The field of educational technology is moving towards the development of more sophisticated and personalized learning systems. Researchers are exploring the potential of large language models to generate hints and provide feedback to learners, rather than simply giving away answers. This approach aims to promote active engagement and critical thinking, and to develop learner-centered educational technologies. Another area of focus is the improvement of code differencing techniques, which are essential for efficient change comprehension in software engineering. Additionally, there is a growing interest in literacy tracing, which involves modeling the growth of higher-order cognitive abilities and literacy from learners' interaction sequences. Noteworthy papers include: Designing and Evaluating Hint Generation Systems for Science Education, which explores the potential of large language models to generate chains of hints that scaffold learners without revealing answers. BDiff: Block-aware and Accurate Text-based Code Differencing, which proposes a text-based differencing algorithm capable of identifying block-level edit actions. TLSQKT: A Question-Aware Dual-Channel Transformer for Literacy Tracing from Learning Sequences, which instantiates a Transformer-based model for literacy tracing and achieves state-of-the-art results on literacy-oriented metrics.