I would love this to be like an assistant, not the teacher: a voice of the customer perspective of what distance learning students want from an Artificial Intelligence Digital Assistant
Project Overview
The document explores the integration of generative AI in education, specifically within distance learning environments, emphasizing its transformative potential. A key study highlighted students' perceptions of an AI Digital Assistant (AIDA), which indicated that learners anticipate services such as real-time assistance, personalized academic support, and emotional encouragement. Despite the enthusiasm for AI tools that could enrich the educational experience, the study also revealed significant concerns regarding ethical issues, data privacy, academic integrity, and the risk of students becoming overly dependent on technology. Overall, the findings underscore a strong student interest in leveraging AI to enhance learning while simultaneously stressing the necessity for responsible implementation to mitigate potential adverse effects.
Key Applications
AI-Powered Student Support and Feedback System
Context: Distance learning students across various educational levels, including those needing essay writing support and accessibility assistance.
Implementation: Development of AI-driven systems that provide real-time feedback on academic tasks such as essay writing and facilitate accessibility for students with disabilities. This includes interactive feedback mechanisms and digital assistants that gather student insights and assist in academic tasks.
Outcomes: Enhanced student engagement and support through personalized feedback, improved writing practices, and better accessibility for students with diverse needs.
Challenges: Addressing ethical implications, ensuring data privacy, avoiding academic dishonesty, catering to diverse student profiles, and maintaining the effectiveness of the systems.
Predictive Learning Analytics System
Context: Distance learning institutions supporting teaching and learning through insights into student performance.
Implementation: Development of a predictive analytics system utilizing machine learning to create risk profiles for students, enabling educators to identify students who may need additional support.
Outcomes: Improved educator insights into student learning patterns and enhanced support mechanisms for at-risk students.
Challenges: Ensuring the reliability and validity of analytics, maintaining ethical standards in data use, and addressing concerns regarding student data privacy.
Implementation Barriers
Ethical and social implications
Concerns about AI's role in educational processes and the necessity to maintain human interaction.
Proposed Solutions: Ensure AI assists rather than replaces human educators, emphasizing the importance of human contact.
Data privacy
Worries about how student data is used, privacy concerns, and biases in AI.
Proposed Solutions: Implement clear guidelines for data usage, ensure transparency, and obtain student consent for data collection.
Academic integrity
Potential misuse of AI for completing assignments and issues of plagiarism.
Proposed Solutions: Establish guidelines and monitoring systems to prevent misuse of AI in academic settings.
Operational challenges
AI might inadvertently affect learning outcomes and student interactions.
Proposed Solutions: Regularly evaluate AI tools for accuracy and reliability to ensure positive educational outcomes.
Project Team
Bart Rienties
Researcher
John Domingue
Researcher
Subby Duttaroy
Researcher
Christothea Herodotou
Researcher
Felipe Tessarolo
Researcher
Denise Whitelock
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Bart Rienties, John Domingue, Subby Duttaroy, Christothea Herodotou, Felipe Tessarolo, Denise Whitelock
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