Deconstructing Student Perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based Instrument
Project Overview
The document explores the integration of generative AI (GenAI) in higher education, focusing on student perceptions through the lens of Expectancy-Value Theory (EVT). It reveals a strong positive correlation between students' perceived value of GenAI and their intention to utilize it, while indicating a weak negative correlation between perceived costs and the intention to adopt this technology. The findings emphasize the significance of understanding students’ experiences and perceptions to effectively incorporate AI technologies in educational settings. Additionally, the document addresses the potential ethical dilemmas and long-term implications of using GenAI, underscoring the need for careful consideration of these factors in the development and implementation of AI tools in education. Overall, the study highlights the promising applications of generative AI in enhancing educational outcomes while also cautioning against overlooking the ethical dimensions involved in its use.
Key Applications
ChatGPT as a generative AI tool
Context: Higher education, targeting university students
Implementation: Used a questionnaire based on EVT to gather data on student perceptions.
Outcomes: Found a positive correlation between perceived value of GenAI and intention to use it, indicating that students see the potential for benefits in using AI tools for their education.
Challenges: Concerns about ethical implications, academic integrity, and the potential for over-reliance on AI technologies.
Implementation Barriers
Perceived Cost
Students perceive the costs of using GenAI as potentially undermining the value of education, limiting social interactions, and hindering the development of holistic competencies.
Proposed Solutions: Promote social and experiential learning, enhance interpersonal interactions, and provide guidance on the effective use of GenAI.
Project Team
Cecilia Ka Yuk Chan
Researcher
Wenxin Zhou
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Cecilia Ka Yuk Chan, Wenxin Zhou
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai