Exploring Data Science Students' Engagement, Usage Patterns, and Perceptions of Large Language Models in Programming...35th Medical Informatics Europe Conference (MIE 2025), May 19-21, 2025, Glasgow, Scotland
Large Language Models (LLMs) are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, paving the way for new applications previously unhandled. The use of LLMs is increasing, especially among students. This study aimed to understand how students perce...
| Publicado en: | Studies in Health Technology & Informatics Vol. 327; pp. 1069 - 1074 |
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| Autores principales: | , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185459382&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185459382 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 327 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 185459382 185459382 185459382 10.3233/SHTI250547 185459382 ppf: 1069 ppct: 5 formats: tig: atl: Exploring Data Science Students' Engagement, Usage Patterns, and Perceptions of Large Language Models in Programming...35th Medical Informatics Europe Conference (MIE 2025), May 19-21, 2025, Glasgow, Scotland aug: au: KHENDEK, Lilia TESTON, Antoine SAINT-DIZIER, Chloé LAMER, Antoine affil: Fédération Régionale de Recherche en Psychiatrie Et Santé Mentale - F2RSM Psy, Hauts-de-France, Saint-André-Lez-Lille, France. sug: subj: Natural Language Processing Methods Programming Languages Education Data Science Education Students Psychosocial Factors Social Participation Student Attitudes Human Congresses and Conferences Scotland Scotland Artificial Intelligence, Generative Methods Questionnaires Language Thinking Habits Education, Masters Descriptive Statistics Data Analysis Software Reliability Comparative Studies ab: Large Language Models (LLMs) are a type of artificial intelligence (AI) that have emerged as powerful tools for a wide range of tasks, paving the way for new applications previously unhandled. The use of LLMs is increasing, especially among students. This study aimed to understand how students perceive and use these technologies for programming tasks. We surveyed students and recent graduates of a Master's degree in Data Science for Health regarding their programming assignments. Among respondents (n=77), 84.4% (n=65) reported using LLMs for tasks such as debugging, generating code, understanding error messages, optimizing code and providing detailed explanations. Of these users, 55.4% (n=36) reported that LLM usage has become a daily habit, while 46.1% (n=30) noted a growing trend in their usage of LLMs. Furthermore, 87.7% (n=57) engaged in monitoring to keep up to date with the latest developments. LLMs are considered reliable by the majority of participants, however most of them still carried out verifications on their answers. Although 81.5% (n=53) of LLM users were satisfied with the tools, citing their speed, ease of use, and debugging potential, concerns about tool dependency, data confidentiality, and the precision of references were also raised. The results highlighted the uptake of these technologies by students, indicating that the integration of LLMs into educational settings is essential to promote best practices while maintaining a focus on the importance of fundamental skills such as problem-solving and critical thinking, which are indispensable in professional life. Therefore, educators are encouraged to adapt their teaching methods and assessment strategies accordingly. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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