1. Introduction

Artificial Intelligence (AI) emotion recognition technology continues to advance, enabling precise detection of human emotional signals. By analyzing multimodal data such as speech, text, and images, AI can identify a range of emotions, including joy, sadness, and anger. Beyond recognition, AI is also capable of generating emotionally rich content. Advances in natural language processing allow AI to compose text with specific emotional tones, such as empathetic customer service responses or compelling marketing copy. In the realm of images and videos, AI can also produce works imbued with emotional resonance. For instance, some advertising agencies leverage AI to create emotionally engaging promotional videos that capture consumer attention and evoke deeper connections. However, several research gaps remain in this field. First, there is a disconnect between theory and practice in cross-cultural affective computing, including biases in micro-expression databases, cross-cultural disparities in emotional semantics, and the influence of cultural values on emotional needs. Second, there is a lack of research on the emotional needs of special populations, such as the conflict between the "predictability needs" of individuals with autism spectrum disorder in emotional interactions and AI's "dynamic responsiveness." Additionally, existing studies predominantly focus on technical implementation while lacking assessment models for user psychological compatibility, as well as research on the intergenerational digital divide affecting elderly populations. Finally, there is a scarcity of neuroscientific evidence on the long-term effects of AI-driven emotional services.

This paper examines the impact of AI on the emotional economy and its subsequent influence on students' career development. The goal is to provide market predictions and guidance for students' future career paths.

2. Current state of AI development

In recent years, AI technology has rapidly evolved, deeply integrating into various sectors of society and profoundly transforming people's lifestyles and work patterns. By 2025, AI has demonstrated distinctive characteristics in terms of technology, industry applications, and market development. At the technological level, AI is transitioning from the stage of informational intelligence to embodied intelligence. While informational intelligence excels at processing data, it cannot directly influence the physical world. Embodied intelligence, however, enables AI to perceive and interact with the physical environment. Since 2024, AI has made significant breakthroughs in deep search, chain-of-thought reasoning, and multimodal fusion. Among these, deep multimodal integration has emerged as a key trend, allowing AI to simultaneously process text, images, speech, and video data, greatly advancing applications in autonomous driving, smart healthcare, and other fields. For example, in 2024, an AI-generated short film co-created by users from 60 countries showcased the immense potential of multimodal generation technology in content creation [1]. Additionally, edge AI, supported by 5G and IoT technologies, has gained widespread adoption, enabling real-time local decision-making on terminal devices and finding extensive use in smart homes, industrial automation, and more. In terms of industry applications, AI has become a core productivity tool for most enterprises. Data indicates that over 70% of companies have adopted AI technologies. In manufacturing, CITIC Group has deployed AI across its 29 global production bases for process optimization and quality control. In the creative industry, the text-to-video platform Kling AI has attracted 22 million creators to participate in user-generated projects. In education, higher education institutions have introduced AI teaching assistants and virtual simulation platforms to enable personalized learning pathways and data-driven teaching decisions. From a market perspective, the global AI industry is experiencing robust growth. According to the China Internet Development Report 2024, China has established 421 national-level smart manufacturing demonstration factories, hosts over 4,500 AI companies, and ranks second globally in computing power scale [2]. The 2025 China AIGC Application Panorama Report notes that China's generative AI user base has exceeded 300 million, with over 120 million monthly active users [3]. In terms of investment, the AI Index Report 2025 reveals that private investment in the U.S. AI sector reached $109.1 billion in 2024, while global generative AI attracted $33.9 billion in private investment, marking an 18.7% increase from 2023 [4]. The current state of AI development demonstrates remarkable vitality and potential. Despite existing challenges, continuous technological breakthroughs, expanding applications, and maturing markets ensure that AI will play a pivotal role in more fields, unlocking new possibilities for societal progress.

3. The impact of AI on the emotional economy

The concept of the emotional economy represents an emerging scientific paradigm where emotion-laden tasks are beginning to dominate the labor market [5]. Specifically, it is characterized by employment and wages associated with feeling tasks surpassing those related to thinking or mechanical tasks [6]. This shift is largely driven by the rapid advancement of AI, which focuses on developing systems capable of replicating human cognitive abilities and performing tasks traditionally executed by humans [7].

As AI is designed to mimic human intelligence, it increasingly demonstrates potential in performing mechanical tasks, cognitive tasks, and—more notably—emotional tasks, sometimes even surpassing human capabilities in these domains [8]. Currently, AI is deeply involved in economic activities centered around consumers' emotional needs, becoming a driving force behind new consumption experiences and the reshaping of social relationships.

The emotional economy emphasizes emotional experiences, emotional resonance, and the creation of emotional value. Leveraging its powerful capabilities in data processing, learning, and simulation, AI is facilitating structural transformations across multiple dimensions of the emotional economy.

