2024 AIGC + Education Industry Report: Human-computer Co-nurturing
The last newsletter offered an overview and insights into the intersection of AI and education, showcasing general trends and data. For this newsletter, we will delve deep into the educational aspects with details, covering topics like the Contemporary Educational Framework, Teaching Entities, Teaching Platforms, Educational Content, and AIGC products in education.
Abstract
The robust capabilities of AIGC technology in terms of knowledge volume, information retrieval, and processing force the education sector to further reflect on existing educational frameworks. Regarding the teaching entities, AIGC brings expectations of human-machine collaborative teaching and teacher empowerment, while also sparking discussions on AI challenging the role of teachers. Concerning the teaching platform, AIGC is expected to empower teachers and achieve scalable personalized teaching, but it also challenges traditional learning modes and assessment tools. In terms of teaching content, the importance of high-order general capabilities and interdisciplinary composite abilities is emphasized, supplemented by requirements for AIGC technological literacy. Regarding the learning subject, it prompts philosophical speculations bordering on science fiction: educating humans versus training large-scale models, potentially implying a broad competition for educational resources.
AIGC reshapes the contemporary education framework
The advent of ChatGPT initially sparked widespread concerns about academic dishonesty among students at universities worldwide, leading education departments and universities in various countries and regions to restrict the use of generative AI technologies. However, the initial anxiety has gradually given way to rationality as universities gain a deeper understanding of the technology. Many have started to explore and assess the potential applications of generative AI technology in teaching and academic research, contemplating how to use advanced technology effectively and responsibly while ensuring academic integrity and data security. Over the past year, discussions around the application of AIGC in the global educational and academic community have been abundant, diverse in perspective, and varied in opinion. It can be said that the emergence of AIGC technology has forced a reconsideration of contemporary educational frameworks: with a defined and regulated application of AIGC technology, the education system can determine what kind of teaching entities, through what methods, and what kind of talent should be cultivated to adapt to this world that is rapidly advancing on the chariot of technology, yet uncertain of its direction.
AIGC Influences Teaching Entities
The impact of AIGC on teaching entities is a compelling topic within discussions on educational transformation. In traditional teacher-student teaching models, teachers are the authoritative conveyors of knowledge. However, this model is challenged by issues such as uneven teacher resources, difficulties in personalized teaching, and the heavy burden of tedious tasks on teachers, hindering their growth. The introduction of AIGC technology is viewed as an opportunity, signifying the potential shift from traditional teaching models to a "teacher-student-machine" format. AIGC promises to supplement teacher resources, enable large-scale personalized teaching, and alleviate teacher workload, thereby enhancing the feasibility of improved teaching methodologies. This has been widely recognized by the academic community and the market, allowing human teachers to transition from traditional knowledge disseminators to companions in learning and guides in values.
Nevertheless, concerns about "AIGC fully replacing human teachers" also exist. The non-neutrality of AIGC values, inadequacies in information accuracy, copyright issues, and the irreplaceability of human emotions and cultural values pose challenges to AIGC's sole efficacy. Moreover, the potential for AIGC to entirely usurp human teachers' roles may trigger employment anxieties among educators. The debate on both sides indicates that the supportive application of AIGC for teachers needs to be carefully defined to ensure it serves as an aid rather than a hindrance to the educational process.
The educational delivery layer, encompassing both hardware foundations and tools, has always been at the forefront of technological integration. Thanks to the continuous improvement of information infrastructure across institutions at all levels within the country, the impact of AIGC technology on the educational delivery aspect is becoming increasingly pronounced. The intervention of AIGC technology at the teaching level makes advanced educational concepts such as large-scale personalized instruction more viable; simultaneously, the feasibility of AIGC in enhancing teaching methodologies and educational research has gained wide recognition. However, the application of this technology comes with challenges and controversies: potential inaccuracies and value biases in AIGC, the risk of technological dependence from prolonged use by students, and the pressures on assessment integrity due to cheating facilitated by AIGC, have led to widespread skepticism or opposition within the educational community towards the direct use of AIGC by students, especially among adolescents and children. This calls for conditional use and effective regulation of AIGC as a learning tool. Thus, amidst the coexistence of opportunities and challenges, educators must deeply consider the functional system and ethical boundaries of AIGC-based educational platforms while adapting to technological advancements and adopting more advanced, fair, comprehensive, and balanced technological tools. This is to ensure that such technology can serve the entire educational system in a healthy and effective manner.
