By Tasbiha Fatima Hasan

Writer/Reporter

For many college students, Handshake is more than just another career platform, it is one of the main places where students go to search for internships, entry-level jobs, campus opportunities, and early career connections. Some students find opportunities quickly. Others keep scrolling, filtering, applying, and waiting without hearing back.

Recently, Handshake entered the AI workspace through Handshake AI, a program that connects students, graduates, and professionals with flexible, project-based AI training and evaluation work. According to Handshake, the program allows participants to use human judgment and subject-matter expertise to help improve large language models, also known as LLMs. Handshake describes this work as flexible and project-based, with fellows contributing to tasks such as designing and evaluating AI prompts and responses in their academic fields.

At first glance, this sounds exciting. For students balancing tuition, assignments, job searching, and career uncertainty, a flexible remote AI opportunity can feel like a dream. It seems to offer what many students are looking for: income, experience, flexibility, and a way to participate in the growing AI economy.

But that excitement can also come with confusion.

A few weeks ago, while scrolling through Handshake in the middle of assignments and job searching, I came across an AI-related opportunity that immediately caught my attention. The posting appeared to connect with environmental or data-training work, which felt especially relevant to me as an Environmental Science student interested in data, sustainability, and applied technology. It seemed like a chance to connect my academic background with AI-related work.

However, after applying or creating the required profile, the specific opportunity I had originally seen did not appear to be available anymore. Instead, different projects showed up. This experience was confusing because the original posting created interest and hope, but the actual available work looked different once I moved further into the process.

While this does not automatically mean the opportunity was fake, it raises a larger concern in that students need clearer information about project availability, eligibility requirements, and whether a listed opportunity will still be accessible after they create a profile.

This question matters because students are often still learning how to evaluate professional opportunities. Many are searching for their first internship, trying to build experience, or looking for work that fits around classes. A job posting can look attractive, but students still need to understand whether they qualify, how they will be paid, what the work actually involves, and whether the opportunity is still active.

For students, the concern is not only whether a platform is legitimate, but also whether the process is transparent enough. When a posting shows a high hourly rate or a field-specific opportunity, students may naturally feel excited. But if the application steps, eligibility rules, project availability, or payment structure are unclear, that excitement can quickly turn into doubt.

AI work is becoming part of early career conversation. As AI-related gigs become more visible on career platforms, students need better guidance on how to judge them. Before applying for AI training or data-related opportunities, students should ask practical questions like is the posting from the official platform? Is the pay hourly or task-based? Is the specific project still available after profile creation? Does the role require a particular major, degree level, or assessment? What kind of work will the student actually be doing? These questions do not mean students should avoid AI opportunities. Instead, they show why students need to approach them carefully and thoughtfully.

The rise of AI work may create new opportunities for students, but it also creates a new kind of job-search anxiety. Students are already navigating uncertainty around internships, entry-level hiring, and career readiness. When high-paying or field-related AI opportunities appear suddenly, they can create hope, but also confusion.

That is why transparency matters. Students should not have to guess whether an opportunity is realistic, active, or appropriate for their situation. Career platforms, universities, and employers should provide clear information about eligibility, pay, work expectations, and project availability.

Handshake AI may represent a new direction in student employment and AI-related work. But for students, the bigger issue is not just technology. It is trust.

As AI changes the job market, students need more than exciting postings. They need clear guidance, honest expectations, and the ability to evaluate opportunities without being carried away by desperation or hope.

What do you think? Have you seen AI training, data annotation, environmental data, or remote job postings on Handshake or other platforms? What would you want a student newspaper article to explore next: pay transparency, project availability, scam prevention, or whether AI work actually builds career skills?

Leave a comment below.