and ethical considerations. jisc.ac.uk/reports/generative-ai-a-primer
Russell Group, Principles on the Use of Generative AI Tools in Education (July 2023) --- Five concise principles (AI literacy, assessment integrity, equitable access, rigour, collaboration). Useful as a concise institutional stance even outside Russell Group. russellgroup.ac.uk/media/6137/rg_ai_principles-final.pdf

2. If you supervise or publish: integrity, disclosure, and citation¶

COPE, Authorship and AI Tools (2023) --- AI cannot be listed as author; authors must transparently disclose AI use in Methods or equivalent. The baseline standard most publishers now reference. publicationethics.org/guidance/cope-position/authorship-and-ai-tools
UKRIO, Embracing AI with Integrity --- Research integrity guidance from the UK Research Integrity Office. Practical, concise, and directly relevant to funder expectations. ukrio.org/activities/embracing-ai-with-integrity/
AHA, Guiding Principles for Artificial Intelligence in History Education (July 2025) --- Broader than citation: covers AI literacy, transparent syllabus policies, assessment design, and pedagogical values. Essential reading for historians and classicists. historians.org/resource/guiding-principles-for-artificial-intelligence-in-history-education/
MLA Style Center, How Do I Cite Generative AI in MLA Style? (updated August 2025) --- Practical citation format for AI-generated text. Useful for literature, religion, and student-facing guidance. style.mla.org/citing-generative-ai-updated-revised/
Chicago Manual of Style, Citing Content Generated by AI --- Chicago/Turabian citation format for AI. Most historians and many classicists use Chicago; don't default to MLA. chicagomanualofstyle.org/qanda/data/faq/topics/Documentation/faq0422.html

3. Discipline-specific resources¶

For source-facing AI tools and discipline-specific pedagogy, see the relevant Field Guides pathway pages:

Classics & Ancient History --- inscription tools (Ithaca, Aeneas), HTR/OCR, and classics-specific teaching resources
History --- archival tools, HTR/OCR, and history-specific pedagogy
Archaeology --- spatial tools, material culture, and archaeological AI resources
Theology & Religious Studies --- faith-based pedagogy and scriptural language tools

4. Books and edited volumes (for depth, framing, and critique)¶

Nina Begus, Artificial Humanities (University of Michigan Press, 2025) --- AI as a lens for humanistic inquiry: language, creativity, ethics, epistemology. Good for seminars and reading groups.
Hannes Bajohr (ed.), Thinking with AI: Machine Learning the Humanities (Open Humanities Press, 2025) --- Treats machine learning as a conceptual catalyst. More theoretically oriented than Begus.
James O'Sullivan (ed.), The Bloomsbury Handbook to the Digital Humanities (2023) --- Dip into algorithmic criticism and ethics chapters selectively. Don't lead with this; it signals "DH specialist" to a general humanities audience.
Kate Crawford, Atlas of AI (2021) --- Political economy and extraction: labour, resources, power. Strong humanities fit; widely taught.
Frank Pasquale, The Black Box Society (2015) --- Algorithmic secrecy and institutional opacity. Still hugely teachable.
Cathy O'Neil, Weapons of Math Destruction (2016) --- Assignable introduction to algorithmic harms, incentives, and institutional deployment.
Ethan Mollick, Co-Intelligence: Living and Working with AI (2024) --- Practical patterns for using LLMs without losing your epistemic grip: drafting, critique loops, verification habits.

5. Critical literacy and practical workflows¶

Moritz Mahr, Critical AI Literacy for Historians --- Source criticism meets reproducible workflows. Good for methods teaching.
Bender et al., "On the Dangers of Stochastic Parrots" (2021) --- Canonical critique of scale, opacity, and downstream harms. Good for committee conversations and teaching critical evaluation.
Constellate (formerly JSTOR/HathiTrust Digital Scholar Lab) --- NLP and text analysis on licensed corpora without heavy coding. Note: licensing limits and OCR quality vary significantly by corpus. constellate.org

What's not here and why¶

Off the Beaten Track

Cormen, Algorithms Unlocked --- Too technical for this audience. If you're supervising computational projects, you need it; otherwise, skip.
AI detection tools (Turnitin AI detection, GPTZero, etc.) --- QAA and Jisc both advise caution. Don't rely on these.
Comprehensive podcast list --- The landscape moves too fast. If you want one starting point, try Ethan Mollick's writing at oneusefulthing.org and follow what he links.