The importance of advanced general abilities and interdisciplinary compound capabilities is being emphasized, supplemented by the requirements of AIGC technology literacy.
The emergence of next-generation AI technologies has significantly enhanced social productivity while also triggering changes in the mode of production. The social division of labor is undergoing restructuring, with some traditional positions being replaced by AI while new professions emerge rapidly, leading to the gradual phasing out of certain disciplines. The AI-driven era presents a vast and formidable research topic for the education system: How should we cultivate talents capable of adapting to this transformation? This not only involves adjustments to educational scope, objectives, and content but also entails changes in the arrangement of academic disciplines. The "Scheme for the Adjustment, Optimization, and Reform of Undergraduate Discipline Setting in General Higher Education Institutions," released in March 2023, further specifies the establishment of a series of new disciplines by 2025 to adapt to new technologies, industries, formats, and models, while eliminating those disciplines no longer aligned with the demands of economic and social development. Moreover, large-scale models like ChatGPT have gradually approached or even surpassed human capabilities in various examinations such as certified public accountants and lawyers, prompting us to reconsider the social adaptability of simply teaching basic professional knowledge in the AI-driven era. Therefore, the education system needs to adjust its curriculum, placing emphasis on cultivating information literacy and high-level interdisciplinary competencies, gradually increasing the importance of interdisciplinary integrated education to expedite the supply of cutting-edge talents. Additionally, the education system must promptly clarify the scope and norms of learning surrounding AIGC technology, aiding learners in accepting and adapting to the advent of the AI-driven era while comprehending the technology and its extensive impact on the world. This will ensure that future technological talents can steer the direction of technology and its development pace, and possess the capability to utilize AIGC technology to better transform the world and benefit humanity.
Competition between AIGC and Learning Subjects
Prompting almost science-fictional yet attainable philosophical contemplation: educating humans or training large models?
The rapid advancement of AIGC technology is significantly boosting the socio-economic landscape. Goldman Sachs forecasts that generative AI could contribute a massive $7 trillion boost to global GDP over the next decade, leading to a 7% annual increase in global GDP. By 2023, the AIGC sector has already attracted substantial investment, with global AIGC venture capital reaching $15 billion as of July. However, the high efficiency of AIGC in the labor market has raised widespread concerns about job security. Goldman Sachs predicts that 300 million jobs globally could be at risk of displacement due to AI technology application in the future. While some argue that AIGC technology could shorten working hours and enhance worker welfare, actions like Google's decision to cut 30,000 positions to adapt to AI integration raise public concerns about unemployment. Although AIGC appears to offer more efficient and cost-effective learning and labor compared to humans, excessive reliance on AIGC while neglecting the value of human education and labor is a risky proposition. Faced with rapid technological advancements, society needs to carefully plan the direction of technological applications and formulate reasonable education and labor protection policies to ensure that AIGC brings more social equity rather than exacerbating inequality. Maintaining a long-term perspective on human well-being and social stability while pursuing technological innovation is crucial.
Technology Commercial Diffusion
The AIGC technology plays a significant role across various roles such as teachers, students, and administrators, spanning multiple scenarios including academic research, lesson planning, homework generation and grading, self-directed learning, auxiliary practice, and testing assessment. In terms of implementation speed, it demonstrates a trend from consumer (C) to business (B) to government (G), with adult education > higher education > K12 > early childhood education, and prioritizes teachers > students > administrators. Specifically, the full-process closed-loop services for teacher-student applications and AIGC academic research with high complexity and a high ceiling are potential opportunity directions. In terms of business models, current models include software value-added services, integrated hardware sales, MaaS (Model as a Service), and AIGC skills training. Companies differentiate themselves based on factors such as model and computing power, understanding of educational business, and educational data. Looking ahead to future competitive dynamics, AIGC technology exhibits significant resource intensity and dependency characteristics. The "brute force" aesthetic approach has been validated by the industry, with major players expected to dominate the future landscape due to their accumulation of various types of resources. Innovative enterprises can enter the market by deeply understanding specific scenarios, but those without proprietary large models may still be limited. Meanwhile, the relationship between general large models and education-specific large models is evolving towards specialization and integration, potentially leading to a future with multiple combinations of general and expert models.
Reference: iResearch. (2024). 2024 AIGC + Education Industry Report. Retrieved from https://www.iresearch.com.cn/Detail/report?id=4312&isfree=0
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Author: Yirui (Sandy) Jiang
Chief Editor: Yirui (Sandy) Jiang