亲爱的亚裔家长们，
作为一名家长，我深知深夜的焦虑：“孩子在AI时代该选什么专业？一个‘热门’学位就能进顶尖大学吗？”在SAT Professionals，我们已帮助数千名亚裔学生，许多成功进入斯坦福、MIT和常春藤名校——而现在家长们最常问的问题是：“如何为AI做准备，又不牺牲其他领域的深度？”
真相是：AI不是单一专业，而是每所顶尖大学和雇主都要求的“超级技能”——它需要与生物、金融、医学，甚至艺术结合。我们的最新文章独家揭秘顶尖大学如何重塑AI教育，以及跨学科路径如何让您的孩子在申请和未来职业中占据决定性优势。
立即阅读完整攻略:
人工智能：未来所有学科的底层能力
随着人工智能（AI）技术的迅速发展，它已不再局限于传统的计算机科学领域，而逐渐渗透到各行各业。许多家长和学生常常会面临一个问题：“孩子应该如何选择人工智能相关的专业和学校？”本文将探讨人工智能的学科交叉特点、如何选择相关专业以及在未来职业生涯中如何培养跨学科的能力。
人工智能：跨学科的综合能力 人工智能的核心不只是简单的编程或算法问题，而是一个跨学科、多领域融合的复杂系统。因此，人工智能并不是一个单独的专业，而是一个覆盖计算机科学、数学、统计学以及其他行业知识的大领域。想要在人工智能领域深耕，单一的“热门”学科选择往往不足以为学生提供全面的能力发展。
核心学科：人工智能的基石 1. 计算机科学（CS） 计算机科学为人工智能提供了最基础的技术支撑，包括编程语言、算法设计、数据结构等基础知识。人工智能的“语言和逻辑”便源于此。因此，学习计算机科学不仅能帮助学生掌握人工智能的核心工具，还能培养解决实际问题的逻辑思维能力。 2. 数学与统计学 数学，尤其是线性代数、微积分、概率论和统计推断等领域，为人工智能提供了强大的理论基础。人工智能背后的大脑便是这些数学原理，通过数据分析和模型训练，使得AI能够模拟、推理、学习并做出预测。缺乏数学知识的支持，AI的智能应用将无法顺利展开。 3. 应用学科（如生物、医学、工程等） 在人工智能的实际应用中，行业知识同样扮演着至关重要的角色。AI不仅需要强大的技术支撑，还需要“真实世界”的数据来驱动决策。因此，跨学科的应用，如生物信息学、医学影像分析、金融建模等，成为了AI创新和实践的重要领域。AI能够为不同学科提供独特的解决方案，但其前提是对相关行业的深刻理解。
为什么跨学科人才更受青睐？ 当前，越来越多的企业尤其是技术巨头开始寻找“跨学科”人才，这正是因为人工智能的复杂性远超单一学科的范畴。真正的AI人才不仅要懂得编程和算法，更需要在行业背景、数据处理和社会伦理等方面拥有深刻的洞察力。例如，医学领域的AI专家不仅需要熟练掌握数据科学和编程技能，还要理解医学影像的特征和临床背景，才能更好地推动AI在医学中的应用。
目前，欧美一些顶尖大学的人工智能专业已经进入了交叉学科的时代，课程内容往往结合了多个学科领域，例如： • AI + 生物（生物信息学） • AI + 医学（医学影像、精准医疗） • AI + 环保（气候变化建模） • AI + 商科（大数据分析、金融科技）
这种交叉学科的模式不仅帮助学生拓宽视野，也使他们能够在不同领域中找到AI应用的创新突破。
如何选择学校和专业？ 当家长和学生在选择人工智能相关的专业和学校时，单纯依赖“哪个学校有AI专业”并不足够。更重要的是，学校在以下几个方面的实力： 1. 计算机科学（CS）实力 一所学校的计算机科学专业的质量直接决定了学生在AI学习中的技术基础。 2. 数据科学与机器学习课程 机器学习和深度学习是人工智能的核心应用技术，因此学校在这些领域的教学和研究能力至关重要。 3. 跨学科实验室 学校是否有跨学科的AI实验室，如生物信息学、机器人学、AI实验室等，能够为学生提供更多的实践机会。 4. 项目式学习和实习机会 选择提供丰富实际项目和行业实习的学校，可以让学生在真实的AI应用中锤炼技能，将理论与实践相结合。
跨学科能力：未来竞争力的关键 真正的AI人才并不局限于单一的学科背景，而是能够跨界融合的复合型人才。未来最受欢迎的AI人才将具备以下几种能力： • AI + 生物：例如医疗科技、药物研发和基因数据分析； • AI + 机器人：例如自动驾驶、智能制造； • AI + 商科：例如金融科技、大数据商业分析； • AI + 心理学：例如人机交互、AI教育等领域； • AI + 艺术：例如创意产业、媒体内容制作等。
因此，跨学科能力的培养不仅能够帮助学生在AI领域脱颖而出，更能为他们未来的职业发展提供多元化的选择。
结语 尽管人工智能技术日新月异，但其核心能力依旧根植于计算机科学、数学和行业知识。对于孩子的未来来说，单一专业的选择往往不能为其带来足够的竞争力，真正的优势在于培养跨学科的综合能力。未来最具竞争力的学生，将是那些能够将多个学科有机融合的复合型人才。
我们所能做的是帮助孩子们打好科学基础，提供跨学科的学习机会，并通过项目式学习让他们亲自体验人工智能在不同领域的实际应用。从而为他们未来的学术发展和职业生涯奠定坚实的基础。
如您有关于大学申请、竞赛或个人兴趣项目的问题，请联系我们办公室进行免费咨询。
Wendy @ SATPro
909-860-2190 www.satprofessionals.com
