title: AITEE -- Agentic Tutor for Electrical Engineering
summary: Intelligent tutoring systems combined with large language models offer a
  promising approach to address students' diverse needs and promote
  self-efficacious learning. While large language models possess good
  foundational knowledge of electrical engineering basics, they remain
  insufficiently capable of addressing specific questions about electrical
  circuits. In this paper, we present AITEE, an agent-based tutoring system for
  electrical engineering designed to accompany students throughout their learning
  process, offer individualized support, and promote self-directed learning.
  AITEE supports both hand-drawn and digital circuits through an adapted circuit
  reconstruction process, enabling natural interaction with students. Our novel
  graph-based similarity measure identifies relevant context from lecture
  materials through a retrieval augmented generation approach, while parallel
  Spice simulation further enhances accuracy in applying solution methodologies.
  The system implements a Socratic dialogue to foster learner autonomy through
  guided questioning. Experimental evaluations demonstrate that AITEE
  significantly outperforms baseline approaches in domain-specific knowledge
  application, with even medium-sized LLM models showing acceptable performance.
  Our results highlight the potential of agentic tutors to deliver scalable,
  personalized, and effective learning environments for electrical engineering
  education.
paper_authors._id: 68380c860fb1ddbe91ba0bf9
paper_authors.name: Christopher Knievel
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:41:12.986Z
paper_authors._id: 631f5035c6b20f03c823c4ba
paper_authors.avatarUrl: /avatars/5557b07bc7bd76f5752b5cf3d8e8f22f.svg
paper_authors.fullname: Christopher Knievel
paper_authors.type: user
paper_authors.user: CKnievel
paper_authors._id: 68380c860fb1ddbe91ba0bfa
paper_authors.name: Alexander Bernhardt
paper_authors._id: 68380c860fb1ddbe91ba0bfb
paper_authors.name: Christian Bernhardt
paper_title: AITEE -- Agentic Tutor for Electrical Engineering
paper_summary: Intelligent tutoring systems combined with large language models offer a
  promising approach to address students' diverse needs and promote
  self-efficacious learning. While large language models possess good
  foundational knowledge of electrical engineering basics, they remain
  insufficiently capable of addressing specific questions about electrical
  circuits. In this paper, we present AITEE, an agent-based tutoring system for
  electrical engineering designed to accompany students throughout their learning
  process, offer individualized support, and promote self-directed learning.
  AITEE supports both hand-drawn and digital circuits through an adapted circuit
  reconstruction process, enabling natural interaction with students. Our novel
  graph-based similarity measure identifies relevant context from lecture
  materials through a retrieval augmented generation approach, while parallel
  Spice simulation further enhances accuracy in applying solution methodologies.
  The system implements a Socratic dialogue to foster learner autonomy through
  guided questioning. Experimental evaluations demonstrate that AITEE
  significantly outperforms baseline approaches in domain-specific knowledge
  application, with even medium-sized LLM models showing acceptable performance.
  Our results highlight the potential of agentic tutors to deliver scalable,
  personalized, and effective learning environments for electrical engineering
  education.
paper_ai_summary: An agent-based tutoring system for electrical engineering enhances learning through natural circuit interaction, context-retrieving generation, and guided questioning, demonstrating superior performance compared to baseline methods.
paper_ai_keywords: agent-based tutoring system large language models electrical circuits adapted circuit reconstruction graph-based similarity measure retrieval augmented generation parallel Spice simulation Socratic dialogue