Inspiration:
Sense-Ed was inspired by the need for personalized education that adapts to individual learning styles and keeps students focused.
What it does:
It uses AI to analyze facial expressions, detect procrastination and distraction, and adapt lessons in real-time to improve comprehension and maintain focus.
How we built it:
We combined facial expression recognition, machine learning, and distraction detection algorithms to create a system that understands both the student’s needs and their level of engagement.
Challenges we ran into:
Detecting procrastination and distractions accurately in a dynamic environment and ensuring the system responds quickly enough to maintain engagement were some of the challenges.
Accomplishments that we're proud of:
We successfully integrated real-time feedback on both learning and focus, making Sense-Ed a more interactive and effective tool for students.
What we learned:
We learned how important it is to balance emotional intelligence with focus detection, and how both can be combined to improve the overall learning experience.
What's next for Sense-Ed:
We plan to refine the procrastination detection algorithms, add more personalized features, and integrate the platform with more educational tools and apps.
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