AI-Assisted Teaching and Learning: A Real Application Of Course Design, Assessment and Evaluation in Higher Education

Abstract

In the rapidly evolving field of educational technology, the potential for integrating Artificial Intelligence (AI) tools like ChatGPT into teaching and learning processes remains untapped and ripe for exploration. This study aims to address the gaps in this field by proposing a framework of AI-assisted teaching and Learning, namely Decompose-Ask-Response (AI-DAR). We compare the learning outcomes of students exposed to AI-assisted teaching with those in a traditional setting. Our findings reveal that AI-assisted instruction significantly enhances student performance, particularly for those with weaker foundational knowledge. Moreover, we find that AI is notably effective in aiding conceptual understanding but shows limited impact on operational tasks. An analysis of student-AI interactions indicates that the ability to formulate effective questions to AI are critical factors for achieving higher scores. Gender-specific behaviors also emerged as differential factors. Overall, this study establishes the efficacy of AI-assisted teaching and offering actionable insights, setting a foundation for future research in this rapidly expanding field.

Ähnliche Arbeiten