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This study presents a comparative experimental analysis of three inventive problem-solving approaches: (a) traditional TRIZ methodology applied without AI assistance, (b) a hybrid approach integrating TRIZ with generative AI, and (c) an autonomous AI-powered process without direct human intervention. A real-world engineering challenge for the experiment, an inline coating measurement for lithium-ion-cell-production, was selected by an expert and structured using detailed problem description. During a maximum seven-hour problem-solving phase, three teams applied the mentioned approaches for ideation, and selected the best 10 solution ideas. The ideation outcomes were evaluated based on key performance metrics: the total number of unique ideas, team and individual ideation productivity, as well as the ideas’ usefulness, feasibility, and novelty. The results offer insights into the performance of AI-augmented problem solving, emphasizing differences in ideation productivity, solution diversity, and the impact of human intervention.

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Comparative Study on AI-Augmented Inventive Problem Solving with TRIZ, TRIZ-AI Hybrid, and Autonomous AI: An Inline Coating Measurement Case in Lithium-Ion Cell Production

  • 会议论文

  • 发表日期：

    2026年

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  • Pavel Livotov,
  • Norbert Huber,
  • Claudia Hentschel,
  • Oliver Mayer,
  • Jens Träger,
  • Gunther Bohn,
  • Leonard Höcht,
  • Mara Kläb,
  • Laura Kuhlmann,
  • Mas’udah,
  • Bruno Scherb,
  • Frank Clemens Schnittker,
  • Jochen Wessner,
  • Wolfgang Weydanz

摘要

This study presents a comparative experimental analysis of three inventive problem-solving approaches: (a) traditional TRIZ methodology applied without AI assistance, (b) a hybrid approach integrating TRIZ with generative AI, and (c) an autonomous AI-powered process without direct human intervention. A real-world engineering challenge for the experiment, an inline coating measurement for lithium-ion-cell-production, was selected by an expert and structured using detailed problem description. During a maximum seven-hour problem-solving phase, three teams applied the mentioned approaches for ideation, and selected the best 10 solution ideas. The ideation outcomes were evaluated based on key performance metrics: the total number of unique ideas, team and individual ideation productivity, as well as the ideas’ usefulness, feasibility, and novelty. The results offer insights into the performance of AI-augmented problem solving, emphasizing differences in ideation productivity, solution diversity, and the impact of human intervention.