Rethinking the Potential of Multimodality in Collaborative Problem Solving Diagnosis with Large Language Models
Project Overview
The document explores the transformative role of generative AI, particularly large language models (LLMs) and multimodal data, in enhancing the assessment of Collaborative Problem Solving (CPS) skills within education. It highlights research demonstrating that transformer-based models, especially those leveraging both text and audio inputs, significantly outperform traditional assessment methods in identifying indicators of CPS. Despite these advancements, the findings indicate that the efficacy of multimodal approaches can vary based on the specific characteristics of the CPS indicators being evaluated. Furthermore, the paper underscores the critical role of human-AI collaboration in educational settings, suggesting that combining AI capabilities with human insights can lead to more effective and nuanced assessments of student skills. Overall, the document presents a promising outlook on the integration of generative AI in education, advocating for a balanced approach that harnesses the strengths of AI while emphasizing the irreplaceable value of human expertise.
Key Applications
Multimodal classification model for diagnosing collaborative problem solving (CPS) subskills
Context: Secondary education, specifically targeting students aged 14-15 in mathematics classes
Implementation: Utilized transformer-based models with multimodal data (text embeddings from verbal data and acoustic embeddings from audio data) to automate the diagnosis of CPS indicators
Outcomes: Improved detection of CPS classes with transformer-based models, especially for social-cognitive CPS classes
Challenges: Integration of multimodal data did not universally improve model performance; complexity of CPS indicators and data composition affected outcomes
Implementation Barriers
Technical
Challenges in accurately detecting CPS indicators in noisy classroom environments and the reliance on manual coding of indicators.
Proposed Solutions: Automated detection using analytics and machine learning techniques, exploring ensemble learning methods for model improvements.
Data-related
Limited empirical work on the potential of LLMs and multimodal data for CPS diagnosis, particularly in real-world scenarios.
Proposed Solutions: Encouraging more rigorous empirical studies and cross-validation with diverse datasets to enhance generalizability.
Project Team
K. Wong
Researcher
B. Wu
Researcher
S. Bulathwela
Researcher
M. Cukurova
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: K. Wong, B. Wu, S. Bulathwela, M. Cukurova
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