Year: 2025
Type: article
Abstract: 随着生成式人工智能技术在高校论文检测中的广泛应用，AI误判现象逐渐引发学生群体的心理焦虑与价值困扰。本文以“AI论文检测误判情境”为研究背景，综合教育技术与心理学视角，探讨大学生焦虑情绪的形成机制与调适路径。研究首先分析了AI检测系统算法偏差、检测标准模糊及高校评价机制失衡等外部诱因，指出技术的不透明与制度的刚性是引发焦虑的重要根源；其次，从个体层面揭示了学生在被误判后的自我效能受挫、学术身份危机与社交信任下降等心理变化，焦虑呈现出“从学业焦虑到存在焦虑”的扩散趋势；最后，提出以“技术改进—教育引导—心理干预”三位一体的调适路径，通过算法优化、申诉机制完善、心理支持体系建设和AI素养教育提升，构建人机协同的教育生态。研究认为，AI论文检测的科学化与人文化并重，是缓解大学生焦虑、促进学术诚信与心理健康的关键方向。With the widespread application of generative artificial intelligence technology in college thesis detection, the phenomenon of AI misjudgment has gradually triggered psychological anxiety and value confusion among students. Taking the "context of AI thesis detection misjudgment" as the research background, this paper integrates the perspectives of educational technology and psychology to explore the formation mechanism and adjustment path of college students' anxiety. The study first analyzes external incentives such as algorithmic bias of AI detection systems, vague detection standards, and unbalanced evaluation mechanisms in colleges and universities, pointing out that the opacity of technology and the rigidity of systems are important roots of anxiety. Secondly, from the individual level, it reveals the psychological changes of students after being misjudged, such as frustrated self-efficacy, academic identity crisis, and decreased social trust, with anxiety showing a spreading trend "from academic anxiety to existential anxiety". Finally, a trinity adjustment path of "technology improvement-educational guidance-psychological intervention" is proposed. By optimizing algorithms, improving appeal mechanisms, constructing psychological support systems, and enhancing AI literacy education, a human-machine collaborative educational ecology is built. The study holds that attaching equal importance to the scientific and humanistic aspects of AI thesis detection is a key direction to alleviate college students' anxiety and promote academic integrity and mental health.
Institution: Guangzhou College of Technology and Business
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416248667