Dear Asian Parents and Families, As a parent myself, I know the late-night worries: “Is my child choosing the right major in this AI-driven world? Will one ‘hot’ degree secure their future at a top university?” At SAT Professionals, we’ve guided thousands of Asian-American students, many have gotten into top colleges like Stanford, MIT, and Ivy League schools—and the #1 question we hear now is “How do we prepare for AI without sacrificing depth in other fields?” The truth? AI isn’t a single major. It’s the new “super-skill” every elite college and employer demands—blended with biology, finance, medicine, or even art. Our latest article reveals exactly how top universities are rethinking AI education and the cross-disciplinary path that gives your child a decisive edge in admissions and beyond.
Read the full guide here
Artificial Intelligence: The Foundational Capability for All Disciplines in the Future
With the rapid development of artificial intelligence (AI) technology, it is no longer confined to the traditional field of computer science but is gradually permeating every industry. Many parents and students often face a question: “How should children choose AI-related majors and schools?” This article will explore the interdisciplinary characteristics of AI, how to select relevant majors, and how to cultivate cross-disciplinary abilities in future careers.
Artificial Intelligence: A Comprehensive Cross-Disciplinary Capability The core of artificial intelligence is not just simple programming or algorithm problems but a complex system that integrates multiple disciplines and fields. Therefore, AI is not a standalone major but a broad domain encompassing computer science, mathematics, statistics, and knowledge from other industries. To excel in the AI field, choosing a single “hot” discipline is often insufficient to provide students with comprehensive capability development.
Core Disciplines: The Cornerstones of Artificial Intelligence
- Computer Science (CS) Computer science provides the most fundamental technical support for AI, including programming languages, algorithm design, data structures, and other foundational knowledge. The “language and logic” of AI originate from here. Thus, studying computer science not only helps students master AI’s core tools but also cultivates logical thinking skills for solving real-world problems.
