How AI Will Shape Education and Health in the Next Decade
NextGen Trends |
Artificial intelligence (AI) is a key driving force behind the new round of technological revolution and industrial transformation. As AI technology matures, its collaborative development with various industries is accelerating, and the concept of "AI+" is gaining widespread acceptance.
In traditional classrooms, a teacher facing dozens of students struggles to cater to each child's individual needs. However, in today's AI era, education is undergoing profound transformation. AI is not only changing the way knowledge is imparted but also demonstrating enormous potential in practical subjects like physical education.
In the health sector, AI in healthcare is no longer a laboratory concept but is genuinely changing how we address health issues.
AI will bring profound changes to education in the next decade: Personalized learning methods
- Precise learning path planning: AI can comprehensively and accurately analyze each student's learning habits, knowledge mastery, interests, etc., to tailor exclusive learning paths and resource recommendations. For example, it can push practice questions and reading materials suitable for different students' individual difficulty and pace, improving learning efficiency and effectiveness.
- Real-time Learning Feedback and Tutoring: The intelligent tutoring system provides students with real-time learning feedback, answers questions promptly, and helps students understand complex concepts. For example, the intelligent writing assistant can provide real-time feedback and suggestions on grammar, logic, and content in students' essays, allowing students to identify and correct problems in a timely manner.
Innovation in Teaching Methods
- Intelligent Assisted Teaching: Teachers can utilize AI-generated teaching materials and case studies to enrich teaching content and formats. For example, using virtual reality (VR) and augmented reality (AR) technologies to create immersive learning environments allows students to learn about historical events and explore scientific phenomena in a truly immersive way.
- Automated Teaching Management: AI can handle administrative tasks such as attendance, grade recording, and course scheduling. It can also assist teachers in teaching evaluation. For example, by analyzing multi-dimensional data such as student classroom performance, homework completion, and exam scores, it provides teachers with comprehensive and objective evaluation results, helping them adjust their teaching strategies in a timely manner.
Optimization of Educational Resources
- Enrichment and Sharing: Artificial intelligence (AI) can rapidly generate a large amount of high-quality educational content, such as lesson plans, handouts, and test questions. It can also break down geographical and school barriers, enabling the sharing of educational resources and making it easier for high-quality educational resources to reach resource-scarce areas, thus promoting educational equity.
- Precise Matching and Recommendation: Based on students' learning needs and characteristics, AI can precisely push the most suitable educational resources to students and teachers, improving resource utilization efficiency. For example, online learning platforms can recommend relevant courses and learning materials based on students' learning history and interests.
Transformation of the Teaching Profession
- Role Shift: Teachers will shift from traditional knowledge transmitters to learning guides, organizers, and companions, focusing more on students' personalized development, critical thinking skills, and moral education. This includes organizing group discussions and project-based learning activities to guide students in independent exploration and learning.
- Capacity Building: Teachers need to continuously improve their digital literacy and technological application skills, mastering the use of AI tools to better collaborate with AI in teaching. This includes using AI for learning analysis and teaching reflection, while also enhancing their innovation and interdisciplinary teaching abilities.
Expanding the Learning Environment
- Virtual Learning Spaces: Leveraging artificial intelligence and virtual reality technology, students can enter virtual learning spaces to learn and interact with students from around the world, such as participating in virtual international academic conferences and cultural exchange activities, broadening their horizons and cultivating global awareness and cross-cultural communication skills.
- Personalized Learning Spaces: Artificial intelligence can create personalized learning spaces based on students' learning preferences and needs, such as adjusting factors like lighting, temperature, and sound to provide a comfortable learning atmosphere, while also providing personalized learning tools and equipment.
In Healthcare
- AI-Assisted Diagnosis and Treatment – Precise Decisions in Every Treatment
By 2035, the deep integration of AI and life sciences will propel healthcare from "symptomatic control" to "precision cure," reshaping the definition, identification, and treatment pathways of diseases. Especially in areas such as oncology, rare diseases, chronic diseases, and autoimmune diseases, AI will no longer be merely an auxiliary tool but will become a core discoverer and treatment designer.
