TY - GEN
T1 - Research on generative AI within university teaching contexts a methodological analysis
AU - Coronado-Otavalo, Ximena
AU - Ligna, Viviana Galarza
AU - Romero-Gutiérrez, José Marcelino
AU - Romero, Abel
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This article presents the results of a systematic literature review aimed at identifying methodological trends in empirical and review studies concerning generative artificial intelligence (GenAI) within higher education institutions. The review was conducted in accordance with PRISMA guidelines, by using a corpus of peer-reviewed articles indexed in Scopus and Web of Science, published between 2021 and early 2024. Most studies are conducted within undergraduate and teacher education settings, with a primary focus on assessment processes and academic performance. A qualitative matrix was applied to 30 selected studies, considering variables such as type of research, methodological approach, educational level, data collection techniques, and ethical-pedagogical approach. The findings show the predominance of quantitative, cross-sectional designs based on surveys with limited validation procedures. The study concludes with an insight: the need to enhance methodological rigor and ethical alignment in future research on generative artificial intelligence within the university context.
AB - This article presents the results of a systematic literature review aimed at identifying methodological trends in empirical and review studies concerning generative artificial intelligence (GenAI) within higher education institutions. The review was conducted in accordance with PRISMA guidelines, by using a corpus of peer-reviewed articles indexed in Scopus and Web of Science, published between 2021 and early 2024. Most studies are conducted within undergraduate and teacher education settings, with a primary focus on assessment processes and academic performance. A qualitative matrix was applied to 30 selected studies, considering variables such as type of research, methodological approach, educational level, data collection techniques, and ethical-pedagogical approach. The findings show the predominance of quantitative, cross-sectional designs based on surveys with limited validation procedures. The study concludes with an insight: the need to enhance methodological rigor and ethical alignment in future research on generative artificial intelligence within the university context.
KW - generative artificial intelligence
KW - higher education
KW - methodology
KW - systematic literature review
UR - https://www.scopus.com/pages/publications/105032531349
U2 - 10.1109/ETCM67548.2025.11304469
DO - 10.1109/ETCM67548.2025.11304469
M3 - Contribución a la conferencia
AN - SCOPUS:105032531349
T3 - ETCM 2025 - 9th Ecuador Technical Chapters Meeting
BT - ETCM 2025 - 9th Ecuador Technical Chapters Meeting
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th Ecuador Technical Chapters Meeting, ETCM 2025
Y2 - 21 October 2025 through 24 October 2025
ER -