Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators
Project Overview
The document explores the transformative potential of generative AI, particularly large language models (LLMs), in enhancing project-based learning (PBL) within K-12 education. Through a co-design study with educators, it identifies how LLM tools can alleviate challenges in PBL implementation by automating routine tasks, personalizing learning experiences, and facilitating project workflow management. Key applications of generative AI, such as using tools like ChatGPT, are highlighted for their role in supporting engineering education, enhancing teacher competency, and improving student learning outcomes. While the findings indicate increased student engagement and enriched learning experiences, the document also addresses significant challenges, including ethical considerations, the need for professional development, and the integration of AI into existing curricula. Overall, it underscores the dual nature of generative AI in education, presenting both promising opportunities for innovation and critical obstacles that need addressing to ensure effective implementation.
Key Applications
Generative AI Tools for Project-Based Learning and Course Development
Context: K-12 and higher education settings, including teachers and students engaged in project-based learning, course development, and online courses.
Implementation: Integration of generative AI tools, such as Large Language Models (LLMs) and ChatGPT, in project-based learning activities and course development processes. This includes co-design workshops with educators to identify challenges, brainstorming tool features, and using AI for enhancing collaboration, idea generation, and educational content design.
Outcomes: Improved student engagement, collaborative skills, effective course design, and personalized educational experiences. Enhanced project management and assessment methods leading to increased student autonomy.
Challenges: Integration of AI tools requires addressing ethical considerations, teacher training, resource constraints, and ensuring fairness and reliability in AI-generated assessments. Data privacy concerns and the need for robust analytics frameworks are also critical.
AI Performance Prediction and Learning Analytics
Context: Online courses in higher education, focusing on engineering and various educational assessments.
Implementation: Utilization of AI tools for predicting student performance, providing tailored learning analytics, and analyzing educational assessments to improve their effectiveness.
Outcomes: Enhanced student learning through personalized educational experiences and more accurate and efficient assessment methods.
Challenges: Data privacy concerns and the need for robust analytics frameworks.
Implementation Barriers
Implementation barrier
Ethical concerns regarding data privacy and algorithmic bias in AI-generated feedback.
Proposed Solutions: Careful design and human oversight of LLM tools, along with comprehensive professional development for teachers.
Resource barrier
Limited access to technology and high-speed internet in some K-12 settings, leading to digital inequities.
Proposed Solutions: Focusing on strategic integration of tools that require minimal resources and supporting schools with fewer technological means.
Training barrier
Teachers may lack the necessary training to effectively integrate and use generative AI tools in their teaching practices.
Proposed Solutions: Provide targeted professional development and training programs for educators to improve their AI competencies and emphasize the benefits and functionalities of LLM tools.
Integration Barrier
Challenges in integrating AI tools into existing curricula.
Proposed Solutions: Develop clear guidelines and frameworks for the effective incorporation of AI in educational practices.
Data Privacy Barrier
Concerns over data privacy and security when using AI tools.
Proposed Solutions: Implement robust data protection policies and ensure compliance with privacy regulations.
Project Team
Prerna Ravi
Researcher
John Masla
Researcher
Gisella Kakoti
Researcher
Grace Lin
Researcher
Emma Anderson
Researcher
Matt Taylor
Researcher
Anastasia Ostrowski
Researcher
Cynthia Breazeal
Researcher
Eric Klopfer
Researcher
Hal Abelson
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Prerna Ravi, John Masla, Gisella Kakoti, Grace Lin, Emma Anderson, Matt Taylor, Anastasia Ostrowski, Cynthia Breazeal, Eric Klopfer, Hal Abelson
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