The field of artificial intelligence is moving towards developing more fair and adaptive systems. Researchers are working on designing algorithms that can mitigate bias and adapt to changing social environments. A key direction is the development of frameworks that can simulate the evolution of ethical and legal frameworks, allowing for more flexible and adaptive-over-time systems. Another area of focus is the improvement of knowledge tracing models, which aim to capture students' knowledge mastery from their historical interactions. Innovations in this area include the use of memory-and-forgetting methods, pattern-based knowledge component extraction, and forward-looking knowledge tracing. Noteworthy papers include: The Fair Game, which proposes a dynamic mechanism to assure fairness in ML algorithm predictions and adapt to societal interactions over time. EDGE, which presents a general-purpose, misconception-aware adaptive learning framework that unifies psychometrics, cognitive diagnostics, and principled scheduling. The Feedback-Driven Decision Support System, which enables continuous model refinement and improves prediction accuracy by learning from real-world academic progress. MemoryKT, which simulates memory dynamics through a three-stage process and jointly models the complete encoding-storage-retrieval cycle. Pattern-based Knowledge Component Extraction, which provides an automated, scalable, and explainable framework for identifying granular code patterns and algorithmic constructs. Collective dynamics of strategic classification, which applies evolutionary game theory to address the problem of feedback loops between collectives of users and institutions.