Read More《教育 Agent 崛起：AI 正在重塑学习产品的逻辑》
Positive Reviews: Educational Agents are Transforming the Learning Ecosystem and Driving the Intelligent Upgrade of Teaching Paradigms
Educational agents driven by generative AI are evolving from “functional tools” to “participants in the teaching ecosystem”. This transformation is not only the result of technological iteration but also a key driving force for the transformation of educational concepts from “knowledge transmission” to “ability cultivation”. From the innovative cases at the student, teacher, and platform levels mentioned in the news, the rise of educational agents has demonstrated positive value in at least three dimensions.
I. Student Side: Cognitive Upgrade from “Answer Acquisition” to “Thinking Growth”
Traditional educational tools often focus on “answering questions”. Students obtain answers through searching or question banks. However, in this model, learning behavior often remains at the fragmented stage of “result – orientation”. VideoTutor and Khanmigo mentioned in the news have broken through this limitation. VideoTutor visualizes and makes the problem – solving process replayable by generating short – video explanations in real – time, enabling students not only to “know the answers” but also to “review the thinking path”. Khanmigo guides students to reason independently through Socratic questioning, transforming “problem – solving” into a cognitive training process of “questioning – verifying – correcting”. The essence of this design is to transform “learning” from a one – time knowledge consumption into a traceable and accumulative thinking asset.
For example, VideoTutor’s “object – oriented” design (encapsulating the problem – solving process into reusable learning records) actually constructs a personalized “thinking growth archive” for students, which is highly consistent with the “metacognition” theory in educational psychology. Learners can adjust their learning strategies more actively by observing their own thinking processes. Khanmigo’s “cognitive companionship” echoes the constructivist learning theory, emphasizing that knowledge is the result of learners’ active construction in interaction. The innovation of these products makes AI truly become students’ “thinking partners” rather than simply “answer machines”.
II. Teacher Side: Process Reconstruction from “Tool Burden” to “Efficiency Liberation”
Teachers are the most crucial “people” in the educational scenario. However, traditional educational tools often become an “extra burden” for teachers due to complex operations and fragmented processes. The success of Brisk Teaching and MagicSchool in the news lies precisely in solving this pain point. Brisk Teaching is directly embedded in the Google Docs and Slides environments that teachers are familiar with. Through functions such as “one – click lesson plan generation” and “differentiated homework writing”, it seamlessly integrates AI capabilities into the existing workflow. MagicSchool reduces the usage threshold with its “modular design”. Teachers can call modules such as lesson plan generation and classroom activity design according to their needs, avoiding the learning cost of “system migration”.
Data shows that MagicSchool users self – reported saving 7 – 10 hours per week on lesson preparation and grading. The significance of this change goes far beyond efficiency improvement itself. Teachers can devote more energy to classroom interaction and personalized tutoring, which is the key to implementing the “student – centered” teaching concept. More importantly, the design logic of Brisk Teaching and MagicSchool (“naturally existing” rather than “dominant”) reflects respect for teachers’ subjectivity. AI is not a replacement but an “invisible assistant”, and teachers always remain at the core of teaching decision – making.
III. Platform Side: Ecosystem Integration from “Single – Point Tools” to “Intelligent Foundation”
The complexity of the educational scenario determines that a single tool is difficult to cover the full – process requirements. Platform – level integration (such as the educational version of Google Gemini for Workspace) provides the infrastructure for the large – scale application of educational agents. By deeply integrating AI capabilities into the Workspace ecosystem, teachers can not only directly call generation functions in documents and slides but also “train” their own exclusive AI teaching assistants based on their own courseware, transforming static courseware into “conversational learning partners”. The value of this integration lies in building an intelligent teaching environment that is “unified, scalable, and self – operating”. Schools can obtain stable intelligent support within a secure framework without piecing together independent applications. Teachers and students can naturally use AI in a familiar environment, avoiding the sense of fragmentation caused by “switching between multiple systems”.
