Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors
Project Overview
The document examines the role of generative AI and large language models (LLMs) in education, particularly among university instructors, assessing their awareness, attitudes, and experiences with these technologies. It finds that educators generally hold positive views regarding the integration of AI tools, recognizing potential benefits such as increased efficiency and the ability to provide personalized teaching experiences. Nonetheless, the study also identifies notable concerns, including issues related to academic integrity, potential stifling of creativity, and the complexities of assessment in AI-enhanced environments. Utilizing a mixed-methods approach, including surveys and interviews, the research uncovers significant variations in attitudes towards generative AI across different academic disciplines, indicating that while enthusiasm for these technologies exists, apprehensions about their implications for teaching and learning must be addressed to fully leverage their capabilities in educational settings. Overall, the findings suggest a cautious optimism among educators, emphasizing the need for thoughtful implementation and ongoing dialogue about the ethical and practical challenges posed by AI in education.
Key Applications
LLM-based tools like GPT-4, CodeX, GitHub Copilot
Context: Higher education programming courses for university instructors
Implementation: Incorporation of LLMs in programming assignments and assessments
Outcomes: Improved performance in assessments and potential curriculum redesign
Challenges: Risks of incorrect solutions, plagiarism, and over-reliance on AI
Implementation Barriers
Technical limitations
Limited technical understanding of AI tools among educators
Proposed Solutions: Professional development and training workshops for educators
Ethical concerns
Concerns about cheating, plagiarism detection, and the integrity of assessments
Proposed Solutions: Developing new assessment methods that account for AI assistance
Resistance to change
Instructors' reservations about relying on AI tools in teaching
Proposed Solutions: Showing positive examples and case studies of successful integration of AI tools
Project Team
Aashish Ghimire
Researcher
James Prather
Researcher
John Edwards
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Aashish Ghimire, James Prather, John Edwards
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