Wharton Students Take AI Training to a New Jersey High School Faculty

A research lab built around K, 12 teachers

WASRL is a six-month interdisciplinary research program run by the Wharton AI & Analytics Initiative (WAIAI). It brings together undergraduate and graduate students from across the University of Pennsylvania to develop and test AI resources for K, 12 educators.

The Lab's initial focus is professional development for high school teachers. Its projects include AI-supported learning modules, lesson-planning and classroom-design assistants, and other resources shaped by research into teachers' needs, routines and experiences with AI. The West Deptford session was the point where that work moved into a real classroom.

What happened at West Deptford

WASRL students introduced faculty to practical uses of generative AI, including Gemini, Gems and NotebookLM. The team began with an overview of AI fundamentals and governance, covering bias, hallucinations, verification and student privacy. Teachers then watched demonstrations and worked with AI using materials from their own classrooms.

Esha Desai, a Penn Carey Law and Wharton JD/MBA candidate who led the collaboration, said: "Teachers came in with different levels of AI experience, and by the end everyone had a working quiz they had built themselves and could actually use in class." She added that the shift from "AI is abstract" to "I built this in 20 minutes" was the point of the workshop.

Mathematics teacher Jaclyn Falcone said she had previously used Gemini and ChatGPT mainly to search for materials or gather information. One of her biggest takeaways was "Seeing it less as just a chat and more as tools." She saw how AI could help adapt existing materials into resources for her students, and said dedicated time to experiment helped her find uses she might not otherwise have considered.

Xuan "Leah" Liu, a WASRL research assistant who introduced teachers to NotebookLM, said she was unsure at first how receptive the group would be. "Once we started, though, I was surprised by how actively everyone explored the tool," she said.

Learning from teachers before designing for them

The team says it had to learn from teachers before building anything for them. Desai said the process demands a lot of teacher expertise: "We can't build good AI-supported lessons without deeply understanding what teachers already do: their workflows, their instincts about students, their concerns about integrity."

Graduate researcher Claire Liu said the team went in with hypotheses about what teachers would want, but a focus group revealed concerns and obstacles the researchers had not anticipated. In response, they redesigned a later session to give teachers more time to work with their own classroom materials. WASRL describes its approach as researching teacher needs, building prototypes, testing them with educators, and revising them based on what teachers say and do.

Where AI helps and where it should not

Both the student researchers and the West Deptford educators arrived at a consistent view: AI is most useful when it supports human judgment rather than replacing it. For teachers, that might mean less time spent adapting materials, drafting communications, creating rubrics or developing lesson resources.

For students, the picture is more complicated. Claire Liu said: "Learning often requires productive struggle. If AI removes the thinking an assignment was designed to build, making it 'easier' can undermine the entire point."

West Deptford Assistant Principal Kelly Saia saw value in tools that guide students through problems instead of simply giving answers, and described AI as another resource for differentiated instruction while teachers remain central to learning. Falcone stressed that teachers must be able to recognize inaccurate outputs and students need enough background knowledge to ask useful questions. "It's not really giving them everything they need; it's giving them guidance," she said.

What it means for schools

The West Deptford workshop was built on a simple goal: giving teachers the knowledge, tools and time to decide for themselves where AI can improve their work and their students' learning. For schools weighing AI adoption, the example points to training that starts from teachers' own materials and covers risks such as bias, hallucinations and student privacy alongside practical uses.