From an industry trend perspective, platform integration marks a leap in educational informatization from “management informatization” (such as OA systems and grade management) to “teaching intelligence” (such as intelligent lesson preparation and personalized learning). When AI becomes the “default configuration” of the teaching environment, the organizational mode of the education system will be redefined. The boundaries between classrooms, teachers, and technology will become more blurred, and collaboration will be more efficient.
Negative Reviews: Potential Challenges and Concerns of Educational Agents
Although educational agents show great potential, their development still faces multiple challenges. The tension between the “instrumental rationality” of technology and the “humanistic attributes” of education, data privacy risks, the side effects of technological dependence, and the potential impact on educational equity all need to be carefully addressed.
I. Data Privacy and Ethical Risks: “Learning Records” May Become “Digital Shackles”
The core capabilities of educational agents rely on the collection and analysis of students’ and teachers’ behavioral data. For example, Khanmigo records students’ “stumbling blocks, pauses, and misunderstandings”, and VideoTutor generates “problem – solving records”. Although these data can optimize teaching, they may also pose risks of privacy leakage. If the data is misused (such as for commercial marketing or labeling students) or stolen due to technical loopholes, it will cause serious damage to users’ rights and interests.
What is even more alarming is the “data ethics” issue. When learning behavior is fully digitized, students’ “thinking processes” may be simplified into quantifiable indicators (such as “reasoning speed” and “error type”). This “digital gaze” may lead to the simplification of educational evaluation. Dimensions that are difficult to quantify, such as learning creativity and emotional experience, may be ignored. For example, a student marked as “mentally slow” in Khanmigo due to “slow questioning speed” may be misjudged by the system, while the real situation may be that the student is in deep thought.
II. Technological Dependence and Weakening of Subjectivity: “Intelligent Partners” May Degenerate into “Replacements”
Although the “companion – style” design of educational agents can enhance the learning experience, excessive dependence may weaken students’ autonomous learning ability. For example, students who are used to the guided questioning of Khanmigo may be unable to independently sort out problems without AI. Teachers who have long used AI to generate lesson plans may gradually lose their original ability in teaching design. This “technological dependence” may lead to the “degeneration of abilities” of educational subjects (students and teachers), which runs counter to the essential goal of education (cultivating independent thinkers).
In addition, the “authority” of AI may suppress students’ critical thinking. When AI appears as a “professional explainer”, students may default to the correctness of its answers and lack the awareness of questioning and verification. For example, if there are logical errors or knowledge biases in the short – video explanations generated by VideoTutor (although the probability is low), students may accept the wrong information due to their trust in technology, which will actually affect the learning effect.
III. Potential Impact on Educational Equity: The Contradiction between Technological Inclusiveness and the Resource Gap
The promotion of educational agents may exacerbate the “digital divide”. The news mentioned that MagicSchool covers most school districts in the United States, and Brisk Teaching has received $15 million in financing. Behind these cases is the technological and financial advantage of leading enterprises. However, in areas with scarce educational resources (such as remote schools in developing countries and weak urban schools), schools may be unable to afford or integrate these AI tools, resulting in the “technological dividend” being monopolized by a minority group.
More importantly, the difference in teachers’ “digital literacy” may magnify the unfairness. Teachers who are familiar with technology can use Brisk Teaching or MagicSchool more efficiently, while teachers lacking training may give up using them due to operational difficulties, which will affect the teaching quality. This difference in “technology – using ability” may further widen the gap in educational quality between schools and regions.
IV. Blurred Boundaries of Human – Machine Collaboration: How to Preserve the “Warmth” of Education?
Education is not only about knowledge transmission but also a process of emotional connection and value guidance. Although AI can optimize the teaching process, it is difficult to replace teachers’ “humanistic care”. For example, students’ anxiety and sense of frustration in learning need teachers’ empathy and encouragement. Sudden interactions in the classroom (such as an unexpected question from a student) require teachers’ flexible responses. If educational agents are overly involved in these scenarios, the classroom may become “mechanical” and “cold”.
For example, when AI becomes students’ main “thinking partner”, in – depth communication between teachers and students may decrease. When teachers rely on “standardized lesson plans” generated by AI, their personalized teaching styles may be eliminated. The “warmth” of education comes precisely from the real interaction between people, which technology cannot fully replicate.
