AI tutors that remember what each student knows, where they struggle, and how they learn best.
AI tutors don't track what the student knows vs. doesn't know. They can't adapt difficulty or skip mastered topics.
Students get the same explanation style even when it didn't work before. No adaptation to individual learning patterns.
Each tutoring session starts fresh. Past mistakes, breakthroughs, and learning trajectory are forgotten.
Without memory, every student gets the same experience regardless of their level, goals, or learning speed.
Semantic memory stores what each student knows, their knowledge gaps, and mastery levels per topic.
Episodic memory records tutoring sessions — which explanations worked, what confused the student, key breakthroughs.
Procedural memory captures effective tutoring approaches per student that improve over time.
Cognitive Profile generates a tutor system prompt with the student's full context — level, preferences, and history.
from mengram import Mengram
m = Mengram(api_key="mg-...")
def tutor_session(student_id: str, topic: str):
# Get student's full learning profile
profile = m.profile(user_id=student_id)
# "Student is a 10th grader studying calculus. Strong in algebra,
# struggles with limits. Learns best with visual examples.
# Last session: practiced chain rule, got 7/10 correct."
past = m.search(topic, user_id=student_id)
# Returns past interactions with this topic
# After the session, store progress
m.add(f"Tutored {topic}. Student understood the concept after "
f"visual explanation with graphs. Scored 8/10 on practice.",
user_id=student_id)Learning speed
Retention rate
Adaptation
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