分析菲律宾大学生AI依赖模式,发现四类人群及能力差异。
Profiles of AI Dependency: A Latent Class Analysis of Filipino Students' Academic Competencies
- 用潜在类别分析识别出四类学生使用AI的模式。
- 高度依赖AI的学生在写作与研究能力上最弱。
- 适合教育政策制定者与高校教师参考。
菲律宾大学生对人工智能(AI)的依赖日益加深,引发对其基础学术能力下降的担忧。本研究考察了大学生对AI的依赖程度及其对批判性思维、写作能力、学习独立性、研究技能和学术参与度的影响。采用横断面研究设计,从菲律宾邦板牙省经高等教育委员会认证的高校中收集了651名学生的调查数据。通过潜在类别分析(LCA)识别出四种不同的AI使用模式:高度参与的独立学习者、选择性使用AI者、中等依赖者和高度依赖者。结果显示,学生在研究与写作任务中表现出中等到高水平的依赖;其中,高度依赖者在各项学术能力上表现最差,严重依赖AI生成内容。研究强调应推动将AI素养纳入教育政策,并调整课程以培养批判性思维与伦理使用AI的能力。未来可进一步探索长期影响及干预策略,以缓解因AI依赖导致的学术能力退化。
原文摘要 · Abstract (English)
The increasing dependency among Filipino college students on artificial intelligence (AI) poses concerns about the potential decline of fundamental academic competencies. This study examines the extent of AI dependency and its perceived effects on students' critical thinking, writing skills, learning independence, research skills, and academic engagement. Using a cross-sectional research design, data was collected from 651 students enrolled in higher education institutions (HEIs) in Pampanga, Philippines accredited by the Commission on Higher Education. The survey data was analyzed using Latent Class Analysis (LCA) to identify AI dependency patterns. Findings indicated that students show moderate to high AI dependency, specifically in research and writing tasks. LCA identified four distinct profiles: highly engaged independent learners, selective AI users, moderate AI users, and AI-dependent learners. Notably, AI-dependent learners demonstrated the weakest academic competencies, with significant dependency on AI-generated outputs. The study highlights the need to foster educational policies that integrate AI literacy while preserving essential academic skills. HEIs must also balance technological advancements with curriculum adaptations to promote critical thinking and ethical use of AI. Future research may explore the longitudinal impacts and intervention strategies to mitigate academic skill erosion caused by AI dependency.
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