Suggestions for Entrepreneurs: Anchor on the Essence of Education and Build “Warm Intelligence”
The innovation of educational agents should always revolve around the core goal of “cultivating people” and balance technological efficiency with the essence of education. Combining news cases with industry pain points, entrepreneurs can focus on the following aspects:
I. Go Deep into the Scenario and Solve Real Needs Instead of “Showing off Technology”
The complexity of the educational scenario far exceeds that of other fields. Entrepreneurs need to avoid the misunderstanding of “using technology for the sake of technology”. For example, the success of Brisk Teaching lies in “embedding into teachers’ existing workflows” rather than creating a new platform. The value of Khanmigo lies in “Socratic guidance” rather than “quick problem – solving”. Entrepreneurs should conduct in – depth research on the real pain points of students and teachers (such as teachers’ lesson – preparation pressure and students’ thinking obstacles) to ensure that the technology solves “real needs” rather than “pseudo – needs”.
II. Attach Importance to Data Privacy and Ethical Design to Build User Trust
Data is the core resource of educational agents, but “compliance” and “transparency” are prerequisites. Entrepreneurs need to establish strict data security mechanisms (such as encrypted storage and minimum collection) and clearly inform users of the data usage (such as only for optimizing teaching and not for commercial purposes). At the same time, add a “data control right” function to the product design (such as students can independently delete learning records and teachers can choose whether to share behavioral data) to avoid the trust crisis caused by “data hegemony”.
III. Balance Technological Empowerment and Subject Ability Cultivation to Avoid the “Replacement Trap”
The essence of education is to “empower people” rather than “replace people”. Entrepreneurs need to reserve “autonomous space” for students and teachers in product design. For example, AI explanation tools can set an “independent exploration” mode (students think independently first and then view AI analysis). Teacher – side tools can retain a “manual modification” function (teachers can freely adjust the lesson plans generated by AI). Through a hierarchical design of “assistance – autonomy”, avoid users’ excessive dependence on technology.
IV. Promote the Co – construction of an Open Ecosystem to Promote Educational Equity
The inclusiveness of educational resources requires ecosystem collaboration. Entrepreneurs can lower the usage threshold in underdeveloped areas through “lightweight tools” (such as free basic functions + paid value – added services); cooperate with educational public welfare organizations to provide technical training for remote schools; open API interfaces to allow schools or teachers to customize AI functions based on their own data (such as the “exclusive teaching agent” model of Google Gemini). Through ecosystem co – construction, let the technological dividend cover a wider range of groups.
V. Preserve the “Humanistic Warmth” of Education and Strengthen the “Emotional Connection” of Human – Machine Collaboration
Technology cannot replace the emotional interaction between people. Entrepreneurs need to integrate “humanistic design” into their products. For example, AI learning companions can add an “emotional feedback” function (such as giving encouraging words when students successfully solve a problem instead of just mechanically indicating “correct”). Teacher – side tools can add a “classroom interaction suggestion” module (such as prompting teachers to pay attention to a certain student based on students’ emotional data). Through the combination of technology and humanity, make educational agents become “warm partners” rather than “cold machines”.
Conclusion: The rise of educational agents is an inevitable trend of the deep integration of technology and education. Their value lies not only in improving efficiency but also in promoting the transformation of the teaching paradigm towards “student – centered” and “ability – cultivation – centered”. However, the “tool attribute” of technology determines that it is always an “assistant”, and the essence of education is always “people cultivating people”. Entrepreneurs need to anchor on the essence of education and inject humanistic care into technological innovation so that educational agents can truly become “co – builders of the educational ecosystem” rather than “foreign invaders”.
- Startup Commentary”Building LLMs: The Knowledge Graph Foundation Every AI Project Needs”
- Startup Commentary”The 17th Year of Tmall Double 11 and the New Map Rewritten by AI”
- Startup Commentary”How to Prepare Your Data for Artificial Intelligence”
- Startup Commentary”Small and Medium-sized Banks: “Cutting the Tail” in Loan Assistance”
- Startup Commentary”The Six AI Giants on Stage: AGI Is No Longer a “Future” Thing”
