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AI Tutoring Best Short Paper Award

Analysis of a Generative AI-Based Graphical Learning Assistance Tool in IPR Courses

C.-C. Yen, P.-T. Hsieh, Y.-C. Chen, and C.-K. Chang

IEEE ICALT 2025 2025 DOI: 10.1109/ICALT64023.2025.00071

Abstract

Intellectual-property-rights (IPR) courses involve dense legal text that is challenging for non-law students to internalize. We pair a generative-AI tutor with auto-generated diagrams and flowcharts to visualize IPR concepts, evaluated in an undergraduate IPR course. Students reported improved understanding and positive reception of the graphical scaffolding.

Problem & Motivation

IPR courses are dense in legal text and case analysis, which makes abstract legal concepts hard to internalize for students without a law background. Pure text-based instruction tends to leave students unable to connect statutes with real-world scenarios.

Method

We developed a generative-AI tutoring tool that combines text responses with auto-generated diagrams and flowcharts, helping students visualize IPR concepts. The tool was deployed in an undergraduate IPR course and evaluated through surveys and learning-outcome analysis.

Findings

  • Graphical scaffolding measurably improved student understanding of IPR concepts.
  • Auto-generated visualizations helped students see connections between related legal concepts.
  • User experience ratings of the AI assistant were positive.

Implications

Generative AI is not limited to text response — visual representation can be a substantial channel for converting abstract knowledge into intuitive understanding. The approach generalizes naturally to other cross-disciplinary teaching settings (law, medicine) where domain-specific abstractions are a known stumbling block.

Citation

C.-C. Yen, P.-T. Hsieh, Y.-C. Chen, and C.-K. Chang, “Analysis of a Generative AI-Based Graphical Learning Assistance Tool in IPR Courses,” in IEEE ICALT 2025, 2025. doi: 10.1109/ICALT64023.2025.00071.

BibTeX

@inproceedings{yen2025ai_assistant_ipr,
  author    = {Chia-Chien Yen and Po-Tang Hsieh and Yi-Cheng Chen and Chia-Kai Chang},
  title     = {Analysis of a Generative {AI}-Based Graphical Learning Assistance Tool in {IPR} Courses},
  booktitle = {Proc. IEEE Int. Conf. on Advanced Learning Technologies (ICALT)},
  pages     = {226--228},
  year      = {2025},
  month     = jul,
  note      = {Best Short Paper Award},
  doi       = {10.1109/ICALT64023.2025.00071},
}