Large-scale introductory CS courses, often enrolling thousands of students, struggle to provide personalized support and encourage active participation. While recent large language models (LLMs) have enabled AI teaching assistants at scale, most existing systems remain reactive, responding only after students explicitly initiate queries. We present SCALA, a student-centered AI learning assistant designed to provide proactive support for students. SCALA introduces predictive query management, a mechanism that generates likely student questions and answers ahead of lectures. Students may choose to view these pre-generated question–answer pairs or engage in interactive conversations with our tutoring model via the same interface. We evaluate SCALA through a semester-long deployment in an undergraduate Python course with over 1,500 students, and find that predictive queries are frequently selected in practice and substantially overlap with real student questions. Based on student feedback, learners preferred SCALA’s responses to their real queries over alternatives such as GPT-4o. These results suggest proactive support as a promising direction for future development of AI-powered teaching assistants. We will release our codebase and interactive demo upon acceptance.

In this paper, we describe POSTECH system for IWSLT 2008 evaluation campaign. The system is based on phrase based statistical machine translation. We set up a baseline system using well known freely available software. A preprocessing method and a language modeling method have been applied to the baseline system in order to improve machine translation quality. The preprocessing method is to identify and remove useless tokens in source texts. And the language modeling method models phrase level n-gram. We have participated in the BTEC tasks to see the effects of our methods.