Introduction

In the Fall 2024 semester, I had the opportunity to serve as a Teaching Assistant (TA) for two advanced courses: “AI Applications in Information Security” and “Machine Learning” at Carnegie Mellon University. This experience not only enriched my understanding of these fields but also offered invaluable lessons in pedagogy, mentorship, and academic integrity. I’d like to share insights from guiding three student project groups and the unique challenges and solutions I encountered along the way.

My Responsibilities as a TA

As a TA, I was responsible for assisting professors in course-related tasks, guiding students through complex projects, and conducting weekly office hours, one in person and one online. My primary objective was to help students without directly giving answers, encouraging them to develop independent problem-solving skills. This approach demanded a deep understanding of course materials and the ability to communicate complex concepts in a clear and accessible manner.

Weekly Office Hours

Weekly office hours were a cornerstone of my TA responsibilities. During assignment deadlines, these sessions were particularly packed, with students bringing a diverse range of issues, often involving intricate bugs. For instance, I helped students understand the relationship between convolutional and fully connected layers in neural networks, drawing on my robotics lab experience in image recognition. Through real-life examples, I demonstrated how convolutional layers extract specific features from images, while fully connected layers leverage these features for classification. Though deep learning algorithms can be opaque, this step-by-step guidance helped students grasp the core functionality of neural networks.

Addressing Academic Integrity in the Age of AI

A significant challenge in my TA role was overseeing potential Academic Integrity Violations (AIV), especially concerning the use of large language models (LLMs) in coursework. While these models provide immense support, their misuse could undermine learning outcomes. Instead of a blanket ban on LLMs, we encouraged students to use them as supplemental tools, requiring references for any LLM-assisted work to maintain the educational value of assignments.

Long-term AI Project Management

In “AI Applications in Information Security,” students embarked on a semester-long project that required choosing a topic, reviewing recent research, and developing a proposal. This self-directed project posed unique challenges, and as a TA, I regularly met with three student groups to monitor progress, offer technical support, and facilitate collaboration. Each group focused on a distinct AI-related topic, which required me to stay updated on each project’s specifics to provide relevant guidance.

Group Projects Highlights

Here are brief overviews of the three student projects I supervised:

  1. Fake Review Detection System: This team addressed the issue of fraudulent online reviews, which can mislead consumers and impact seller reputations. They developed a system using deep learning models, including Text-CNN, LSTM, and transformer-based models, to detect and filter out fake reviews on e-commerce platforms. By analyzing linguistic patterns and applying ensemble learning, the team improved detection accuracy for fake reviews​:contentReference[oaicite:0]{index=0}​:contentReference[oaicite:1]{index=1}.

  2. Protecting reCAPTCHA Systems Against AI-Based Attacks: In this project, the group focused on enhancing the robustness of reCAPTCHA by incorporating adversarial techniques to prevent AI from bypassing CAPTCHA security. They proposed methods like adding perturbations and utilizing GANs to create adversarially robust CAPTCHA images, improving resilience against sophisticated AI-driven attacks​:contentReference[oaicite:2]{index=2}.

  3. Enhanced Fake Review Detection Using Attention Mechanisms: The final group extended the capabilities of fake review detection by incorporating attention-based Bidirectional LSTM models. This approach captured nuanced contextual information in reviews to more accurately classify deceptive content, aiming to strengthen consumer trust in online platforms​:contentReference[oaicite:3]{index=3}.

Reflecting on Teaching and Learning

Being a TA taught me that effective teaching is not only about knowledge delivery but also about fostering critical thinking and adaptability. These skills became crucial as I worked with students from diverse backgrounds, each bringing unique perspectives and challenges.