3.1. Current applications of AI technology in the emotional economy

AI technology has been widely adopted across multiple scenarios in the emotional economy, including intelligent customer service, virtual brand IPs, and creative content generation. For instance, JD.com's AI customer service assistant "JIMI" utilizes natural language processing and emotion recognition technologies to detect emotional nuances in user inquiries and adjust its communication strategies accordingly. During major sales events, this system can handle millions of customer inquiries daily with a resolution rate reaching 85%, significantly alleviating the workload of human customer service representatives. Furthermore, AI-powered virtual brand ambassadors are emerging as a new approach for building emotional connections with consumers. L'Oréal's virtual makeup advisor "Mica," engages users through a friendly persona and professional expertise. The system can recommend products based on users' skin conditions while employing affective computing models to perceive emotional needs, thereby providing personalized makeup tutorials and fashion advice.

3.2. Three mechanisms through which AI drives the emotional economy

The evolution of the emotional economy through AI is primarily achieved via three key mechanisms:

(1) Precise Emotion Insight Driving Personalized Services: AI technologies leverage emotion recognition and data analytics to accurately capture users' emotional states and preference patterns. In e-commerce, Taobao's personalized recommendation system comprehensively analyzes multi-dimensional data, including browsing history, search queries, and product reviews, to identify emotional tendencies. For instance, users showing preference for "healing-style" greenery products receive recommendations for complementary decorative items, effectively stimulating purchase desire while increasing click-through and conversion rates by 12-15%. This emotion-aware personalization not only fulfills emotional needs but also generates substantial commercial value, propelling the consumption dimension of the emotional economy.

(2) Enhanced Emotional Interaction Improving Service Quality: AI-powered virtual assistants and smart interaction products significantly upgrade emotional engagement experiences. JD.com's JIMI customer service demonstrates emotional intelligence by detecting frustration during complaints and dynamically adjusting response strategies to resolve issues while providing emotional comfort. During peak sales events, it handles millions of daily inquiries with 85% resolution rates, reducing human agent workload by 40% while boosting customer satisfaction scores by 18 points. Such AI-driven interactions deliver both functional efficiency and emotional fulfillment, strengthening brand loyalty and advancing service sector development within the emotional economy.

(3) Emotion-Centric Product Innovation Creating New Markets: AI is spawning novel emotional product categories and service models. In content creation, Kling AI's deep integration with film, web series, and advertising enables high-efficiency production of emotionally resonant content, achieving 113% month-over-month global user growth in January 2025. The embodied humanoid robot "Aiqiu EMO1," equipped with affective cognition systems featuring micro-expression recognition, contextual analysis, and haptic feedback, is poised to establish new emotional consumption verticals. These innovations are dissolving traditional industry boundaries, attracting new consumer participation, and expanding the emotional economy into unprecedented territories.

3.3. Future trends in the development of the emotional economy

As AI continues to deepen its integration with the emotional economy, four major trends are emerging: First, AI-powered emotional interactions will reshape commercial and social relationships. With artificial intelligence now capable of simulating empathy, affective computing will become increasingly sophisticated in identifying, analyzing, and responding to user emotions [9]. This technology will see widespread application across customer service, healthcare, education, and other sectors. Second, the metaverse and virtual emotional economy will rise. New forms such as digital humans, virtual companions, and emotional NFTs will emerge in virtual worlds, where users may pay for "virtual companionship" or "emotional memorabilia." Social platforms will increasingly rely on algorithms to analyze users' emotional preferences, potentially exacerbating information cocoons and emotional polarization. Third, emotional labor will become further commodified while facing ethical challenges. Business models like "Emotion-as-a-Service" may become mainstream, including professional virtual partners and smile-rating systems. However, issues of emotional exploitation in the gig economy (such as delivery workers' emotional management pressures) will spark more social debate, potentially driving new labor protection policies.

Finally, global variations in emotion economies will create cultural contrasts and conflicts. Western individualistic approaches (like "self-care consumption") will contrast with Eastern collective emotional cultures (such as "family honor marketing"). Meanwhile, tech giants' globalization may risk "emotional colonialism," where dominant emotional expressions suppress local cultural norms. Looking ahead, emotional economy must balance technological innovation with ethical governance-maximizing the commercial value of emotions while protecting user privacy and emotional laborers' rights. These trends will profoundly transform human-computer interaction, consumer behavior, and social governance, making this a defining issue of the digital economy era.

4. Impact of the emotional economy on students' future career development

The emotional economy is fundamentally reshaping the structure of the job market and talent demands. This transformation presents new challenges and requirements for students in terms of career preparation and skill development.

