Beyond Traditional Learning: The LLM Revolution in BPM Education at University
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F24%3A43930385" target="_blank" >RIV/60461373:22340/24:43930385 - isvavai.cz</a>
Výsledek na webu
<a href="https://link.springer.com/book/10.1007/978-3-031-70445-1" target="_blank" >https://link.springer.com/book/10.1007/978-3-031-70445-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-70445-1_29" target="_blank" >10.1007/978-3-031-70445-1_29</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Beyond Traditional Learning: The LLM Revolution in BPM Education at University
Popis výsledku v původním jazyce
Large Language Models (LLMs) significantly impact higher education, requiring changes in educational processes, especially in Business Process Management (BPM) practical exercises. The research aims to evaluate the effectiveness of LLMs in BPM education to determine if LLMs can supplement educators. The study involved 33 master's degree students. Students' works were manually evaluated and compared to LLM-generated responses. Results highlight the effectiveness and limitations of LLMs in supporting BPM education, revealing discrepancies between human and AI assessments. Our findings indicate that LLMs like ChatGPT-3.5 and ChatGPT-4o can aid BPM education, but their performance and reliability differ from traditional human grading. The findings underscore LLMs' potential to offer additional perspectives and reduce educators' workload. However, LLMs should be supplementary tools, not replacements for traditional methods. This exploration contributes to understanding the transformative role of LLMs in reshaping educational methodologies. Future research should consider larger samples and a broader range of tasks to validate and extend these findings.
Název v anglickém jazyce
Beyond Traditional Learning: The LLM Revolution in BPM Education at University
Popis výsledku anglicky
Large Language Models (LLMs) significantly impact higher education, requiring changes in educational processes, especially in Business Process Management (BPM) practical exercises. The research aims to evaluate the effectiveness of LLMs in BPM education to determine if LLMs can supplement educators. The study involved 33 master's degree students. Students' works were manually evaluated and compared to LLM-generated responses. Results highlight the effectiveness and limitations of LLMs in supporting BPM education, revealing discrepancies between human and AI assessments. Our findings indicate that LLMs like ChatGPT-3.5 and ChatGPT-4o can aid BPM education, but their performance and reliability differ from traditional human grading. The findings underscore LLMs' potential to offer additional perspectives and reduce educators' workload. However, LLMs should be supplementary tools, not replacements for traditional methods. This exploration contributes to understanding the transformative role of LLMs in reshaping educational methodologies. Future research should consider larger samples and a broader range of tasks to validate and extend these findings.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
50204 - Business and management
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
BUSINESS PROCESS MANAGEMENT: BLOCKCHAIN, ROBOTIC PROCESS AUTOMATION, CENTRAL AND EASTERN EUROPEAN, EDUCATORS AND INDUSTRY FORUM: BPM 2024 BLOCKCHAIN, RPA, CEE, EDUCATORS AND INDUSTRY FORUM
ISBN
978-3-031-70444-4
ISSN
1865-1348
e-ISSN
1865-1356
Počet stran výsledku
10
Strana od-do
406-415
Název nakladatele
Springer Cham
Místo vydání
Cham
Místo konání akce
AGH Univ Krakow
Datum konání akce
1. 9. 2024
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
001338400200029