生成式人工智能环境下的搜索即学习：技术路径、行为演化与伦理挑战

doi:10.13998/j.cnki.issn1002-1248.25-0274

Abstract

Abstract:

[Purpose/Significance] This paper investigates how generative artificial intelligence (GenAI) is reshaping the Searching as Learning (SAL) paradigm, focusing on its implications, challenges, and prospects in Library and Information Science (LIS). Traditional SAL emphasizes the cognitive and metacognitive processes by which users acquire and construct knowledge through information retrieval. However, the advent of GenAI - especially large language models (LLMs) - introduces a transformative shift from keyword-based querying to dynamic, dialogic, and multimodal interactions. This study aims to clarify the conceptual and practical significance of GenAI-driven SAL, explore its technical trajectories, and evaluate its impact on learners' behavior, learning strategies, and information literacy. It also highlights the emerging ethical and epistemological challenges posed by GenAI systems in learning-oriented search contexts. [Method/Process] Using the PRISMA-ScR framework, this study conducted a scoping review of academic and gray literature published between January 2023 and May 2025. A total of 1 681 records were retrieved from major academic databases and preprint repositories. After screening titles, abstracts, and full texts, 22 studies were selected for in-depth qualitative analysis. Thematic coding and synthesis were conducted to extract recurring patterns and theoretical insights across three key dimensions: GenAI-enhanced search technologies, evolving user behaviors in SAL contexts, and normative concerns associated with credibility, agency, and transparency. The analysis was grounded in LIS theories, including information behavior, metacognitive models of learning, and digital/information literacy frameworks. [Results/Conclusions] The results reveal that GenAI is fundamentally reshaping SAL in three key areas. First, in terms of technology, GenAI systems (e.g., GPT-based chat interfaces) provide conversational, context-aware, and multimodal assistance, transforming SAL from reactive searching to proactive co-learning. These systems scaffold learning through adaptive query reformulation, real-time content summarization, and source triangulations supporting iterative reflection and cognitive engagement. Such affordances mirror the functions traditionally associated with human tutors, thereby expanding learners' capacity for critical inquiry and self-directed exploration. Second, user behaviors in SAL are undergoing a paradigm shift. Learners increasingly engage in human-AI co-construction of knowledge, participating in iterative query-dialogue loops that facilitate concept clarification and knowledge synthesis. While this enhances engagement, personalization, and perceived learning efficiency, it also raises concerns. Over-reliance on AI-generated content may undermine learners' critical thinking, reduce information discernment, and promote passive consumption. The study identifies a dual effect. While GenAI augments higher-order thinking and strategic learning, it can also lead to superficial comprehension when learners lack the skills to critically evaluate AI output. Third, the review underscores the urgency of addressing ethical and pedagogical challenges. Issues such as AI hallucination, algorithmic opacity, and biased content threaten the credibility of GenAI-enhanced learning environments. From an LIS perspective, this necessitates a reconfiguration of information literacy education to include AI literacy. Students must be equipped not only to retrieve and evaluate information, but also to interrogate algorithmic sources, verify provenance, and triangulate AI outputs with authoritative references. GenAI should be positioned as a cognitive assistant, not a definitive knowledge authority. GenAI holds substantial promise in enhancing SAL through greater interactivity, personalization, and cognitive scaffolding. However, these benefits must be balanced with informed practices that mitigate risks to learner autonomy, critical reasoning, and information ethics. This work establishes an analytical foundation for future research and practices at the intersection of AI, learning, and information behavior.

Key words: Generative artificial intelligence (GenAI), Searching as Learning (SAL), human-AI cognitive collaboration, information literacy restructuring, AI ethics

CLC Number:

G252.7

SHI Xujie, YUAN Fan, LI Jia. Searching as Learning in the Context of Generative Artificial Intelligence: Technological Pathways, Behavioral Evolution, and Ethical Challenges[J].Journal of library and information science in agriculture, 2025, (): 1-18.

