AIGC赋能高校大学生知识产权素养培育路径优化研究

doi:10.13998/j.cnki.issn1002-1248.25-0444

Abstract

Abstract:

[Purpose/Significance] The rapid expansion of artificial intelligence generated content (AIGC) is transforming how intellectual property (IP) literacy is cultivated in universities. Conventional approaches, often constrained by disciplinary fragmentation, uneven teaching capacity, and time–space limitations, are increasingly misaligned with human-AI collaborative learning. Against this backdrop, IP literacy must integrate legal knowledge, ethical judgment, compliance awareness, and AI-enabled creative practice. This study clarifies the renewed connotations of IP literacy in the AIGC era, develops a theoretically grounded model of influencing factors, and examines how multiple educational conditions combine to generate high-level outcomes. By focusing on IP literacy rather than generic digital competence, the paper addresses a clear gap in existing research and offers a configuration-based understanding that links theory to implementable strategies for intelligent, student-centered IP literacy education. [Method/Process] Grounded in Activity Theory, the study developed a six-dimensional framework consisting of the following variables: teacher professional competence, AI-IP awareness, diversified educational support, role division, evaluation mechanisms, and AI resources. These variables were operationalized via a structured questionnaire. Fuzzy-set Qualitative Comparative Analysis (fsQCA) was then employed to identify conjunctural causality and equifinal pathways that extend beyond linear models. High-outcome configurations were achieved through variable calibration, truth-table analysis, and minimization. Robustness was confirmed by tightening the PRI consistency threshold from 0.80 to 0.85. The path structure, overall coverage, and overall consistency remained stable. [Results/Conclusions] Findings show that AIGC-enabled IP literacy emerges through multiple effective configurational paths, rather than a single dominant factor. Across high-outcome configurations, teacher professional competence, AI–IP awareness, and diversified educational support consistently function as core drivers that shape learning processes and outcomes. Evaluation mechanisms and AI resources act as complementary or substitutive conditions, reinforcing effectiveness under specific institutional and resource constraints. Three typical paths were identified: a path emphasizing practice generation coupled with collaborative organization; a path that integrates resource sharing with practice-oriented development; and a path highlighting collaborative division of labor and effective communication to compensate for limited technical supply. Together, these paths confirm the internal logic of the six-dimensional model and demonstrate that coordinated configurations, rather than isolated improvements, are necessary to optimize IP literacy education in AI-rich contexts. Practical implications include strengthening AI-oriented teacher development, embedding AI-IP awareness in curricula and supporting services, building cross-unit collaboration mechanisms, and aligning role division and process evaluation with available AI resources. Although the cross-sectional design and limited scope constrain generalizability, the results provide a theoretically grounded and empirically supported basis for developing intelligent, collaborative, and student-centered IP literacy systems and offer a foundation for future longitudinal and comparative research in AIGC-enabled higher education.

Key words: intellectual property literacy education, AIGC, influencing factor, configuration path, fuzzy-set qualitative comparative analysis, information literacy

CLC Number:

G258.6

FENG Li, GUO Bochi, GAO Mian. Optimizing the Path of Cultivating Intellectual Property Literacy among College Students through AIGC Empowerment[J].Journal of library and information science in agriculture, 2025, (): 1-15.

Figures/Tables 6

Fig.1

Table 1

Variable measurement items and measurement content"

