Interactionalism: Re-Designing Higher Learning for the Large Language Agent Era

TL;DR

This paper proposes Interactionalism, a new framework for designing learning in the era of Generative AI, emphasizing interactional intelligence as a key skill set enhanced through working with Large Language Models.

Contribution

It introduces Interactionalism as a practical blueprint for integrating GenAI into learning practices, focusing on developing interactional intelligence with LLM-based agents.

Findings

Interactionalism provides guiding principles for AI-enhanced learning.

Meta-cognitive and meta-emotional skills are central to interactional intelligence.

Working with LLMs can proactively develop learners' skills.

Abstract

We introduce Interactionalism as a new set of guiding principles and heuristics for the design and architecture of learning now available due to Generative AI (GenAI) platforms. Specifically, we articulate interactional intelligence as a net new skill set that is increasingly important when core cognitive tasks are automatable and augmentable by GenAI functions. We break down these skills into core sets of meta-cognitive and meta-emotional components and show how working with Large Language Model (LLM)-based agents can be proactively used to help develop learners. Interactionalism is not advanced as a theory of learning; but as a blueprint for the practice of learning - in coordination with GenAI.