Work

Engaging with AI in Crowdsourced Digitization of Ancient Texts: User Perception and Interaction

Year: 2025

Type: article

Abstract: ABSTRACT This paper explores how users perceive and interact with AI in a crowdsourcing ancient texts digitization project. We conducted semi‐structured interviews with 28 participants from the ancient text digitization crowdsourcing project “I am a Collator of Ancient Texts”. Participants viewed AI recognition as accurate and efficient but noted its limits with deteriorated text quality and complex layouts. We found that AI misidentification or failure to provide alternative character suggestions may reduce users' task completion efficiency or even influence their task selection. A key theme emerging from the study is user trust development when interacting with AI—despite initial skepticism, trust in AI technologies gradually increased with positive interaction experiences, leading users to adjust their validation strategies. We also observed that participants acquired knowledge from AI recognition and suggestion results, but could also be misled when AI made errors. This study contributes to understanding human‐AI collaboration in crowdsourced cultural heritage digitization and suggests that future platforms should provide customizable confidence indicators, clearer AI explanations, and learning supports to accommodate diverse users.

Source: Proceedings of the Association for Information Science and Technology

DOI: 10.1002/pra2.1352

Authors: Chenyu Zhang, Wenqi Li, Zeyang Luo, Pengyi Zhang

Institution: Peking University

Language: English

Topic: Digital Humanities and Scholarship

Subfield: Literature and Literary Theory

Field: Arts and Humanities

Domain: Social Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Peking University

Awards: G2772652594, G4665483522