Guosen: AI education needs to build a closed loop of "teaching-learning-practicing-assessment", continuously track and innovate potential opportunities for breakthroughs.

Guosen's research report suggests paying attention to AI education products that have the ability to deliver services throughout the entire chain and effectively improve the quality of teaching. Drawing on the development history of online education, the ideal AI education product should achieve a closed-loop service of "teaching-learning-practice-assessment" and have verifiable teaching effectiveness. In terms of specific implementation paths, for high school and adult learners who are highly self-driven and have clear quantifiable effects, AI interactive courses and practice system classes are outstanding. For younger students, AI education remains a compliant supply carrier for scarce subject resources after the "double reduction", and industry companies are integrating real-simulated classrooms with emotional interaction technology in hopes of improving teaching effectiveness. In addition, industry players are actively expanding diverse business models, and it is recommended to continue monitoring potential opportunities brought about by innovative breakthroughs. Guosen's main points are as follows: The development history of online education is of reference value to AI education, but the development environment has changed. AI education and online education are both technology-driven supply changes. However, specifically, online education rose to alleviate uneven regional distribution, while the external training supply was cleared after the "double reduction". AI education needs to face a more serious total mismatch between supply and demand, with the core objective shifting towards driving total supply efficiency improvement. Technologically, online education companies benefitted from the 4G technology dividend and have relatively low technical barriers such as live broadcast classrooms; while AI education is the product core, requiring education companies to meet technical requirements, significantly raising the technical threshold. After the "double reduction", domestic education technology VC investments rapidly contracted, and the commercialization path may lengthen, but existing players are more likely to maintain competitive advantages, with AI education led by education industry funds also expected to focus more on education. Changes in online education business models Billion-dollar market value online education companies are concentrated in the online live broadcast track with full chain delivery and relatively strong teaching effectiveness. In the early stages of online education, various products emerged covering different dimensions of education, but historical billion-dollar market value online education companies are concentrated in the online live broadcast track that can frequently meet user needs and directly take responsibility for teaching outcomes. Compared to online live broadcast courses, the demand for in-home tutoring O2O platforms is infrequent, with low teacher and user stickiness; although recorded courses show economies of scale, completion rates are low, and the use of search tools is high, but it remains problem-driven and cannot cover the learning loop. Many of these tools further transition to the online live broadcast model with better teaching effectiveness in the monetization phase. The inspiration for AI education product business models is to transform from a single tool to a full chain service of "teaching-practice-testing-feedback" and emphasize the conversion of final learning outcomes. Evolution of AI education business models, with age and demand-driven differentiated designs Younger students have easily distracted attention, so to enhance learning effectiveness, it is necessary to increase interest and participation (e.g. Bean God AI real classroom, New Oriental Cartoon Foreign Teachers), and visual large models can also be used for attention monitoring. Older users (high school/adult) are self-driven, focusing on efficiency, result verification, and product value; preparing for the college entrance exam and civil service exam both emphasize creating AI practice tools to achieve precise identification and remedy of deficiencies, such as Youdao LingShi fragmented knowledge consolidation, Tianli Qiming AI sprint camp significantly improving scores on average, and FENBIAI system class dynamic path planning. AI question-and-answer tools, similar to search tools, play an important role as high-frequency traffic entries. Intelligent learning tablets serve as compliant subject resource gateways, with high demand but constrained by high hardware costs and marketing expenses, long repurchase cycles, the business model is still in the optimization process; other tools such as electronic dictionaries focus on specific functions. Teacher-side tools significantly reduce costs and improve efficiency, but individual willingness to pay is low, and B-side procurement cycles are long. Risk warning: Data leaks and AI illusion risks; intensified competition; talent drain; policy changes.