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AI时代的平权假象
Hu Xiu· 2025-09-04 23:54
Core Viewpoint - The article discusses the impact of online education platforms like Khan Academy on individual learning experiences, highlighting a case of a 17-year-old math prodigy from the Bahamas who excelled through self-study and gained admission to prestigious academic programs despite lacking formal qualifications [1][4]. Group 1: Online Education Platforms - Khan Academy, founded by an Indian entrepreneur, provides free educational resources, particularly excelling in foundational mathematics education [2]. - The platform allows students from underprivileged backgrounds to access high-quality educational content, enabling exceptional individuals to break through local educational limitations [4][5]. Group 2: Individual Success Stories - The 17-year-old girl utilized Khan Academy to master university-level mathematics at a young age and later enrolled in graduate courses at UC Berkeley, where she received mentorship from renowned mathematicians [2][3]. - A Chinese professor played a crucial role in her development by challenging her with complex problems, leading her to solve a long-standing mathematical conjecture [3]. Group 3: AI and Learning Dynamics - The article contrasts the experiences of exceptional learners with those of average individuals, emphasizing that while talented individuals can leverage AI for growth, many others may become complacent and seek validation from AI without critical engagement [6][8]. - The feedback mechanisms of AI systems often reinforce existing biases, leading to a disparity in how individuals utilize these technologies for learning [7][10]. Group 4: Recommendations for Individuals - Individuals are encouraged to remain open-minded and critical of the information they receive, especially from AI, to avoid falling into self-satisfaction traps [13][14]. - Emphasizing the importance of actively engaging with new technologies, individuals should focus on using AI to enhance their professional capabilities rather than relying solely on it for validation [16].