Who is Helping Whom? Student Concerns about AI- Teacher Collaboration in Higher Education Classrooms
Project Overview
The document examines the role of generative AI in education, particularly its integration into higher education to support teachers by delivering data-driven insights and personalized feedback to students. It highlights the potential for AI tools to enhance teaching efficiency and improve learning outcomes, while also addressing concerns related to classroom dynamics, privacy, and biases inherent in AI systems. The findings underscore the necessity of understanding the complex interactions among students, teachers, and AI, calling for educational designs that take into account the specific contexts of learning environments and the implications for all stakeholders. Additionally, it discusses various applications of generative AI, emphasizing how these technologies can enrich learning experiences, boost student engagement, and tailor feedback. Ethical considerations, such as privacy and algorithmic bias, are also outlined as critical challenges that must be navigated in the implementation of AI in educational settings. Overall, the document advocates for a thoughtful and context-sensitive approach to the use of generative AI in education to maximize benefits while mitigating risks.
Key Applications
AI-Enabled Classroom Analysis and Engagement Monitoring
Context: Higher education and K-12 classrooms targeting both teachers and students, involving real-time monitoring of student engagement and behavior through ambient sensing and AI tools.
Implementation: Integration of AI tools and ambient sensors (e.g., computer vision) to collect and analyze data on student behavior and performance in real-time. This approach includes systems like ClassID which analyze student behavior through environmental sensors and tools for classroom analysis and intervention.
Outcomes: Enhanced teaching efficiency, tailored interventions based on student engagement, improved learning outcomes, and increased teacher and student ownership of technology integration.
Challenges: Concerns about privacy, algorithmic bias, inaccurate representations of behavior, and balancing diverse stakeholder needs.
AI Guidance for Fair Assessment and Policy Development
Context: Policy guidance for educators and educational leaders in both K-12 and higher education, focusing on equity in assessment practices.
Implementation: Research and recommendations on integrating AI in education, including transparency in student assessment algorithms and addressing algorithmic bias to ensure fairness in evaluation methods.
Outcomes: Enhanced understanding of AI's role in education, informed policymaking, increased awareness of bias in assessments, and implications for student evaluations.
Challenges: Resistance to change from traditional educational practices, complexity in addressing algorithmic bias, and ensuring fairness in assessments.
Implementation Barriers
Ethical
Concerns about privacy violations, data misuse, algorithmic bias, and the ethical implications of AI monitoring systems and educational assessments.
Proposed Solutions: Implementing strict data governance and privacy protection measures, establishing ethical guidelines, and involving diverse stakeholders in the development of AI systems.
Technical
AI systems may oversimplify complex educational contexts and fail to accurately assess student engagement. Integration of AI tools can be hampered by technical difficulties such as data privacy issues and system compatibility.
Proposed Solutions: Incorporating contextual understanding and human oversight in AI evaluations, developing clear data governance frameworks, and ensuring systems are interoperable.
Social
Potential biases in AI systems could lead to unequal treatment of students based on race, gender, or behavior.
Proposed Solutions: Raising awareness of AI biases and ensuring diverse data representation.
Cultural Barrier
Resistance from educators and institutions to adopt AI technologies due to fear of change or misunderstanding of AI capabilities.
Proposed Solutions: Provide training and awareness programs to demonstrate the benefits of AI in education.
Project Team
Bingyi Han
Researcher
Simon Coghlan
Researcher
George Buchanan
Researcher
Dana McKay
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Bingyi Han, Simon Coghlan, George Buchanan, Dana McKay
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