Data from Students’ Use of Generative AI in Innovation Education Programs at CERN IdeaSquare

This article presents a dataset comprising Master-level students’ prompts and discussions with generative AI as part of a challenge-based innovation problem-solving process. Openly accessible through Zenodo (https://doi.org/10.5281/zenodo.17857679), the data consists of 466 pages of students’ conversations with generative AI tools and a 28-page extract comprising only the prompts. The data was collected through a survey for students participating in week-long intense innovation education programs at CERN IdeaSquare throughout 2024. The data can be used to advance practice and theory regarding the use of generative AI in problem solving and innovation education. Scholars are encouraged to use the data in various forms of analysis, including thematic, content, and narrative analysis. The data can also complement other similar datasets in larger studies examining the effects of different pedagogical approaches, student profiles, problem-solving situations, or other factors on the use of generative AI.

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Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-12-09
DOI
https://doi.org/10.23726/cij.2025.1799
Primary Topic
Biomedical and Engineering Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Data from Students’ Use of Generative AI in Innovation Education Programs at CERN IdeaSquare

Jimmy Poulaillon, Ole Werner, Giulia Gaddi, Ángel Costa-Perales et al.
SHILAP Revista de lepidopterología
Biomedical and Engineering Education
article

Data from Students’ Use of Generative AI in Innovation Education Programs at CERN IdeaSquare

Jimmy Poulaillon, Ole Werner, Giulia Gaddi, Ángel Costa-Perales, Lauri Valtonen, Jacopo Dardini, Mikko Sairanen, Matteo Balocco, Adrián Toledo Martínez, Eduard Puga Creus, Anastasiia Korovina, Diego Canova Tandaypan, Ngo Hung Dung, Javier Pérez Trujillo, Filomena Scirocco, Andrea Bruno, Fernando Palma Fanjul, Catarina Batista
article en

Abstract

This article presents a dataset comprising Master-level students’ prompts and discussions with generative AI as part of a challenge-based innovation problem-solving process. Openly accessible through Zenodo (https://doi.org/10.5281/zenodo.17857679), the data consists of 466 pages of students’ conversations with generative AI tools and a 28-page extract comprising only the prompts. The data was collected through a survey for students participating in week-long intense innovation education programs at CERN IdeaSquare throughout 2024. The data can be used to advance practice and theory regarding the use of generative AI in problem solving and innovation education. Scholars are encouraged to use the data in various forms of analysis, including thematic, content, and narrative analysis. The data can also complement other similar datasets in larger studies examining the effects of different pedagogical approaches, student profiles, problem-solving situations, or other factors on the use of generative AI.

SHILAP Revista de lepidopterología
Centre National de la Recherche Scientifique (FR), Université Paris Nanterre (FR), Universidad Andrés Bello (CL), Zambon (Italy) (IT), University of Bío-Bío (CL), Universitat Ramon Llull (ES), Universitat Politècnica de València (ES), European Organization for Nuclear Research (CH), University of Bologna (IT), Universitat Politècnica de Catalunya (ES), Aalto University (FI)
Industry, innovation and infrastructure
Openalex Percentile: Top 41%
Biomedical and Engineering Education
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