- Future Scenario: Diseases Identified Before Symptoms Appear
By 2035, diseases in hospitals will often be accurately detected by AI before symptoms even appear. During physical examinations or outpatient visits, AI-powered collaborative diagnostic systems integrate imaging, genetic, laboratory results, and physiological monitoring information in real time, acting like experienced "predictive experts" to find early clues. For example, a seemingly healthy patient enters the examination room, and AI is already analyzing their comprehensive health record. When the system identifies a subtle shadow in a lung CT scan and correlates it with a suspicious mutation in genetic testing, it immediately alerts to early risk and recommends further investigation. In this scenario, AI is no longer just a "second opinion," but a proactive participant in risk identification.
This advanced diagnostic capability stems from AI's deep modeling and reasoning of multimodal medical data. It can not only judge single-point examination results but also predict disease evolution trends across imaging, pathology, physical signs, and genetic information, helping doctors make intervention decisions earlier, thus truly achieving "detection before disease onset." With the widespread application, the infrastructure needs of the healthcare system are also undergoing a fundamental transformation. AI is evolving from "perception assistance" to "intelligent decision-making hub," covering multimodal diagnosis and treatment, disease prediction, pathway optimization, individual modeling, scientific research simulation, and automated drug screening.
The increasing scale of applications and the complexity of models have led to an exponential growth in computing power and storage demands. Future hospitals will not only be places of diagnosis and treatment, but also deep integration centers of scientific research and clinical practice, with intelligent computing and data management at their core. It is precisely this leap in computing power and storage, along with multimodal integration, that will enable the diagnostic model in 2035 to move from "waiting for symptoms" to "early detection." Identifying diseases before they manifest not only gives individuals more opportunities for treatment but also reduces misdiagnosis and missed diagnosis in the healthcare system, lowers the proportion of sudden severe cases, and significantly slows the accumulation of disease burden. In other words, AI is advancing medicine from "post-diagnosis" to "pre-emptive protection," truly realizing the ideal of "prevention before disease."
- Future Scenario: Every Treatment is a Precise Decision—Towards Personalized Smart Healthcare
By 2035, every treatment plan in hospitals will no longer rely on "one-size-fits-all" guidelines but will be generated through data-driven personalized decision-making. Once a patient is diagnosed, the intelligent diagnosis and treatment system will access a massive population database and the patient's own genetic, pathological, and lifestyle information to formulate a unique treatment path for them. Even for the same disease, the treatment plans for different patients may vary greatly, and medicine has fully entered the era of "personalized" precision medicine. This transformation stems from the in-depth application of global health data initiatives and big data models. Projects such as the US NIH's "All of Us" have accumulated genetic and health data of nearly one million people, laying the foundation for personalized medicine.
By 2035, this data will be integrated into medical big data models, enabling clinical decisions to comprehensively consider genetic risks, environmental factors, and lifestyles, improving the scientific nature and targeted nature of treatment. In clinical practice, AI has become an important partner for doctors. It can simultaneously integrate pathological slides, gene sequencing, vital signs, medical records, and imaging examinations, and reason within a unified framework, not only providing the patient's current state but also simulating the future trajectory under different plans. After quantitatively comparing the efficacy and side effects of each plan, AI will present them in a visual form, allowing doctors and patients to intuitively understand various trade-offs. For complex cases, AI can even surpass guidelines, proposing more targeted innovative therapies based on specific genetic or metabolic characteristics. AI's role extends to new drug development. When encountering difficult cases, AI not only helps select existing drugs but can also retrieve omics data based on molecular characteristics, discover potential targets, and recommend experimental therapies, making it possible to "develop a drug for a specific patient."
Research institutions, hospitals, and pharmaceutical companies form a closed loop using massive computing power and high-speed networks. Molecular simulations, virtual drug screening, and clinical trials are efficiently completed through cloud and edge computing collaboration, significantly shortening the cycle from research to drug use. Therefore, by 2035, healthcare will have achieved a dual breakthrough in both research and clinical application. Treatment plans will no longer be rigid standards but precise decisions dynamically generated by doctors and AI, respecting individual differences while also considering efficacy and safety.
