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Harvard Professor Trains Claude to Become a Second-Year Physics Grad Student in Just Two Weeks
The rapid evolution of artificial intelligence in academic research is pushing the boundaries of human imagination.
In a recent experiment, Professor Schwartz of Harvard University achieved a remarkable feat: after two weeks of “mentorship” training, he turned the AI model Claude into a researcher on par with a second-year physics graduate student. This shows that large language models are moving beyond simple knowledge retrieval to become research partners capable of deep involvement in cutting-edge scientific exploration.
The Evolution Path: From “Novice” to “Independent Researcher”
Over the 14-day experiment, Claude demonstrated a learning curve closely resembling that of a human graduate student:
Task Decomposition: When faced with complex physics problems, Claude actively collaborated with models like GPT-5.2 and Gemini3.0 to organize ideas, breaking the main topic into 102 smaller tasks.
Intensive Dialogue: During the experiment, the tutor engaged in roughly 270 in-depth conversations with the AI, using about 36 million tokens.
Research Paper Iteration: After 110 drafts, the AI independently produced a professional-grade research output.
The Tutor’s Role: Humans Provide Only “Guidance” and “Correction”
Throughout the research, Professor Schwartz acted purely as a “tutor”:
Setting Boundaries: He pointed out logical errors, defined research boundaries, and steered the overall direction.
Refusing “Ghostwriting”: The professor never intervened in specific calculations or derivations; the AI tackled all core challenges independently.
Targeted Solutions: When the AI occasionally took shortcuts or skipped steps, the professor guided it to self-correct with precise reminders.
New Research Paradigm: “AI Postdoctoral Fellow” Handling Dual Tasks
During the critical phase of the experiment, Claude exhibited a multi-tasking capability that would be challenging for humans: while deriving complex physical formulas, it simultaneously wrote the underlying computational code. This dual-track collaboration between “theoretical derivation” and “programming computation” drastically shortened the research cycle.
Conclusion: The Dawn of the AI Graduate School Era
This experiment by the Harvard professor sends a clear signal to the academic community: AI has already acquired the ability to handle high-level, non-standardized research tasks. When large models can rapidly grow through “hands-on experience” like graduate students, future scientific discoveries may enter an “autonomous driving” era where humans set directions and AI handles deep execution.
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The rapid evolution of artificial intelligence in academic research is pushing the boundaries of human imagination.
In a recent experiment, Professor Schwartz of Harvard University achieved a remarkable feat: after two weeks of “mentorship” training, he turned the AI model Claude into a researcher on par with a second-year physics graduate student. This shows that large language models are moving beyond simple knowledge retrieval to become research partners capable of deep involvement in cutting-edge scientific exploration.
The Evolution Path: From “Novice” to “Independent Researcher”
Over the 14-day experiment, Claude demonstrated a learning curve closely resembling that of a human graduate student:
Task Decomposition: When faced with complex physics problems, Claude actively collaborated with models like GPT-5.2 and Gemini3.0 to organize ideas, breaking the main topic into 102 smaller tasks.
Intensive Dialogue: During the experiment, the tutor engaged in roughly 270 in-depth conversations with the AI, using about 36 million tokens.
Research Paper Iteration: After 110 drafts, the AI independently produced a professional-grade research output.
The Tutor’s Role: Humans Provide Only “Guidance” and “Correction”
Throughout the research, Professor Schwartz acted purely as a “tutor”:
Setting Boundaries: He pointed out logical errors, defined research boundaries, and steered the overall direction.
Refusing “Ghostwriting”: The professor never intervened in specific calculations or derivations; the AI tackled all core challenges independently.
Targeted Solutions: When the AI occasionally took shortcuts or skipped steps, the professor guided it to self-correct with precise reminders.
New Research Paradigm: “AI Postdoctoral Fellow” Handling Dual Tasks
During the critical phase of the experiment, Claude exhibited a multi-tasking capability that would be challenging for humans: while deriving complex physical formulas, it simultaneously wrote the underlying computational code. This dual-track collaboration between “theoretical derivation” and “programming computation” drastically shortened the research cycle.
Conclusion: The Dawn of the AI Graduate School Era
This experiment by the Harvard professor sends a clear signal to the academic community: AI has already acquired the ability to handle high-level, non-standardized research tasks. When large models can rapidly grow through “hands-on experience” like graduate students, future scientific discoveries may enter an “autonomous driving” era where humans set directions and AI handles deep execution.
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