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From Code to Physical AI: Near-Peer Mentors Bring Robotics to Life at ACCESS Academy Artificial Intelligence Summer Camp
August 14, 2026
By Christy Hudson
For high school students at ACCESS Academy Artificial Intelligence (AI) Summer Camp, learning alongside George Mason University undergraduate mentors provides a near-peer perspective on robotics, artificial intelligence, and pathways to higher education.
George Mason undergraduate students from the School of Engineering and Computing are leading the camp’s advanced sessions, serving as near-peer mentors as students develop skills in robotics, Python programming, artificial intelligence, and problem-based learning. Through hands-on activities and real-world challenges, the Mason students help campers move from learning technical concepts to applying them.
"Near-peer mentoring is much more than a staffing model, it's a deliberate learning strategy," said Ingrid Guerra-López, Dean of George Mason University's College of Education and Human Development. "Students often learn differently when they're working alongside someone who has recently overcome the same challenges they are facing. Near-peer mentors create an environment where students feel more comfortable asking questions, taking risks, and learning through trial and error because they see someone who was recently in their position. Those mentors make the pathway ahead feel tangible and achievable while reinforcing and extending their own learning through teaching, coaching, and leadership. We intentionally designed ACCESS Academy to create those reciprocal learning opportunities."
Now in its second year, the camp brings approximately 150 rising ninth through twelfth graders to Fuse at Mason Square across three weeks of hands-on learning. Students participate in either a beginner or advanced session, with the advanced group including current ACCESS Academy students and returning campers from last year’s inaugural camp.
The undergraduate mentors play a role that extends beyond classroom support. They help develop the curriculum, monitor student progress, and adjust activities based on campers’ needs and feedback. Through this work, they provide an authentic near-peer learning experience while developing their own leadership and project management skills.
“Our guiding principle was that the camp had to be fun with lots of hands-on activities,” said Toan Do, a rising junior studying electrical engineering at Mason who helped lead the undergraduate mentor team. “Whatever the students learned during the day had to be applied later that same day. No ‘Remember what you learned on Day 1 because you’re going to need it on Day 4.’”
That approach is central to ACCESS Academy’s problem-based learning model, which asks students to apply new concepts to authentic challenges.
The advanced group includes current ACCESS Academy students as well as students returning for a second summer after attending last year’s inaugural ACCESS Academy AI Summer Camp. Working in small teams, campers assemble and program SparkFun XRP robots, learn Python programming and sensor technologies, and apply what they learn as they work toward a final challenge.
Campers also fly drones, work in the Fuse Robotics Lab, participate in simulation exercises, and engage in conversations about artificial intelligence in school and the workplace, including current AI regulations. Through hands-on projects tailored to their skill levels, students are introduced to the basics of machine learning and encouraged to prototype and present original ideas and solutions.
“We hope that the participants of our camp see the connection between the concepts of coding and the practical implementation of physical AI in real-time applications,” said Roberto Pamas, director of ACCESS Academy.
The connection between coding and physical AI becomes especially clear as students work to program their robots to navigate a maze without human control. Using Python, campers work with sensors that detect obstacles and pathways, adjust their code, test their solutions, and troubleshoot when their robots do not behave as expected.
For Ishaan Sharma, a rising sophomore at Potomac Falls High School attending the camp for the first time, the maze presented a significant challenge.
“The maze is really hard,” Sharma said. “The sensors on the robots are challenging. Our robot keeps veering to the right. The mentors are helping us a lot. We’re asking a lot of questions, and they’re being very helpful. The last advice we got was to change the values in our code. We have to adjust and see if a smaller or larger value will help our robot make the turns.”
Rather than simply providing the solution, the undergraduate mentors encourage campers to work through problems themselves. They ask questions, offer suggestions, and help students identify possible approaches.
The mentors also designed the camp so students could move between activities when they encountered a particularly difficult problem. When campers became frustrated with their robots, for example, they could take a break and practice flying drones before returning to the challenge.
Do said that approach reflects his own experience with problem-solving.
“I’ve found, anecdotally, that taking a break from a problem allows my brain to process it in the background,” he said. “When I return, I’m often able to see the obvious solution to the problem I was struggling with.”
The undergraduate mentors continually assess whether the material is appropriately challenging. Do and his colleagues use their observations as well as daily student feedback forms, which ask campers to reflect on their experience, the level of challenge, and the pacing of the class.
“I don’t want a challenge to be too easy to the point where it isn’t engaging, but I also don’t want it to be so hard that it becomes frustrating,” Do said.
That balance informed the final robotics challenge. Although the mentors considered increasing the difficulty of the maze, they ultimately decided to maintain the existing challenge after observing that many students were still working to get their robots to navigate it successfully.
The students’ feedback offered another measure of the experience. Do said one response appeared repeatedly when campers were asked about the best part of their day: “When my code finally worked.”
For Do, those moments captured one of the camp’s most important objectives: helping students develop an engineering mindset.
“I hope the students were able to learn and apply an engineering mindset: breaking down complex problems into small, manageable tasks,” he said.
That mindset extends beyond robotics. Throughout the camp, students are encouraged to prototype and present original ideas and solutions as they work through a final challenge designed to foster creativity. They are also introduced to the fundamentals of machine learning through hands-on projects tailored to their skill level.
For the undergraduate mentors, the experience is also a learning opportunity.
Do joined the camp in part to gain experience in leadership and project management. But mentoring high school students also required him to examine his own understanding of engineering concepts.
“Teaching is hard,” he said.
When a student asks him to explain how a motor encoder or inertial measurement unit works, or why a piece of code isn’t working, Do sometimes realizes that he doesn’t understand the concept as well as he thought.
“Whenever I explain a topic to a student, such as how a motor encoder works, how an IMU works, how to get a robot to drive straight, or answering ‘why does my code not work?’ and find myself stuttering, that’s when I realize I don’t truly understand the topic or problem well enough.”
At 19, Do is also only a few years removed from the experience of being a high school student himself. He graduated from high school in June 2025 and remembers the frustration of learning concepts without having an immediate opportunity to apply them.
“I understand the frustration of listening to a lecture without getting to apply the concepts immediately,” he said.
That shared experience is part of what makes near-peer mentoring valuable. Campers are working with undergraduate students who have recently navigated many of the same academic decisions and are now applying those skills in college classrooms, research labs, and emerging technology fields.
For ACCESS Academy, the model is also part of a larger goal: exposing students to the technologies shaping the future while helping them develop the creativity, critical thinking, collaboration, and problem-solving skills needed to engage with those technologies responsibly.
Through hands-on experiences in robotics and AI, campers are not only learning how technology works. They are beginning to see how concepts like coding and machine learning can translate into physical systems and real-world applications.
At the same time, the near-peer mentoring model gives students a firsthand connection to the college pathways and careers they are beginning to explore. The Mason undergraduates are close enough to remember what it was like to be in their campers’ shoes, yet far enough along to show what those pathways can look like in practice.
In that exchange, both groups are learning. Campers gain technical skills, confidence, and a clearer vision of what they might pursue, while their mentors strengthen the leadership, communication, and problem-solving skills they will carry into their own careers. The result is more than a week of learning about robotics and AI. It is an opportunity for students to see themselves as part of the technology pathways they are beginning to explore.