FIRE 2019 AILA Track: Artificial Intelligence for Legal Assistance The FIRE 2019 AILA track focused on creating a framework for evaluating different methods of retrieving relevant priorprecedent cases and statutes given a factual scenario. There were two tasks for this track: (i) Identifying relevant prior cases for a given situation (Precedent Retrieval), and (ii) Identifying most relevant statutes for a given situation (Statute Retrieval). Given a situation that can lead to filing a case, the precedent retrieval task aims at finding case documents where similar legal situations were addressed. The statute retrieval task aims at finding relevant statutes that are applicable to the situation. The factual scenarios, statutes and prior case documents used in the tasks were from the Indian Supreme Court judiciary.

FieldAware Knowledge Tracing Machine by Modelling Students Dynamic Learning Procedure and Item Difficulty Knowledge tracing is essential for adaptive learning to obtain studentsu0027 current knowledge states to provide adaptive service. However, the learning process is evolving constantly since students learn and forget over time. Moreover, the difficulty of learning materials can also have huge influence on their performances. This work shows an ongoing project on knowledge tracing by proposing a FieldAware Knowledge Tracing Machine (FAKTM) to integrate studentsu0027 dynamic learning procedure (learning and forgetting) and adaptive item difficulty. We propose methods to model item difficulty and the dynamic learning procedure and present the framework to integrate them together. Preliminary analyses on a dataset show the promising results.