Large language models as collaborative learning partners in higher education: a theoretical framework

Abstract

The integration of Large Language Models (LLMs) into higher education environments reveals notable challenges within existing educational technology frameworks. Current theoretical models conceptualize artificial intelligence as an instrumental tool that supports human learning processes, yet numerous empirical studies demonstrate that LLMs function as collaborative learning partners with reciprocal agency, dynamic adaptation capabilities, and shared knowledge-construction behaviors. Research in Hungarian higher education contexts provides compelling evidence for this partnership phenomenon, with studies revealing that students primarily use AI systems for collaborative tasks such as brainstorming, information exploration, and iterative problem-solving rather than simple tool operation (Folmeg et al., 2024). I pro-pose the Collaborative Learning Partnership Model as a theoretical framework that reconceptualizes LLMs as active learning agents rather than passive educational instruments. Drawing from systematic analyses of recent implementation research across multiple educational domains, my model identifies four core components: reciprocal agency, dynamic contextual adaptation, shared knowledge construction, and transparent collaboration protocols. This frame-work addresses challenges in AI integration, including academic integrity concerns, assessment innovation, and pedagogical role transformation. The model provides a theoretical foundation for effective LLM integration while maintaining educational rigor and human agency in learning processes. Furthermore, I present a four-stage partnership development progression that guides practical implementation strategies.