Banca de QUALIFICAÇÃO: LUZIANE DA COSTA CARVALHO

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : LUZIANE DA COSTA CARVALHO
DATE: 01/10/2025
TIME: 14:00
LOCAL: https://meet.google.com/ixx-zyax-hag
TITLE:

Generative Artificial Intelligence in Programming Education: An Analysis of the Relationship Between Metacognition and Academic Emotions

KEY WORDS:

Artificial Intelligence in Education; Metacognition; Academic Emotions; Higher Education; Programming.

PAGES: 54
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Sistemas de Computação
SPECIALTY: Teleinformática
SUMMARY:

With the advancement of educational technologies, especially natural language models, new possibilities emerge to support the learning process, requiring a deeper understanding of how students use these resources, with what intentions, and with which emotional and cognitive implications. From the perspective that metacognition involves the ability to reflect, plan, monitor, and evaluate one’s own learning process, and that emotions directly influence motivation and academic performance, this research seeks to identify possible correlations between these two aspects in the use of generative AI. The general objective of the research is to investigate how levels of metacognitive awareness and academic emotions of motivation and frustration are articulated during the process of learning programming in higher education, with and without the use of generative artificial intelligence. For this purpose, the Metacognitive Awareness Inventory (MAI) will be applied at the beginning of the semester, aiming to identify students’ levels of metacognition and possible changes in their perception of their own learning processes throughout the academic term. In parallel, academic emotions of motivation and frustration expressed by participants will be mapped during activities supported by generative artificial intelligence and in traditional learning situations, without the use of these technologies. The research adopts a mixed methodological approach, combining quantitative and qualitative data (reflective records and notes taken during interactions). The analysis will integrate statistical procedures and thematic content analysis. It is expected to understand how different metacognitive and emotional profiles relate to students’ motivation and performance in the context of programming in higher education. The results may inform pedagogical practices more sensitive to both cognitive aspects that involve AI and the affective dimensions of learning, in addition to guiding the development of adaptive educational resources based on empirical evidence.

COMMITTEE MEMBERS:
Interno - FABIO YOSHIMITSU OKUYAMA
Externa à Instituição - LUCIA MARIA MARTINS GIRAFFA - PUC - RS
Interna - MARCIA AMARAL CORREA UGHINI VILLARROEL
Interna - MARCIA HAFELE ISLABAO FRANCO
Presidente - RODRIGO PRESTES MACHADO