CollaClassroom: An AI-Augmented Collaborative Learning Platform with LLM Support in the Context of Bangladeshi University Students

TL;DR

This study evaluates an AI-augmented collaborative learning platform with LLM support among Bangladeshi university students, showing positive engagement, usability, and potential for promoting equitable participation in a Global South context.

Contribution

It provides the first empirical assessment of LLM-supported collaborative learning in a Bangladeshi university setting, highlighting design implications for fairness and engagement.

Findings

High student receptivity to LLM support (92%)

Positive usability ratings for learnability and reliability

Strong correlation between perceived fairness and meaningful contribution

Abstract

CollaClassroom is an AI-enhanced platform that embeds large language models (LLMs) into both individual and group study panels to support real-time collaboration. We evaluate CollaClassroom with Bangladeshi university students (N = 12) through a small-group study session and a pre-post survey. Participants have substantial prior experience with collaborative learning and LLMs and express strong receptivity to LLM-assisted study (92% agree/strongly agree). Usability ratings are positive, including high learnability(67% "easy"), strong reliability (83% "reliable"), and low frustration (83% "not at all"). Correlational analyses show that participants who perceive the LLM as supporting equal participation also view it as a meaningful contributor to discussions (r = 0.86). Moreover, their pre-use expectations of LLM value align with post-use assessments (r = 0.61). These findings suggest that LLMs can enhance engagement and perceived learning when designed to promote equitable turn-taking and transparency across individual and shared spaces. The paper contributes an empirically grounded account of AI-mediated collaboration in a Global South higher-education context, with design implications for fairness-aware orchestration of human-AI teamwork.