AUTHOR=Arguedas Marta , Daradoumis Thanasis , Caballé Santi 

TITLE=Measuring the effects of pedagogical agent cognitive and affective feedback on students’ academic performance

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 7 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1495342

DOI=10.3389/frai.2024.1495342

ISSN=2624-8212

ABSTRACT=There is still a debate on the influence and effectiveness of pedagogical agents in a learning environment, especially on the means these agents employ for enhancing students’ academic performance. The current study aims at measuring the effectiveness of cognitive and affective feedback (CaAF) types that a human teacher and a virtual Affective Pedagogical Tutor (APT) used in their groups of students (control and experimental groups respectively) in an authentic long-term learning situation. Participants were a sample of 115 students carrying out collaborative activities in a “web design” course. Our findings showed that APT cognitive feedback (CF) significantly increased students’ learning outcomes compared to the human teacher’s feedback, whereas APT affective feedback (AF) only achieved partial success. Nevertheless, the study has some limitations: it is based on a single course and a specific academic context, limiting the generalizability of its findings. Additionally, while cognitive feedback demonstrated a clear impact, the analysis of affective feedback was less conclusive, and its design requires further refinement. Finally, the cross-sectional design of the study restricts the ability to assess whether improvements in learning outcomes persist over time. Future research directions include exploring the generalizability of results across diverse disciplines, deepening the analysis of affective feedback, and incorporating longitudinal studies to evaluate the durability of the observed effects.