变量	测量题项及内容	来源
专业素质	①教师熟悉掌握AI工具，并持续关注自身能力的提升，不断更新知识储备 ②教师拥有扎实的知识产权专业知识和教学技能，能将AIGC融入教学过程 ③教师能够利用AIGC设计个性化的知识产权教学方案，满足不同学生学习需求 ④教师利用AIGC提供场景模拟，将知识产权知识和技能运用到创新创业指导	[33]
AI+IP意识	①使用AIGC辅助知产学习时，我会主动检查其数据来源的合法性与内容的合规性 ②使用AIGC辅助知产学习时，我会严格遵守生成内容的权利归属规则，并尊重原创者与权利人的合法权益 ③使用AIGC辅助知产学习时，我认为有责任确保整个过程符合道德规范与法律法规 ④我认为AIGC对于知识产权课程的学习具有重要的应用价值	[34,35]
多元教育	①学校设置人工智能基础课与融合课模块，积极打造“人工智能+”知识产权课程体系 ②知识产权课程由图书馆、创业创业指中心、人工智能等学院协同开展 ③学校紧抓AIGC发展机遇，开展多元化的AI+知识产权科普活动 ④学校会依托联合导师、项目合作、实训实习等方式，开展人才校企联合培养	[36]
角色分工	①在AIGC协作教学中，各参与主体角色分工明确，协作高效顺畅 ②教师与AIGC通过协同教学设计与质量评估达成行动共识、角色共识 ③学习者利用AIGC根据自身需求定制与获取学习资源，拥有充分的交互自主权 ④活动相关主体在价值理念上达成利益一致、目标同向，在实践中实现行动协同	[37,38]
评价机制	①AIGC反馈的过程数据，有助于教师及时调整知识产权教学策略 ②AIGC反馈的学情数据，能准确反映我的学习进展，有助于调整个人学习策略 ③AIGC可根据定制化的需求进行拓展，提供个性化的指导建议 ④我觉得引入AIGC的过程评价机制，可显著提升知识产权素养培育效果	[39,40]
AI资源	①学校在知识产权通识课程教学中嵌入AIGC辅助教学功能 ②学校拥有AI+知识产权数字资源库和分析工具，帮助学生快速查找和分析相关资料 ③学校建设AI+智能教学系统，集成智能助教、智能学伴、知识图谱等，为学生提供个性化知识产权学习服务 ④学校建有AI+虚拟仿真实验平台，为学生提供模拟的知识产权实践场景	[41]
知识产权素养	①我不仅熟悉知识产权的基础理论与法规，更能主动运用AIGC，将理论知识转化为实际应用能力 ②我具备识别和评估日常知识产权风险的能力，善于利用AIGC分析潜在问题，并制定避免侵权的策略 ③我在学习和生活中坚持尊重并合法使用他人知识产权，运用AIGC进行合规性验证，确保行为正当 ④我能有效运用知识产权知识来保护个人创新，并积极借助AIGC，主动维护自身的知识产权权益	[42]

Table 1

Table 2

Table 3

Table 4

Table 5

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类别	分类	频数	占比/%
性别	男	249	53.78
性别	女	214	46.22
学历	大学本科生	195	42.12
	硕士研究生	167	36.07
	博士研究生	101	21.81
专业	人文社科类	98	21.17
	理工类	169	36.50
	医学类	154	33.26
	农学类及其他	42	9.07

变量	测度项	因子荷载	Cronbach's α	AVE	CR
专业素质（A）	A1	0.782	0.853	0.593	0.853
	A2	0.767
	A3	0.749
	A4	0.782
AI+IP意识（B）	B1	0.792	0.858	0.601	0.858
	B2	0.765
	B3	0.767
	B4	0.777
多元教育（C）	C1	0.731	0.860	0.606	0.861
	C2	0.787
	C3	0.788
	C4	0.806
角色分工（D）	D1	0.804	0.862	0.610	0.862
	D2	0.742
	D3	0.782
	D4	0.796
评价机制（E）	E1	0.784	0.857	0.616	0.857
	E2	0.762
	E3	0.810
	E4	0.744
AI资源（F）	F1	0.785	0.865	0.656	0.866
	F2	0.795
	F3	0.779
	F4	0.781
知识产权素养（X）	X1	0.812	0.884	0.656	0.884
	X2	0.800
	X3	0.832
	X4	0.794
KMO=0.929 近似卡方=6 130.030 Sig=0.000

变量	高知识产权素养		非高知识产权素养
变量	一致性	覆盖度	一致性	覆盖度
专业素质	0.770 775	0.672 269	0.536 124	0.437 082
~专业素质	0.354 598	0.449 888	0.598 003	0.709 175
AI+IP意识	0.760 742	0.668 090	0.544 011	0.446 568
~ AI+IP意识	0.369 817	0.464 572	0.595 664	0.699 438
多元教育	0.797 150	0.661 301	0.566 671	0.439 412
~多元教育	0.324 253	0.444 612	0.563 209	0.721 853
角色分工	0.778 498	0.669 110	0.544 217	0.437 214
~角色分工	0.345 208	0.447 603	0.588 127	0.712 796
评价机制	0.786 717	0.696 420	0.504 822	0.417 708
~评价机制	0.342 209	0.425 072	0.633 106	0.735 071
AI资源	0.770 722	0.667 411	0.546 024	0.441 966
~AI资源	0.355 585	0.455 922	0.589 103	0.706 024

变量	高知识产权素养
变量	组态H1	组态H2	组态H3
专业素质	○	●	●
AI+IP意识	●	●	●
多元教育	●	●	●
角色分工	●	⊗	●
评价机制			○
AI资源	●	●
原始覆盖度	0.623 805	0.541 544	0.533 196
唯一覆盖度	0.106 967	0.024 706 1	0.016 358 1
一致性	0.937 600	0.947 741	0.949 327
总体覆盖度	0.681 268
总体一致性	0.913 283

Optimizing the Path of Cultivating Intellectual Property Literacy among College Students through AIGC Empowerment

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