Paper Number
ICIS2025-1699
Paper Type
Short
Abstract
This study aims to explore how student perceptions of AI-powered Teaching Assistants influence engagement, learning processes, and understanding of course material. Adopting a mixed-methods approach, the research investigates the role of factors - perceived usefulness, clarity of feedback, and trust in AI in shaping student experiences. Guided by the Technology Acceptance Model, Constructivist Learning Theory, and Cognitive Load Theory, we propose a research model to be tested through data collected through a survey administered to university students, with quantitative data analyzed using Covariance-Based Structural Equation Modeling and qualitative data examined through thematic analysis. By examining the interactions between students and AI Teaching Assistants, the study aims to provide new insights into how these tools support learner autonomy, foster engagement, reduce cognitive strain and impact learning outcomes. The findings are expected to inform the development of more effective strategies for integrating AI into educational settings.
Recommended Citation
Bhagat, Sarbottam and Sveum, Evan, "Navigating AI Teaching Assistants: How Perceptions and Interactions Shape Student Engagement, Cognition, and Learning" (2025). ICIS 2025 Proceedings. 8.
https://aisel.aisnet.org/icis2025/learn_curricula/learn_curricula/8
Navigating AI Teaching Assistants: How Perceptions and Interactions Shape Student Engagement, Cognition, and Learning
This study aims to explore how student perceptions of AI-powered Teaching Assistants influence engagement, learning processes, and understanding of course material. Adopting a mixed-methods approach, the research investigates the role of factors - perceived usefulness, clarity of feedback, and trust in AI in shaping student experiences. Guided by the Technology Acceptance Model, Constructivist Learning Theory, and Cognitive Load Theory, we propose a research model to be tested through data collected through a survey administered to university students, with quantitative data analyzed using Covariance-Based Structural Equation Modeling and qualitative data examined through thematic analysis. By examining the interactions between students and AI Teaching Assistants, the study aims to provide new insights into how these tools support learner autonomy, foster engagement, reduce cognitive strain and impact learning outcomes. The findings are expected to inform the development of more effective strategies for integrating AI into educational settings.
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