A research team led by Nagaland University has published a study in Acta Innovations that examines how artificial intelligence (AI) can aid in preserving India’s cultural heritage. The study involves scholars from various Indian institutions across Tamil Nadu, Delhi, Uttar Pradesh, Telangana, and Karnataka.
Study Focus and Framework
The research focuses on endangered cultural materials such as manuscripts, languages, oral traditions, and historical records at risk due to aging or damage. The team developed a comprehensive framework to evaluate AI tools’ effectiveness in preservation, emphasizing not just digitisation but maintaining historical accuracy, cultural context, and deeper meaning.
The framework assesses AI applications across nine areas: digitisation accuracy, text recognition, classification, translation, visual reconstruction, descriptive information, cultural context, accessibility, and ethics. It integrates technical assessments with expert and community reviews to ensure cultural preservation respects tradition and ownership rights.
Key Research Insights
The study identifies five main areas where AI can contribute: printed and handwritten documents, photographs and artworks, oral histories and endangered languages, archaeological and architectural heritage, and folklore and community knowledge.
Potential AI applications include making documents searchable, organising image collections, identifying damage, transcribing oral recordings, translating materials, and creating digital maps and 3D models of heritage sites.
The researchers highlight the importance of distinguishing restoration from reconstruction, advocating for retaining original digital files, documenting AI-generated edits, and clearly marking uncertain interpretations.
NIRF 151-200Nagaland UniversityLumami, NagalandAdmission: CUET UG, CUET PG, NET/JRFEst. 1994View details
NAAC A++
NIRF #2 (2025)Jawaharlal Nehru UniversityNew Delhi, DelhiAdmission: CUET UG, CUET PG, NET/JRFEst. 1969View details
NAAC A++
NIRF #4 (2025)Jamia Millia IslamiaNew Delhi, DelhiAdmission: CUET UG, CUET PG, NET/JRFEst. 1920 · 239 acresView details
NAAC A++
NIRF #5 (2025)University of DelhiNew Delhi, DelhiAdmission: CUET UG, CUET PG, JEE MainEst. 1922View details
NAAC A
NIRF #6 (2025)Banaras Hindu UniversityVaranasi, Uttar PradeshAdmission: CUET UG, CUET PG, NET/JRFEst. 1916View details
NAAC A+
NIRF #10 (2025)Aligarh Muslim UniversityAligarh, Uttar PradeshAdmission: CUET UG, CUET PG, NET/JRFEst. 1920View details
NIRF #14 (2025)Dr. Rajendra Prasad Central Agriculture UniversityPusa, Samastipur, BiharAdmission: CUET UG (through ICAR UG counselling), ICAR AIEEA PG, GAT-B (eligible biotechnology programme)Est. 1905 · 2063 acresView detailsUOHNAAC A++
NIRF #18 (2025)University of HyderabadHyderabad, TelanganaAdmission: CUET UG, CUET PG, NET/JRFEst. 1974View details
NAAC A
NIRF #37 (2025)Babasaheb Bhimrao Ambedkar UniversityLucknow, Uttar PradeshAdmission: CUET UG, CUET PG, NET/JRFEst. 1996View detailsCUPNAAC A
NIRF #77 (2025)Central University of PunjabBathinda, PunjabAdmission: CUET UG, CUET PG, NET/JRFEst. 2009View detailsCommunity and Ethical Considerations
Prof Ravi Ranjan Kumar and the team stress that AI should complement, not replace, community involvement and expert understanding. The framework calls for guidelines on data use, privacy, and access, emphasizing the inclusion of heritage communities in decision-making.
Implications for India
The research is particularly relevant to India’s diverse cultural record, including regional languages and oral traditions. It may assist in preserving endangered languages and documenting oral histories, especially in the Northeast, with appropriate tools and community participation.
Such AI-assisted preservation can enhance access for researchers and students while protecting fragile originals in libraries, museums, universities, and archives.
Future Directions
The authors recommend piloting the framework with different collections, scripts, and dialects, with ongoing documentation of errors and integration of community feedback to determine the appropriateness of AI-generated outputs.