- Mathematics and Statistics Mathematics—especially linear algebra, calculus, probability theory, and statistical inference—provides a powerful theoretical foundation for AI. The “brain” behind AI consists of these mathematical principles, which enable AI to simulate, reason, learn, and make predictions through data analysis and model training. Without the support of mathematical knowledge, intelligent AI applications cannot proceed smoothly.
- Applied Disciplines (e.g., Biology, Medicine, Engineering, etc.) In the practical applications of AI, industry-specific knowledge plays an equally crucial role. AI requires not only strong technical support but also “real-world” data to drive decision-making. Therefore, cross-disciplinary applications—such as bioinformatics, medical imaging analysis, and financial modeling—have become important areas for AI innovation and practice. AI can provide unique solutions for different disciplines, but this presupposes a deep understanding of the relevant industries.
Why Are Cross-Disciplinary Talents More Favored? Currently, an increasing number of companies, especially tech giants, are seeking “cross-disciplinary” talent. This is precisely because the complexity of AI far exceeds the scope of a single discipline. True AI professionals must not only understand programming and algorithms but also possess deep insights into industry backgrounds, data processing, and social ethics. For example, an AI expert in the medical field needs to master data science and programming skills while also understanding the characteristics of medical imaging and clinical contexts to better advance AI applications in medicine.
At present, AI programs at top universities in Europe and the United States have entered an era of interdisciplinary integration, with curricula often combining multiple fields, such as: • AI + Biology (Bioinformatics) • AI + Medicine (Medical Imaging, Precision Medicine) • AI + Environment (Climate Change Modeling) • AI + Business (Big Data Analysis, FinTech)
This interdisciplinary model not only broadens students’ horizons but also enables them to discover innovative breakthroughs in AI applications across different fields.
How to Choose Schools and Majors? When parents and students select AI-related majors and schools, simply relying on “which school has an AI program” is not enough. More importantly, the school’s strengths in the following areas:
- Computer Science (CS) Strength The quality of a school’s computer science program directly determines students’ technical foundation in AI studies.
- Data Science and Machine Learning Courses Machine learning and deep learning are core technologies in AI applications, so the school’s teaching and research capabilities in these areas are crucial.
- Interdisciplinary Laboratories Whether the school has cross-disciplinary AI labs—such as those in bioinformatics, robotics, or general AI—can provide students with more hands-on opportunities.
- Project-Based Learning and Internship Opportunities Choosing schools that offer rich real-world projects and industry internships allows students to hone their skills in actual AI applications, bridging theory and practice.
Cross-Disciplinary Abilities: The Key to Future Competitiveness True AI talent is not limited to a single disciplinary background but consists of composite professionals capable of cross-boundary integration. The most sought-after AI professionals in the future will possess abilities such as: • AI + Biology: e.g., Medical Technology, Drug Development, and Genomic Data Analysis; • AI + Robotics: e.g., Autonomous Driving, Intelligent Manufacturing; • AI + Business: e.g., FinTech, Big Data Business Analysis; • AI + Psychology: e.g., Human-Computer Interaction, AI Education; • AI + Art: e.g., Creative Industries, Media Content Production.
Therefore, cultivating cross-disciplinary abilities not only helps students stand out in the AI field but also provides diverse options for their future career development.
Conclusion Although AI technology is advancing rapidly, its core capabilities remain rooted in computer science, mathematics, and industry knowledge. For children’s futures, choosing a single major often fails to provide sufficient competitiveness; the real advantage lies in developing comprehensive cross-disciplinary abilities. The most competitive students in the future will be those composite talents who can organically integrate multiple disciplines.
What we can do is help children build a strong scientific foundation, provide cross-disciplinary learning opportunities, and allow them to personally experience AI’s real-world applications in various fields through project-based learning. This will lay a solid foundation for their future academic development and careers.
If you have questions about college applications, competitions, or passion projects, please contact our office for a general consultation.
Wendy @ SATPro
(909) 860-2190 www.satprofessionals.com
