--- title: "Building a Peer Tutoring Matching Agent: Connecting Students for Study Groups" description: "Build an AI agent that matches students for peer tutoring based on skills, availability, and learning preferences, while collecting feedback and tracking tutoring quality." canonical: https://callsphere.ai/blog/building-peer-tutoring-matching-agent-connecting-students-study-groups category: "Learn Agentic AI" tags: ["AI Agents", "EdTech", "Peer Tutoring", "Python", "Student Matching"] author: "CallSphere Team" published: 2026-03-17T00:00:00.000Z updated: 2026-05-07T18:41:31.238Z --- # Building a Peer Tutoring Matching Agent: Connecting Students for Study Groups > Build an AI agent that matches students for peer tutoring based on skills, availability, and learning preferences, while collecting feedback and tracking tutoring quality. ## Why Peer Tutoring Works Research consistently shows that peer tutoring benefits both the tutor and the tutee. Tutors deepen their understanding by explaining concepts, while tutees receive relatable, accessible help. The challenge is logistics — matching students with complementary skills, coordinating schedules, and ensuring quality. An AI matching agent solves these coordination problems at scale. ## Peer Tutoring Data Model ```python from dataclasses import dataclass, field from enum import Enum from datetime import datetime, date, time from typing import Optional class SkillLevel(Enum): BEGINNER = 1 INTERMEDIATE = 2 ADVANCED = 3 EXPERT = 4 class SessionStatus(Enum): SCHEDULED = "scheduled" IN_PROGRESS = "in_progress" COMPLETED = "completed" CANCELLED = "cancelled" NO_SHOW = "no_show" class DayOfWeek(Enum): MON = "Monday" TUE = "Tuesday" WED = "Wednesday" THU = "Thursday" FRI = "Friday" SAT = "Saturday" SUN = "Sunday" @dataclass class TimeBlock: day: DayOfWeek start_time: time end_time: time @dataclass class SubjectSkill: subject: str course_code: str skill_level: SkillLevel can_tutor: bool # True if they can tutor this subject wants_help: bool # True if they need help @dataclass class StudentTutor: student_id: str name: str email: str major: str year: int skills: list[SubjectSkill] = field(default_factory=list) availability: list[TimeBlock] = field(default_factory=list) preferred_group_size: int = 2 preferred_mode: str = "in_person" # in_person, online, either rating_as_tutor: float = 0.0 total_sessions_tutored: int = 0 total_sessions_as_tutee: int = 0 @dataclass class TutoringSession: session_id: str tutor_id: str tutee_ids: list[str] subject: str course_code: str scheduled_time: datetime duration_minutes: int = 60 status: SessionStatus = SessionStatus.SCHEDULED location: str = "" feedback: list[dict] = field(default_factory=list) notes: str = "" ``` ## Matching Algorithm The matching algorithm considers subject expertise gaps, schedule overlap, and tutor quality ratings. ```mermaid flowchart LR CALLER(["Student or Parent"]) subgraph TEL["Telephony"] SIP["Twilio SIP and PSTN"] end subgraph BRAIN["Education AI Agent"] STT["Streaming STT Deepgram or Whisper"] NLU{"Intent and Entity Extraction"} TOOLS["Tool Calls"] TTS["Streaming TTS ElevenLabs or Rime"] end subgraph DATA["Live Data Plane"] CRM[("CRM and Notes")] CAL[("Calendar and Schedule")] KB[("Knowledge Base and Policies")] end subgraph OUT["Outcomes"] O1(["Enrollment captured"]) O2(["Tour scheduled"]) O3(["Counselor callback"]) end CALLER --> SIP --> STT --> NLU NLU -->|Lookup| TOOLS TOOLS CRM TOOLS CAL TOOLS KB NLU --> TTS --> SIP --> CALLER NLU -->|Resolved| O1 NLU -->|Schedule| O2 NLU -->|Escalate| O3 style CALLER fill:#f1f5f9,stroke:#64748b,color:#0f172a style NLU fill:#4f46e5,stroke:#4338ca,color:#fff style O1 fill:#059669,stroke:#047857,color:#fff style O2 fill:#0ea5e9,stroke:#0369a1,color:#fff style O3 fill:#f59e0b,stroke:#d97706,color:#1f2937 ``` ```python STUDENTS: dict[str, StudentTutor] = {} SESSIONS: list[TutoringSession] = [] def find_tutor_matches( student_id: str, subject: str, course_code: str ) -> list[dict]: student = STUDENTS.get(student_id) if not student: return [] # Verify student needs help in this subject needs_help = any( s.course_code == course_code and s.wants_help for s in student.skills ) if not needs_help: return [] student_availability = set( (tb.day, tb.start_time, tb.end_time) for tb in student.availability ) matches = [] for tutor_id, tutor in STUDENTS.items(): if tutor_id == student_id: continue # Check if tutor can teach this subject tutor_skill = None for skill in tutor.skills: if skill.course_code == course_code and skill.can_tutor: tutor_skill = skill break if not tutor_skill: continue # Check schedule overlap tutor_availability = set( (tb.day, tb.start_time, tb.end_time) for tb in tutor.availability ) common_times = student_availability & tutor_availability if not common_times: continue # Check mode compatibility if (student.preferred_mode != "either" and tutor.preferred_mode != "either" and student.preferred_mode != tutor.preferred_mode): continue # Calculate match score score = 0.0 score += tutor_skill.skill_level.value * 0.3 score += min(tutor.rating_as_tutor / 5.0, 1.0) * 0.3 score += min(len(common_times) / 5, 1.0) * 0.2 score += min(tutor.total_sessions_tutored / 20, 1.0) * 0.2 matches.append({ "tutor_id": tutor_id, "tutor_name": tutor.name, "skill_level": tutor_skill.skill_level.name, "rating": tutor.rating_as_tutor, "sessions_completed": tutor.total_sessions_tutored, "common_available_slots": len(common_times), "match_score": round(score, 2), "mode": tutor.preferred_mode, }) matches.sort(key=lambda m: m["match_score"], reverse=True) return matches[:5] def find_study_group( subject: str, course_code: str, max_size: int = 5 ) -> list[dict]: """Find students interested in forming a study group.""" interested = [] for student in STUDENTS.values(): for skill in student.skills: if skill.course_code == course_code and skill.wants_help: interested.append({ "student_id": student.student_id, "name": student.name, "skill_level": skill.skill_level.name, "availability_slots": len(student.availability), }) break interested.sort(key=lambda s: s["availability_slots"], reverse=True) return interested[:max_size] ``` ## Feedback and Quality Tracking ```python def submit_session_feedback( session_id: str, reviewer_id: str, rating: int, comment: str, was_helpful: bool, ) -> dict: session = None for s in SESSIONS: if s.session_id == session_id: session = s break if not session: return {"error": "Session not found"} feedback_entry = { "reviewer_id": reviewer_id, "rating": min(max(rating, 1), 5), "comment": comment, "was_helpful": was_helpful, "submitted_at": datetime.now().isoformat(), } session.feedback.append(feedback_entry) # Update tutor rating tutor = STUDENTS.get(session.tutor_id) if tutor: all_ratings = [ fb["rating"] for s in SESSIONS if s.tutor_id == session.tutor_id for fb in s.feedback ] if all_ratings: tutor.rating_as_tutor = round( sum(all_ratings) / len(all_ratings), 2 ) return {"status": "Feedback submitted", "tutor_new_rating": tutor.rating_as_tutor if tutor else None} ``` ## Agent Assembly ```python from agents import Agent, function_tool, Runner import json @function_tool def find_tutors( student_id: str, subject: str, course_code: str ) -> str: """Find matching tutors for a student in a specific subject.""" matches = find_tutor_matches(student_id, subject, course_code) return json.dumps(matches) if matches else "No tutors available." @function_tool def find_group(subject: str, course_code: str) -> str: """Find students interested in a study group for a course.""" group = find_study_group(subject, course_code) return json.dumps(group) if group else "No students available." @function_tool def submit_feedback( session_id: str, reviewer_id: str, rating: int, comment: str, was_helpful: bool, ) -> str: """Submit feedback after a tutoring session.""" result = submit_session_feedback( session_id, reviewer_id, rating, comment, was_helpful ) return json.dumps(result) tutoring_agent = Agent( name="Peer Tutoring Coordinator", instructions="""You are a peer tutoring matching agent. Help students find tutors, join study groups, and provide feedback on sessions. When matching, explain why each tutor is a good fit. Encourage students to try tutoring subjects they excel in. After sessions, always ask for feedback. If a student reports a poor experience, escalate to program staff.""", tools=[find_tutors, find_group, submit_feedback], ) ``` ## FAQ ### How does the agent prevent scheduling conflicts? Before confirming a match, the agent checks both the tutor and tutee existing session schedule to avoid double-booking. It presents only mutually available time slots. If a popular tutor is overbooked, the agent suggests alternative tutors or waitlist options. ### What happens when a tutor receives consistently low ratings? The quality tracking system flags tutors whose rolling average drops below 3.0 out of 5. The agent stops recommending them for new matches and notifies the tutoring program coordinator who can offer coaching or remove them from the tutor pool. The system distinguishes between subject-specific and general ratings. ### Can the agent handle group tutoring with multiple tutees? Yes. The `TutoringSession.tutee_ids` field supports multiple tutees. The matching algorithm can assemble groups where all members need help with the same topic and share overlapping availability. The agent caps group size at the tutor preferred limit and the student preferred group size. --- #AIAgents #EdTech #PeerTutoring #Python #StudentMatching #AgenticAI #LearnAI #AIEngineering --- Source: https://callsphere.ai/blog/building-peer-tutoring-matching-agent-connecting-students-study-groups