The New Calculator? Practices, Norms, and Implications of Generative AI in Higher Education
Project Overview
Generative AI (GenAI) is reshaping higher education by offering both significant opportunities and notable challenges. The document outlines how students are motivated to use GenAI for its accessibility and efficiency, despite concerns regarding plagiarism and skill development. Many students navigate this landscape with unclear university guidelines, leading to a degree of self-governance in their GenAI usage. Educators display mixed feelings, recognizing the potential benefits of GenAI while expressing worries about maintaining academic integrity and the potential erosion of essential skills among students. The integration of GenAI tools in various educational contexts, including programming and creative tasks, is examined, showcasing their ability to enhance learning experiences. However, the document also emphasizes the need for clear guidelines and responsible use training to mitigate concerns and improve implementation. Key research questions are proposed to address these challenges and foster better integration of GenAI in education. Moving forward, there is a call for more defined policies and practices to responsibly incorporate GenAI into assessment and learning processes, ensuring that both educators and students can harness its benefits while safeguarding academic standards.
Key Applications
GenAI for Learning Enhancement and Support
Context: Students and educators utilizing generative AI tools for a variety of educational purposes, including tutoring, generating study materials, summarizing information, brainstorming ideas, enhancing digital art creativity, and improving assessment processes.
Implementation: Students and educators interact with generative AI tools such as ChatGPT and other LLM-based applications for diverse functions like code assistance, content generation, summarization, grammar correction, and creative ideation. The tools are integrated into self-paced learning environments, digital art courses, and higher education assessments.
Outcomes: Enhanced understanding of study materials, improved coding skills, increased creativity and engagement in art education, and potential for improved learning outcomes and engagement in assessments.
Challenges: Concerns about over-reliance on AI tools, accuracy of AI responses, potential loss of original writing style, trust and reliability of AI-generated outputs, originality in digital art, and the need for effective guidelines around academic integrity.
Implementation Barriers
Institutional
Unclear university guidelines regarding GenAI use leading to confusion among students.
Proposed Solutions: Develop clear, comprehensive guidelines involving both students and educators.
Communication
Inconsistent communication from educators regarding appropriate use of GenAI tools and lack of open communication among educators hinders consistent teaching practices.
Proposed Solutions: Encourage open dialogues among educators about GenAI to foster a supportive learning environment and empower educators to share experiences while developing clear, systematic guidelines.
Skill Development
Concerns over skill loss due to over-reliance on GenAI tools.
Proposed Solutions: Implement safeguards and training to maintain essential skills alongside GenAI usage.
Literacy Barrier
Insufficient GenAI literacy among students and educators affects effective tool use.
Proposed Solutions: Develop multi-faceted training programs to enhance GenAI literacy.
Integrity Barrier
Concerns regarding academic integrity and the potential for plagiarism with AI tools.
Proposed Solutions: Redesign assessments to incorporate AI while maintaining integrity and critical thinking.
Project Team
Auste Simkute
Researcher
Viktor Kewenig
Researcher
Abigail Sellen
Researcher
Sean Rintel
Researcher
Lev Tankelevitch
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Auste Simkute, Viktor Kewenig, Abigail Sellen, Sean Rintel, Lev Tankelevitch
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