Figures/Tables 3

Fig.1

Table 1

Basic characteristics of included studies"

作者/年份/国家	样本	研究目的	关键结果
DUONG、VU和NGO（2023，越南）	1 389名大学生	运用改进的技术接受模型（TAM）考察ChatGPT使用情况，以知识共享为调节变量	√ 努力期望通过绩效期望和使用意图对实际使用产生直接与间接正向影响 √ 知识共享显著促进使用意图向实际使用的转化 × 存在对学习质量构成威胁的担忧
JO（2023，韩国）	645名大学生及职场人士	基于知识获取、个性化等 13 项变量模型探究用户对 ChatGPT的参与度	√ 知识获取与个性化影响功利性收益及个体影响 √ 信任对行为意图产生作用 × 使用行为未显著预测口碑传播
JO和PARK（2023，韩国）	351名职场人士（20~40岁）	探究ChatGPT在工作场景中信息支持与知识获取的作用。	√ 信息支持与知识获取正向影响感知效用及使用意图 × 实际使用受性别、年龄等人口学因素影响，非单一效用驱动
RAHMAN等（2023，孟加拉国）	344名大学生	通过纳入信息性和愉悦感扩展技术接受模型（TAM），探究信任的调节作用	√ 有用性、易用性及信息性显著预测态度与意向 √ 信任增强愉悦感对态度的影响效应 × 缺乏信任时，愉悦感影响甚微
YILMAZ和KARAOGLAN（2023，土耳其）	41名本科生	考察学生对使用ChatGPT进行编程学习的观点	√ 优势包括快速答疑、思维能力提升与调试支持 × 风险涉及懒惰倾向、错误答案引发的焦
WANDELT等（2023，中国）	北京航空航天大学研究生中的102名学生	通过学生调查与实验评估ChatGPT对航空运输教育与研究的影响	√ 优势：高效学习、编程与写作能力提升 × 风险：辅助作用与过度依赖的权衡
SONGSIENGCHAI等（2023，泰国）	120名一年级准教师	评估ChatGPT对泰国学生英语学习效能的影响	√ 语言能力提升具有统计显著性 √ 学习动机与参与度增强 × 关注伦理及长期影响
AMER等（2023，未明确说明）	未明确说明	研究信息检索中从传统搜索引擎到生成式AI的范式转变	√ 生成式AI提供更拟人化响应及情境感知型信息检索 × 过度依赖带来伦理与结构性风险
LAI等（2025，美国）	34名统计学课程本科生	运用认知网络分析探究苏格拉底式聊天机器人的自我调节学习模式	√ 高分者表现出反思性与评价性参与特。 × 低分者聚焦表面层次提问
ALDULAIJAN等（2025，沙特阿拉伯）	11名女性研究生	研究生学习中生成式AI使用模式的质性研究	√ 优势：创新性、参与度提升 × 挑战：学生与工具互动的模糊性及效能不明确
ZHANG和YANG（2025，中国台湾地区）	916名台湾地区大学生	比较Google ChatGPT在学术求助中的差异并识别影响因素	√ ChatGPT更受青睐；影响因素：流畅性、信息失真、年龄 × 需强化批判性思维与工具优化
LIU等（2024，中国）	31名本科生	比较ChatGPT与传统搜索在不同复杂度学习任务中的表现	√ ChatGPT在复杂任务中提升用户体验与绩效
TIBAU等（2024，未明确说明）	未明确说明	提出45种对话策略以优化ChatGPT支持的自我调节学习（SAL）	√ 对话式搜索促进反思性与迭代式学习 × 人工智能内容准确性验证困难
YANG等（2025，未明确说明）	40名组间实验参与者	研究生成式AI与搜索系统集成在学习任务中的应用。	√ 集成系统提升知识保持率与学习效果
SHIRI和JIN（2025，未明确说明）	4项研究共4 591名参与者	比较大语言模型（LLMs）与网络搜索的学习深度差异	× 大语言模型导致更浅层、被动的学习 √ 网络搜索促进主动整合与深度学习
YANG等（2024，韩国）	92名大学生	比较大语言模型（LLMs）、搜索引擎与书籍的学习效果	√ 大语言模型辅助理解但限制记忆保持 √ 高表现者阅读深度显著高于其他群
LIN等（2025，未明确说明）	未明确说明	提出SEAL/SEAL-C框架比较大语言模型搜索与传统搜索	√ 大语言模型在引导下提升效率 × 复杂问题缺乏独立搜索能力
孙晓宁等（2024，中国）	未明确说明	人机交互视域下对话式搜索研究的系统综述	√ 新范式增强用户交互并弥合人机鸿沟 × 评估模型仍存在挑战
王喆和夏清泉（2023，中国）	未明确说明	探讨生成式AI引发的研究生与导师角色变迁	√ 提出双方适应AI融合的四重转型路径
孙妍妍等（2025，中国）	华东某大学硕士课程的21名学生	通过学生-聊天机器人对话分析人机协作学习模式	√ 识别3种行为模式与协作模式 √ 提出对话式、高认知水平的学习设计建议
GHOSH等（2023，未明确说明）	未明确说明	基于自我调节学习（SAL）的AI增强系统与错误信息治理专题研讨	√ 基于SAL的AI系统提升学习与意义建构能力 × 需聚焦信息素养与系统设计
王俊等（2025，中国）	19名熟练使用ChatGPT的高校学生	采用日记法与访谈法，纵向分析用户与ChatGPT的交互行为及任务类型	√ 划分5类任务（信息获取至创造表达型），揭示满意度差异 √ 提出Gen AI的“工具-助手-替代”多元角色理论 × 未深入挖掘交互情境与信息内容 × 需扩展行为维度与群体特性研究

Table 1

Table 2

Research themes and representative citations"

子主题	代表性引文
主题一：基于人工智能的搜索式学习模式转型
生成式人工智能促使对话式检索的兴起	探究通过比较ChatGPT与传统搜索，提出ChatGPT等大语言模型有效弥补了传统搜索方式的不足，标志着对话式搜索范式的兴起^[42]
生成式人工智能搜索策略框架	研究中提出了45种策略，旨在帮助用户更清晰地界定信息需求、优化检索词、并评估ChatGPT的回应在相关性、实用性与可信度等方面的表现^[43]
AI工具对复杂学习任务支持功能的研究	研究在“Search+Chat”实验条件下构建了一个融合传统网络搜索组件与基于生成式AI对话组件的系统（Chat AI）提升学习效率与深度^[44]
主题二：用户行为与 AI 辅助学习中的参与模式
用户对AI工具的采纳行为和使用频率影响因素研究	运用了技术接受模型（TAM）揭示努力期望是直接影响学生对ChatGPT实际使用的因素，并且通过绩效期望与使用意图的中介作用间接影响其使用频率^[45]
用户使用AI工具学习时参与行为特征分析	通过13个变量模型（知识获取、个性化、信任等）分析用户参与行为的特征及对行为意图的影响^[46]
生成式人工智能驱动的学习行为调适	通过开发聊天机器人，结合过程-行动认知网络分析（Process-Action Epistemic Network Analysis），深入探究生成式人工智能动态调整学习路径与互动策略以支持学生的自我调节学习（SRL）^[47]
主题三：生成式人工智能对学习成效的影响
生成式人工智能对技能发展的作用	研究考察了学生对使用ChatGPT进行编程学习的认知观点，其促进作用体现为能够快速答疑、提升思维能力等^[48]
生成式人工智能促进可持续学习的研究	通过实证分析表明生成式人工智能可通过动态互动模式与认知序列引导，提升学习者在复杂任务中可持续学习能力，实现知识保持^[49]
AI工具使用的挑战与局限	研究详细对比了大语言模型（LLMs）与网络搜索的学习深度差异，指出大语言模型在知识构建上存在深度不足的问题^[50]

Table 2

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Searching as Learning in the Context of Generative Artificial Intelligence: Technological Pathways, Behavioral Evolution, and Ethical Challenges

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