AI on Campus

TL;DR: Four students from top universities discuss the current state, challenges, and innovative applications of AI on campus, emphasizing that how AI is used reflects students' learning motivations and that responsibility lies with students themselves in leveraging tools to achieve personal goals. ## The Campus AI Landscape: Gray Areas and Polarization Currently, the adoption of AI in higher education is extremely high and diverse in its applications. Surveys indicate that approximately 90% of students use AI in their daily academic workflows for tasks such as summarizing lecture content, solving problem sets, and receiving feedback on assignments. However, this widespread adoption has not resulted in a unified code of conduct. University policies are still evolving; some courses prohibit AI use while others encourage it, leaving students in a "gray area" where they are often unclear about how to use this tool appropriately. Attitudes toward AI vary significantly across disciplines, leading to a phenomenon of "identity polarization": * **Business and Social Sciences:** Students extensively use AI for business case studies, market research, and financial analysis. Among graduate students, AI is also employed to rapidly complete quizzes to manage the pressure of multitasking. * **Humanities:** Because courses in the humanities emphasize close reading of texts, many students in this field choose to avoid AI entirely. * **Computer Science and Engineering:** Although AI coding assistants are heavily used in non-academic, real-world project building, using AI in coursework remains taboo due to professors' discouragement. For example, at UC Berkeley, professors currently prohibit enabling AI features in VS Code. However, as institutions like Stanford begin introducing related courses, this boundary is expected to shift in the coming years. ## Lowering the Barrier: Empowering Non-Technical Students One of the most significant breakthroughs of AI tools is the drastic reduction in the barriers and accessibility of software development. Take Claude Code, for example: it enables students without a computer science background (such as those majoring in political science, psychology, or mathematics) to go from concept to a deployable prototype in just a few days. This shift has had tangible impacts on campus: * **Widespread Use of the Terminal:** Students who previously lacked confidence with raw code are now becoming proficient with command-line interfaces. * **Club Development:** For instance, at the London School of Economics (LSE), student clubs are no longer limited to basic Instagram pages; instead, they are using AI to build independent, information-rich websites. * **Interdisciplinary Collaboration:** Non-technical students can now independently complete projects that previously required specialized technical backgrounds, creating unprecedented possibilities. ## Claude Builder Club: Student Innovation in Practice As Anthropic Claude Campus Ambassadors, the four interviewees lead "Claude Builder Clubs" at their respective universities. They shared several typical examples of students using AI to address pain points or unleash creativity: 1. **"Princeton Prospect":** A gamified checklist app developed by a group of freshman dormmates, designed to track items graduates want to complete before leaving. The project's success stemmed from deep humanistic insights rather than technical complexity. 2. **Smart Lecture Assistant:** A tool that allows users to upload slides and generates annotations similar to those of a professor. It anticipates student questions and provides contextual definitions for abstract concepts, greatly aiding final exam review. 3. **Course Registration Alert ("Courseer"):** Addressing the issue of popular courses filling up instantly, this AI tool monitors course availability and notifies students immediately when spots open up, helping them secure a seat. 4. **Library Seat Alternative:** To tackle the shortage of library seats, students built a tool that scans data on empty classrooms, directing students to other available rooms when the library is full. 5. **Healthcare Use Cases:** During hackathons, students combined computer vision with the Claude API to develop tools that identify signs of stroke or dementia via camera and interpret emotions to support mental health. ## AI Usage Reflects Learning Motivations The interviewees believe that how students use AI deeply reflects their core motivations for attending college. Generally, undergraduate goals can be categorized into three types: 1. **Deep Learning:** Deepening understanding of their chosen subject. 2. **Career Positioning:** Securing a good job. 3. **Social Experience:** Enjoying college life and social interactions. AI usage patterns are highly correlated with these goals: * **Substitutional Use:** Some students use AI to complete assignments entirely on their behalf, saving time to invest in other areas (such as socializing or job hunting). While efficient, this approach may lead to superficial knowledge. * **Augmentative Use:** Other students actively use AI as a thinking partner or catalyst for brainstorming, multi-perspective analysis, or reinforcing understanding. These students are typically committed to gaining deep knowledge. ## Responsibility Lies with Students: Balance and Intent AI can be a powerful tool for learning, or it can become a crutch that hinders it. The key lies in students' "intent" and "self-accountability." * **From Passive to Active:** Initially, students tended to directly accept the first answer provided by AI, which led to low quality in group projects. Over time, students have realized the need to invest more effort, treating AI as a starting point rather than an endpoint. * **The Importance of Intent:** Before using AI, students should clarify whether they want AI to complete the task directly or provide different perspectives to assist in brainstorming. * **Personal Responsibility:** Since regulation struggles to keep pace with technological development, rules cannot fundamentally change student behavior. Therefore, responsibility rests with the students. They must take control of the situation, deciding whether to use AI to bypass the learning process or to leverage it to enhance personal outcomes and deepen their knowledge.