Interactionalism: Re-Designing Higher Learning for the Large Language Agent Era
Project Overview
The document explores the transformative potential of Generative AI (GenAI) in education through the lens of Interactionalism, advocating for a re-design of educational practices to incorporate interactional intelligence, which includes meta-cognitive and meta-emotional skills. It underscores the importance of moving away from conventional, linear educational approaches towards more dialogical and interactive learning experiences enabled by dialogical agents (DAs) derived from Large Language Models (LLMs). The use of GenAI is positioned as a means to foster personalized learning and enhance student engagement, thereby equipping learners with critical skills essential for today’s job market. Additionally, the document addresses the challenges that GenAI poses to assessment integrity within traditional educational frameworks, highlighting the need for innovative assessment methods that align with the interactive, personalized learning environment that GenAI can create. Overall, the findings suggest that embracing GenAI in education can lead to more effective and relevant learning experiences, preparing students for the complexities of the modern workforce.
Key Applications
Dialogical agents (DAs) based on Large Language Models (LLMs)
Context: Higher education, aimed at students and educators
Implementation: Redesigning educational practices to integrate DAs into learning scenarios, allowing for personalized and interactive learning experiences.
Outcomes: Enhanced learner engagement, development of interactional intelligence, and improved learning outcomes through continuous feedback.
Challenges: Potential issues with assessment integrity due to reliance on GenAI for producing answers and concerns over the quality of learning from AI.
Implementation Barriers
Technological
Challenges in effectively integrating GenAI into existing educational frameworks and practices.
Proposed Solutions: Redesign of learning activities to incorporate interactional approaches that utilize GenAI technologies.
Assessment Integrity
Risk of reducing the reliability of skill assessments due to the ability of learners to use GenAI to produce 'good enough' answers.
Proposed Solutions: Developing new assessment frameworks that focus on interactions with DAs rather than traditional one-shot evaluations.
Project Team
Mihnea C. Moldoveanu
Researcher
George Siemens
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mihnea C. Moldoveanu, George Siemens
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