4.1. Restructuring of the job market by the emotional economy

The emotional economy has spawned numerous emerging professions centered around "emotional value creation." Virtual digital human operators have become highly sought-after professionals, responsible for designing emotional interaction scripts for virtual idols and corporate digital ambassadors. According to Zhaopin data, job postings for this role surged by 320% year-over-year in 2024, with leading internet companies offering annual salaries exceeding 400,000 RMB to attract experienced talent. AI emotion interaction designers focus on technical implementation of human-machine emotional connections. For instance, the Sichuan-based embodied robotics company requires these professionals to possess both AI algorithm capabilities and psychological modeling skills for their "Aiqiu EMO1" project, with entry-level salaries typically above 25K RMB/month. Traditional industries like customer service and education are undergoing "emotional skill transformation." Smart customer service trainers now go beyond script design-they must teach AI systems to recognize emotional scenarios like "anxious inquiries" or "angry complaints" by annotating millions of user dialogues. JD.com's "JIMI" customer service team improved resolution rates to 85% through emotional labeling systems. These roles demand NLP expertise and emotion management knowledge, commanding salaries 2-3 times higher than traditional customer service positions. Emotion-driven product managers have become essential in retail. Data from an FMCG company shows projects led by emotionally intelligent product managers achieved 40% higher repurchase rates, with 35% salary premiums for these professionals. The workplace skill system is shifting from "specialization-focused" to "emotion + technology" hybrid competencies. Digital emotion analysts must master both SPSS data analysis and Jungian psychological typology. For example, Yuewen Group's "Dream Island" team optimizes virtual character dialogues through emotional word frequency analysis of user chats, creating intense competition for such talent. Immersive experience designers combine VR technology with emotional storytelling skills. In one cultural tourism company's AI virtual travel project, designers simultaneously build 3D environments and craft emotional narratives around themes like "nostalgia" or "adventure," with "emotional scriptwriting" and "Unity engine development" listed as equally critical skills (average monthly salary: 30K RMB). Finally, the emotional economy is fueling growth in freelance and platform-based work. Independent emotional content creators on AI companion platforms like Glow and Celebrity Moments earn over 100,000 RMB/month crafting customized virtual character dialogues. Meituan data shows a 280% year-over-year increase in "emotional companionship service" gig workers in 2024, forming a new platform-driven gig economy ecosystem.

4.2. New skill requirements for students in the emotional economy

With the transformation of occupational patterns in the emotional economy, students need to focus on developing the following core competencies in their career development:

First, interdisciplinary knowledge integration capability must be cultivated. Emerging positions generally require compound knowledge structures. For instance, AI emotion interaction designers need both AI technical skills and affective psychology knowledge, while digital emotion analysts must master both data analysis and psychological typology theories. Therefore, students should not limit themselves to single-discipline learning but actively pursue cross-disciplinary courses-computer science students could minor in psychology, and marketing students should supplement their knowledge with data analytics. This approach builds a comprehensive knowledge system to meet the interdisciplinary demands of emotional economy positions [10].

Second, technical tool application capability is essential. As AI, VR, and big data technologies become widely used in the emotional economy, proficiency in relevant tools becomes crucial. Students need to actively learn data analysis software like Python and SPSS, scene modeling tools like Unity and 3D Max, as well as AI technology frameworks including NLP and image recognition. Simultaneously, they should stay updated on technological trends and participate in both online and offline technical training and certification programs to enhance practical technical skills, thereby better qualifying for positions such as immersive experience planners and intelligent customer service trainers.

Third, emotional insight and expression capabilities are vitally important. Meeting consumers' emotional needs is the core driver of the emotional economy. Students should improve their emotion recognition and understanding abilities through psychology studies and practical activities, while strengthening their writing, visual storytelling, and emotional resonance script creation skills.

Finally, innovation and problem-solving abilities are indispensable. The emotional economy continuously generates new business models and consumption scenarios, requiring students to possess innovative thinking and problem-solving capabilities. Through participation in innovation and entrepreneurship competitions and corporate project practices, students can develop the ability to identify users' emotional pain points and propose innovative solutions, thereby enhancing their adaptability and practical capabilities in a rapidly changing market environment.

5. Conclusion

In the context of AI development and the rise of the emotional economy, artificial intelligence has profoundly engaged in economic activities centered on consumers' emotional needs, driving multi-dimensional transformations in the emotional economy. AI technologies have been widely applied in scenarios such as intelligent customer service and virtual brand IPs, promoting the development of emotional economy through three key mechanisms: precise emotional insight, optimized interactive experiences, and product innovation. Looking ahead, AI-powered emotional interactions will reshape commercial and social relationships, while the metaverse and virtual emotional economy emerge. The commodification of emotional labor will present ethical challenges, and global variations in emotional economies will create cultural contrasts and conflicts. The emotional economy is transforming the job market by creating emerging professions like virtual digital human operators, restructuring emotional skills in traditional positions, shifting toward "emotion + technology" hybrid competencies, and fostering freelance and platform-based employment models.

Facing these AI-driven trends in emotional economic development, students encounter three major career challenges: First, the increasingly complex knowledge systems and difficulties in interdisciplinary integration require personalized learning pathways focused on core competencies. Second, the rapid pace of technological advancement creates gaps in academic curricula, necessitating self-directed learning to stay current with technological frontiers. Third, the high risks and insufficient protections in flexible employment models demand early adaptation to new work paradigms while enhancing self-management and career planning capabilities. To address these challenges, students should emphasize the synergistic development of interdisciplinary integration capabilities, technological adaptability, and career resilience-cultivating comprehensive competencies that align with the evolving demands of the emotional economy.