（来源：中国改革报） 转自：中国改革报 □ 雷炳锋 在新文科建设纵深推进与国家战略人才需求的双重驱动下，汉语言文学专业教育的范式转型正驶入快车 道。传统宽口径培养模式虽注重基础积淀，却与现代社会对复合型、创新型人才的精准化需求存在脱 节。面对这一结构性矛盾，依托汉语言文学开设的微专业以其"小而精、跨学科、强应用"的鲜明特征， 正在成为破解困局的关键支点。本文立足于新时代文化传承创新与产业升级的实践需求，系统探讨微专 业人才培养的内在逻辑、创新模式与实施路径，旨在为构建更加灵活且高效的高等教育供给体系提供理 论支撑。 汉语言文学微专业的建设必须超越"课程拼盘"式的浅层思维，坚持系统观念，强化协同创新，在目标定 位、课程体系和教学方法三个维度进行系统性创新，构建具有前瞻性和适应性的人才培养新范式。 完成从"知识传授"到"价值塑造与能力集成"的目标重塑。微专业的培养目标应旗帜鲜明地定位于培养"T 型人才"，其中"竖轴"象征对汉语言文学本体性知识的深度把握，"横轴"则代表通过微专业拓展的跨学 科实践能力。推动两种能力协同培养可提高学生运用人文智慧应对并解决复杂现实问题的综合素养，最 终实现"文以载道"与"以文化人"的 ...

Core Viewpoint - 2025 is expected to be the inaugural year for Agentic AI applications, with a consensus in the industry on the efficiency improvements brought by AI4SE (Artificial Intelligence for Software Engineering), although challenges remain in enterprise implementation [1]. Group 1: AI Agent Capabilities and Implementation - Current AI Agents are viewed as "smart assistants" rather than fully autonomous "colleagues," with their capabilities primarily enhancing repetitive and mechanical tasks [3][4]. - In the front-end domain, AI's effectiveness in code generation has led to concerns about job displacement, but the reality is that AI's application is more visible rather than indicative of higher acceptance [4]. - AI's role in operations is currently to augment human capabilities, allowing one person to accomplish the work of ten, but human oversight remains essential [6][12]. Group 2: Knowledge Management and AI Training - Effective self-learning for AI Agents requires structured knowledge management, as unstructured data can hinder performance [5][11]. - The introduction of a knowledge base significantly improves the output quality of AI Agents, enabling them to perform impact analysis and business reasoning more effectively [9]. - The front-end knowledge base must support complex structures and multi-modal understanding to be effective [10][11]. Group 3: Challenges in AI Adoption - Resistance from development teams is common when introducing AI concepts, necessitating a foundational knowledge engineering approach to facilitate acceptance [8][9]. - The integration of AI in operations faces challenges due to the need for human verification in critical processes, as AI cannot yet fully replace human judgment [6][12]. - Trust in AI systems is built gradually through transparency in decision-making processes and collaborative mechanisms [13]. Group 4: Future of Roles and Skills in AI-Driven Environments - The emergence of AI Agents is reshaping traditional roles, with a shift towards a collaborative model where roles may overlap, such as combining demand and testing responsibilities [19][20]. - Developers are encouraged to embrace AI as a tool to enhance their capabilities rather than viewing it as a threat to their roles [32][33]. - The concept of "T-shaped talent" is gaining traction, emphasizing the need for developers to have a broad understanding of the development process while specializing in specific areas [20]. Group 5: Strategic Implementation and ROI Measurement - Companies are advised to start integrating AI in the development phase, where standardization allows for easier incorporation of AI capabilities [23]. - The initial phase of AI implementation often focuses on creating a conducive environment for exploration, followed by demonstrating tangible results to secure further investment [16]. - Measuring the ROI of AI initiatives should encompass efficiency, quality, and the enhancement of human factors, not just productivity metrics [14][15]. Group 6: Future Trends and Innovations - The future of AI in software engineering may see the rise of multi-agent collaboration, where agents can communicate and work together more effectively [26][27]. - The potential for AI to independently manage complex tasks in operations is anticipated, with a shift towards natural language commands for task execution [27][28]. - The evolution of AI capabilities may lead to the emergence of new roles, such as AI application engineers, who will blend traditional development skills with AI knowledge [21][22].

Core Viewpoint - The article discusses the importance of combining both vertical and horizontal strategies in personal and professional development, advocating for the concept of "T-shaped talents" who possess deep knowledge in a few areas while also having a broad understanding of various fields [6][8]. Group 1: Importance of Skills - The future increasingly requires composite skills rather than a single expertise, as industries evolve and new business models emerge [9][11]. - Understanding multiple disciplines, such as communication, management, and marketing, is becoming essential for individuals, especially with the rise of small personal studios [10][11]. Group 2: Role of AI - AI acts as an amplifier of existing skills and knowledge, enhancing efficiency and effectiveness in various tasks [12]. - Individuals with diverse knowledge can better leverage AI to achieve cross-disciplinary integration [12]. Group 3: Knowledge Network Effects - The accessibility of information has lowered barriers to entry in various fields, allowing individuals to gain sufficient understanding without becoming experts [13]. - The potential for innovation increases with the accumulation of knowledge across multiple domains, leading to richer combinations and ideas [13]. Group 4: Personality Traits and Strategies - Vertical strategists tend to be more conscientious but less open to new experiences, making it harder for them to diversify their interests [15]. - Horizontal strategists, on the other hand, are more likely to explore various interests, which can lead to greater potential if given the right opportunities and guidance [14][17]. Group 5: Encouragement for Horizontal Strategists - The environment often favors vertical strategists, which can hinder horizontal strategists from receiving recognition and support [18]. - Horizontal strategists possess a natural curiosity and enthusiasm, which can lead to greater adaptability and resilience in a rapidly changing world [21]. Group 6: Strategies for Development - Engaging in new fields and hobbies can enhance flexibility and resilience, reducing the pressure associated with work [31][32]. - The "15% rule" suggests dedicating a portion of personal time to explore new interests, fostering a more dynamic and adaptable life [28][29]. Group 7: Building a Supportive Environment - Finding a community of like-minded individuals can provide encouragement and a sense of belonging, which is crucial for sustaining motivation [44][45].

Core Insights - The emergence of large language models is considered a significant technological opportunity, potentially greater than any previous industrial revolution [1] - The AI talent competition is intensifying, with companies offering high daily wages to attract interns [2] - The evolution of AI programming skills may disrupt mid-level programming jobs within 3-5 years, necessitating a shift in focus towards problem-posing abilities for future talent [3] Group 1: Company Initiatives - The establishment of Beijing Singularity Intelligence Technology Co., Ltd. by a former executive from Meituan aims to develop AI-powered educational hardware for children [1] - The company's products target children aged 3-8, emphasizing learning through interest-driven play rather than traditional rote methods [5] Group 2: Educational Insights - The importance of interest-driven education is highlighted, with a focus on fostering curiosity and exploration in children [8] - The shift in educational focus from problem-solving to problem-posing is essential in the AI era, as AI tools can easily generate solutions [11] - The need for cross-disciplinary talent is emphasized, transitioning from "T-shaped" to "π-shaped" talent, which combines depth in multiple fields [13][14] Group 3: Future Trends - AI is expected to permeate all aspects of life, making AI literacy a foundational skill for future generations [12] - The necessity for students to engage with AI from an early age is underscored, as they will be the first true "AI natives" [12] - Recommendations for future AI professionals include cultivating interest in AI, engaging in hands-on practice, and mastering foundational knowledge [17]