Generating Situated Reflection Triggers about Alternative Solution Paths: A Case Study of Generative AI for Computer-Supported Collaborative Learning
This work addresses the challenge of engaging students with dynamic feedback in collaborative learning environments, though it is incremental as it applies existing LLM technology to a specific educational domain.
The researchers tackled the problem of providing contextualized feedback in computer-supported collaborative learning by augmenting an online programming exercise bot with ChatGPT to generate reflection triggers based on student discussions. In a pilot study with 34 students, they demonstrated that LLMs can produce highly situated triggers that incorporate collaborative context details.
An advantage of Large Language Models (LLMs) is their contextualization capability - providing different responses based on student inputs like solution strategy or prior discussion, to potentially better engage students than standard feedback. We present a design and evaluation of a proof-of-concept LLM application to offer students dynamic and contextualized feedback. Specifically, we augment an Online Programming Exercise bot for a college-level Cloud Computing course with ChatGPT, which offers students contextualized reflection triggers during a collaborative query optimization task in database design. We demonstrate that LLMs can be used to generate highly situated reflection triggers that incorporate details of the collaborative discussion happening in context. We discuss in depth the exploration of the design space of the triggers and their correspondence with the learning objectives as well as the impact on student learning in a pilot study with 34 students.