Research Article 6
Journal of Education & Social Sciences - Volume 14, Issue 1 2026
By Dr. Roomi S. Hayat
DOI: 10.20547/jess1412614101
Keywords: Artificial Intelligence; Anthropology; Anthropology Education; Ethnographic Research; Large Language Models; Qualitative Research; Research Ethics; AI-Assisted Research; Data Governance; Pedagogy

Artificial intelligence (AI) is increasingly embedded in higher education, including research, transcription, analysis, and academic writing. These developments create distinctive opportunities and challenges for anthropology, a discipline grounded in long-term engagement, interpretive reflexivity, thick description, and ethical accountability to research participants. Unlike fields oriented primarily toward prediction or optimization, anthropology produces knowledge through situated relationships and contextual interpretation. The growing use of AI, particularly large language models (LLMs), therefore raises important questions about how these technologies can be integrated without undermining the discipline’s epistemic and ethical commitments. Using an integrative literature review with a structured search and screening process, the study synthesizes 21 sources addressing the pedagogical, methodological, and ethical dimensions of AI in anthropology and related qualitative research. Drawing on this evidence base, the paper conceptualizes AI as a methodological and pedagogical scaffold for enriching anthropology education and research rather than as a substitute for ethnographic practice. It develops an integrative framework for responsible AI use across the anthropological research lifecycle. The framework encompasses five stages of anthropological practice: ethnographic preparation and research design; fieldwork documentation, transcription, and translation; qualitative analysis and thematic development; multimodal interpretation and representation; and scholarly writing and communication. Across these stages, AI is positioned as an assistive resource for documentation, organization, exploratory analysis, and reflexive comparison, while interpretive authority remains with the researcher. The paper also approaches AI as a sociotechnical phenomenon embedded in relations of labor, power, and value extraction, thereby positioning AI itself as a legitimate object of ethnographic inquiry. The paper further proposes pedagogical strategies that prioritize process over product, including transparent documentation of AI use, critical verification of AI-generated outputs, and assessment practices that make interpretive reasoning visible. It concludes that AI can meaningfully enrich anthropology when it expands analytical possibilities without displacing contextual interpretation, ethical responsibility, and sustained engagement with research participants and communities.

Submission Date: 3 Apr, 2026 Reviews Completed: 21 May, 2026
Acceptance Date: 10 Jun, 2026 Publication Date: 30 Jun, 2026