Abstract
At the forefront of Artificial Intelligence of Things, this paper delves into empathic agents to revolutionize computer competencies acquisition and catalyze motivational, regulatory, and metacognitive dynamics in online higher education. Previous research on student processing of empathic feedback has been limited, often neglecting learning performance and its impact on students’ motivation, self-regulation, and metacognitive reasoning. The objective was to analyze the effectiveness of empathic feedback, cognitive and affective, on these four issues in online learning. A quasi-experimental design was used, in which a conversational agent, DSLab-Bot, was integrated into the syllabus and Information Technology infrastructure. Students from an online university's Distributed Systems course participated (N= 196), selected through one-stage cluster probability sampling. They were divided into experimental and control groups receiving feedback from DSLab-Bot and the teacher, respectively. Results showed no significant differences between the groups in learning performance, motivation, or self-regulation, except in one item of motivation (self-efficacy) and self-regulation. There were strong correlations between thirteen cognitive (1–4, 6, 7, 9–15) and seven affective (1, 4–9) chatbot feedback types with conceptual change (MRCC) and personal growth and understanding (MRPGU). There were high weights of similar chatbot feedback types indicating a pronounced influence of these on metacognitive reasoning components, even self-reflection (MRSR). In conclusion, empathic chatbot feedback is as effective as human teacher feedback in facilitating learning, motivation, and self-regulation. Moreover, specific empathic feedback types are crucial in fostering MRCC, MRPGU, and MRSR strongly. Practitioners should consider these specific types of empathic feedback for future empathic agent configurations.
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