A Telegram-Based Platform for Self-Assessment: NLP-Driven Classification and Collaborative Question Generation

Abstract

The increasing adoption of conversational interfaces and semantic technologies in education is enabling new forms of scalable, personalised, and reflective assessment. In this context, a self-assessment platform has been designed and deployed within Telegram to promote formative learning through collaborative content creation and iterative practice. The system allows students to generate multiple-choice questions (MCQs), which are automatically classified into instructional topics using Natural Language Processing (NLP) techniques. These questions are reused to construct personalized quizzes, offering immediate feedback and supporting iterative improvement. During a semester-long deployment involving over 149 participants, students authored 519 MCQs and completed 1,709 quizzes. Results indicate high engagement, consistent score improvements across attempts, and effective reuse of learner-generated content, demonstrating the potential of NLP-assisted, mobile-friendly assessment to support self-regulated learning and performance growth.