Exploring the Impact of Generative AI for StandUp Report Recommendations in Software Capstone Project Development

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dc.catalogadorjlo
dc.contributor.authorNeyem, Hugo Andrés
dc.contributor.authorSandoval Alcocer, Juan Pablo
dc.contributor.authorMendoza Rocha, Marcelo Gabriel
dc.contributor.authorCentellas-Claro, Leonardo
dc.contributor.authorGonzález, Luis A.
dc.contributor.authorParedes Robles, Carlos Daniel
dc.date.accessioned2024-05-31T13:16:00Z
dc.date.available2024-05-31T13:16:00Z
dc.date.issued2024
dc.description.abstractStandUp Reports play an important role in capstone software engineering courses, facilitating progress tracking, obstacle identification, and team collaboration. However, despite their significance, students often grapple with the challenge of creating StandUp Reports that are clear, concise, and actionable. This paper investigates the impact of the use of generative AI in producing StandUp report recommendations, aiming to assist students in enhancing the quality and effectiveness of their reports. In a semester-long capstone course, 179 students participated in 16 real-world software development projects. They submitted weekly StandUp Reports with the assistance of an AI-powered Slack, which analyzed their initial reports and provided suggestions for enhancing them using both GPT-3.5 and the early access GPT-4 API. After each submitted report, students voluntarily answered a survey about usability and suggestion preference. Furthermore, we conducted a linguistic analysis of the recommendations made by the algorithms to gauge reading ease and comprehension complexity. Our findings indicate that the AI-based recommendation system helped students improve the overall quality of their StandUp Reports throughout the semester. Students expressed a high level of satisfaction with the tool and exhibited a strong willingness to continue using it in the future. The survey reveals that students perceived a slight improvement when using GPT-4 compared to GPT-3.5. Finally, a computational linguistic analysis performed on the recommendations demonstrates that both algorithms significantly improve the alignment between the generated texts and the students' educational level, thereby improving the quality of the original texts.
dc.fechaingreso.objetodigital2024-09-05
dc.fuente.origenORCID
dc.identifier.doi10.1145/3626252.3630854
dc.identifier.urihttp://doi.org/10.1145/3626252.3630854
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86121
dc.information.autorucEscuela de Ingeniería; Neyem, Hugo Andrés; 0000-0002-5734-722X; 1007638
dc.information.autorucEscuela de Ingeniería; Sandoval Alcocer, Juan Pablo; S/I; 1210748
dc.information.autorucEscuela de Ingeniería; Mendoza Rocha, Marcelo Gabriel; S/I; 1237020
dc.information.autorucEscuela de Ingeniería; Paredes Robles, Carlos Daniel; S/I; 1064912
dc.language.isoen
dc.nota.accesocontenido parcial
dc.relation.ispartof55th ACM Technical Symposium on Computer Science Education (SIGCSE)
dc.rightsacceso restringido
dc.subjectGenerative AI
dc.subjectLarge Language Models
dc.subjectChatGPT,
dc.subject.ddc000
dc.subject.deweyCiencias de la computaciónes_ES
dc.subject.ods04 Quality education
dc.subject.odspa04 Educación de calidad
dc.titleExploring the Impact of Generative AI for StandUp Report Recommendations in Software Capstone Project Development
dc.typecomunicación de congreso
sipa.codpersvinculados1007638
sipa.codpersvinculados1210748
sipa.codpersvinculados1237020
sipa.codpersvinculados1064912
sipa.trazabilidadORCID;2024-05-27
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