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Journal of Library and Information Science in Agriculture

   

Addressing the Knowledge Worker AI Literacy Gap with AI Technology

Shuyi WANG1, Wen ZENG1, Weishi ZHANG2, Junjie LI1   

  1. 1.School of Management, Tianjin Normal University, Tianjin 300387
    2.School of Geography, Tianjin Normal University, Tianjin 300387
  • Received:2024-07-11 Online:2024-11-19

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Abstract:

[Purpose/Significance] With the proliferation of artificial intelligence (AI) technology, knowledge workers' basic understanding of AI and their ability to evaluate its application, known as AI literacy, is particularly important. This study addresses the current state of the AI literacy gap among knowledge workers and explores how AI technology itself can be used to bridge this gap, with the goal of shortening the AI literacy gap. The article analyzes the reasons for the existence of the AI gap in multiple dimensions and the need to bridge the AI gap. In today's era of proliferating AI tools, mastering the use of AI tools and improving AI literacy is the best assistant for knowledge workers. Learning to use AI tools can significantly improve the efficiency of information collection, processing, and storage, and greatly facilitate the building of information resources. [Method/Process] From the dimensions of cognition, practice, and assessment, the study explores how AI technology can assist in enhancing AI literacy. At the cognitive level, the research examines how AI agents can provide personalized knowledge services and help users build a systematic body of knowledge in a particular domain; at the practical level, it analyzes how AI tools can simplify professional tasks such as data analysis, and suggests how to improve knowledge workers' own ways and means of using AI tools through human-computer interaction and mastering effective AI problem-solving aids; at the assessment level, the application of AI tools in verifying the authenticity of information and the importance of the "human-in-the-loop" model in AI applications are discussed, emphasizing the need for human oversight. The article comprehensively draws on the excellent literature on AI, library intelligence, and learning literacy at home and abroad, and combines its own practical situation of using AI tools to achieve AI literacy, which helps knowledge workers establish basic cognition, practical operation ability, and evaluation of answer results. [Results/Conclusions] AI technology has significant advantages in closing the AI literacy gap. AI agents can provide customized knowledge explanation based on user needs, and AI tools can automate the execution of professional tasks and provide factual evidence. These applications not only compensate for the shortcomings of traditional educational methods but also offer new directions for innovation in the field of education. In presenting the arguments, the number of AI tools chosen is small, but fundamentally representative of the problem. Subsequent research will focus on improving AI literacy from other perspectives, and on ways and means to make knowledge workers more comfortable with AI tools. With the continuous development of AI technology, its role in enhancing AI literacy will become more pronounced, contributing to the construction of a more intelligent and efficient information environment.

Key words: knowledge workers, artificial intelligence, AI literacy

CLC Number: 

  • G312

Table 1

Summary of AI literacy issue types in existing research"

归类作者提出的具体问题
认知类SIREGAR K E（2024）现有的教育课程往往未能有效整合AI相关内容，导致学生无法获得必要的知识和技能，许多教育体系的课程设计未能跟上技术发展的步伐，缺乏对AI的系统性教育[26]
认知类SIAHAAN R（2023）数字素养的差距，在提升AI素养的过程中，学生和教育工作者的数字素养水平参差不齐，这种差距直接影响了AI教育的效果。数字素养的提高是实现AI素养提升的前提，但当前的教育体系未能有效解决这一问题[27]
实践类SAPUTRA I（2023）教育机构在整合AI技术时面临的高成本是一个普遍问题，教师在AI相关领域的专业培训不足，导致他们无法有效教授AI相关的技术课程[28]
实践类CASAL-OTERO L（2023）许多教育体系的课程设计未能跟上技术发展的步伐，缺乏对AI的系统性教育[29]
评估类PALADHI M M（2024）目前尚未建立起统一的AI素养评估标准，不同机构和研究者使用的评估方法各不相同。这种缺乏标准化的情况使得评估结果难以比较和推广[30]
评估类NG D T K（2021）现有的评估方法往往只关注AI知识和技能的掌握情况，忽视了对AI的批判性思考和伦理意识的评估[31]
评估类LINTNER T（2024）评估工具的可靠性和信效度不足，目前用于评估AI素养的工具大多缺乏足够的信效度检验，无法确保评估结果的准确性和一致性[32]

Fig.1

Research roadmap"

Fig.2

Results of searching "ChatGPT Training Principles" on Wikipedia"

Fig.3

Results of universal primer inquiry on "ChatGPT Training Principles""

Fig.4

Traditional "Machine Learning Classification Model" modeling steps"

Fig.5

Presentation of the automated results of ZhiPu QingYan's "Machine Learning Classification Model""

Fig.6

Incorrect solution of the football match problem by the "Mistral Nemo 12B Starcannon" model"

Fig.7

Vague solution to the football match problem by the "Tongyi Qianwen" model"

Fig.8

Correct solution to the football match problem by the "Wenxin Yiyan" model"

Fig.9

Incorrect solution to the comparison problem by the "ChatGPT-4o" model"

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