While completing her undergraduate degree in biosciences at Tec de Monterrey, Alejandra Valdivia Padilla approached lengthy exams with a unique perspective. Unlike her classmates, who often complained about the seemingly endless hours spent answering questions, Valdivia maintained a positive outlook.
“I felt those exams worked for me. I know I wasn’t in the majority,” she explains. In surveys taken after the exam about their experience, other students shared frustrating experiences, underscoring how subjective assessments can feel depending on a student’s emotional state.
To address the lack of tools providing real-time, objective feedback on learning, researchers at Tec de Monterrey are exploring how monitoring brain activity could revolutionize classroom teaching.
Valdivia, now pursuing a PhD in physiology, biophysics, and systems biology at Weill Cornell Medicine, and Milton Candela Leal, a biomedical engineering student, presented three studies on learning tools in Amsterdam.
Developed at Tec’s BRAIN Center, these projects harness biometric signals to optimize educational environments. The first, the Advanced Learner Assistance System, detects mental fatigue in real time. The second assesses interest in STEM topics among students aged 6 to 15. The third identifies students’ emotions while learning to create personalized educational settings.
“All three projects share the same foundation: how we can use biometrics and real-time technologies to support education,” explains Candela, a researcher on the project.
Adapting Educational Environments with Light and Sound
The team has created the Neurohumanities Lab, an immersive space where the environment adapts to students’ brain signals. “The goal is to identify strategies teachers can implement to improve performance,” says Mauricio Ramírez Moreno, project coordinator and mechatronics professor. “We want to make classes more dynamic and engaging for students.”
Based on brain signals, the system can adjust classroom elements like lighting and sound. Researchers have found that gamma waves are particularly effective in detecting emotional states such as admiration, joy, and sadness. Using electroencephalography (EEG) through specialized headphones, they’ve achieved 94% accuracy in identifying mental states.
For Valdivia, the implications extend beyond education. “This is a critical issue in health sciences, especially given today’s challenges with stress, mental health, and the pressure students face,” she notes.
In the classrooms of the future, each student could wear a discreet device—such as headphones, glasses, or a headband with electrodes—that monitors different brain regions. “To make this as natural as possible, we’re developing less intrusive devices that resemble everyday objects,” Ramírez explains.
Detecting Mental Fatigue, Stress, and Anxiety
The system will require a computing hub to gather and process data from these devices. “Biometrics provide real-time feedback to teachers,” says Candela. “In future classrooms, while conducting an activity, a teacher could monitor a control panel that shows how students’ emotions evolve—whether they’re bored, fatigued, or less engaged.”
This technology offers significant advances in education: continuous monitoring to detect mental fatigue during assessments, early detection of stress and anxiety, and real-time adaptation of teaching methods based on student responses. It also provides objective data to evaluate the effectiveness of different educational strategies.
“These are unbiased, objective metrics that are more reliable over multiple sessions,” Candela emphasizes.
The BRAIN Center team is refining the system in collaboration with the Living Lab. Although widespread implementation of these future classrooms remains uncertain, the potential impact of their discoveries is promising.
“This will benefit the entire education system,” Valdivia concludes, “from students and teachers to administrators and investors looking to optimize resource allocation.”
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