LearnAIng: Generative Artificial Intelligence to boost teaching and training in technical field

Generative Artificial Intelligence is revolutionizing the field of education, offering innovative tools to support students in the learning process and teachers in their activities. This paper presents an AI-powered software developed to support the teaching of systematic innovation courses, following TRIZ methodology, through the integration of Large Language Models (LLMs), Retrieval Augmented Generation (RAG) techniques and the access to patent source. The article describes the methodology implemented and the results of an experimental phase conducted on a large sample of students. The analysis shows how the use of AI in technical Problem-Solving enhances a more structured approach but at the same time is more effective in stimulating creativity and lateral thinking, reducing psychological inertia and boosting Technology Transfer. The findings highlight significant improvements over traditional didactics methods, both in learning effectiveness and instructional support.

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Publication Details

Journal
Procedia Computer Science
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procs.2025.09.145
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

LearnAIng: Generative Artificial Intelligence to boost teaching and training in technical field

Simone Avogadri, Davide Russo
Procedia Computer Science
Artificial Intelligence in Healthcare and Education
article

LearnAIng: Generative Artificial Intelligence to boost teaching and training in technical field

Simone Avogadri, Davide Russo
article en

Abstract

Generative Artificial Intelligence is revolutionizing the field of education, offering innovative tools to support students in the learning process and teachers in their activities. This paper presents an AI-powered software developed to support the teaching of systematic innovation courses, following TRIZ methodology, through the integration of Large Language Models (LLMs), Retrieval Augmented Generation (RAG) techniques and the access to patent source. The article describes the methodology implemented and the results of an experimental phase conducted on a large sample of students. The analysis shows how the use of AI in technical Problem-Solving enhances a more structured approach but at the same time is more effective in stimulating creativity and lateral thinking, reducing psychological inertia and boosting Technology Transfer. The findings highlight significant improvements over traditional didactics methods, both in learning effectiveness and instructional support.

Procedia Computer ScienceVol. 270
University of Bergamo (IT)
Openalex Percentile: Top 74%
Artificial Intelligence in Healthcare and Education
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