Paper Reading AI Learner

The collective use and evaluation of generative AI tools in digital humanities research: Survey-based results

2024-04-18 18:33:00
Meredith Dedema, Rongqian Ma

Abstract

The advent of generative artificial intelligence (GenAI) technologies has revolutionized research, with significant implications for Digital Humanities (DH), a field inherently intertwined with technological progress. This article investigates how digital humanities scholars adopt, practice, as well as critically evaluate, GenAI technologies such as ChatGPT in the research process. Drawing on 76 responses collected from an international survey study, we explored digital humanities scholars' rationale for GenAI adoption in research, identified specific use cases and practices of using GenAI to support various DH research tasks, and analyzed scholars' collective perceptions of GenAI's benefits, risks, and impact on DH research. The survey results suggest that DH research communities hold divisive sentiments towards the value of GenAI in DH scholarship, whereas the actual usage diversifies among individuals and across research tasks. Our survey-based analysis has the potential to serve as a basis for further empirical research on the impact of GenAI on the evolution of DH scholarship.

Abstract (translated)

生成人工智能(GenAI)技术的出现已经彻底颠覆了研究,对数字人文主义(DH)领域产生了重大影响,这个领域与技术进步密不可分。本文调查了数字人文主义者如何在其研究过程中采用、实践以及批判性评估ChatGPT等GenAI技术。通过收集来自国际调查研究的76个回答,我们探讨了数字人文主义者对GenAI在DH研究中的采用原因,确定了使用GenAI支持各种DH研究任务的特定用法和实践,并分析了学者们对GenAI的益处、风险以及对DH研究的影响。调查结果表明,DH研究社区对GenAI在DH研究中的价值持有分歧意见,而实际使用情况则存在个体差异和研究任务之间的差异。基于调查的分析有可能成为进一步研究GenAI对DH研究演变影响的实证研究的依据。

URL

https://arxiv.org/abs/2404.12458

PDF

https://arxiv.org/pdf/2404.12458.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot