In the constantly changing field of data-driven decision making, accurately predicting future events is crucial for strategic planning in various sectors. The emergence of Large Language Models (LLMs) marks a significant advancement in this area, offering advanced tools that utilise extensive text data for prediction. In this industry paper, we introduce a novel method for AI-driven foresight using LLMs. Building on top of previous research, we employ data on current trends and their trajectories for generating forecasts on 15 different topics. Subsequently, we estimate their probabilities via a multi-step approach based on log probabilities. We show we achieve a Brier score of 0.186, meaning a +26% improvement over random chance and a +19% improvement over widely-available AI systems.

The field of AI has changed significantly in the past couple of years and will likely continue to do so. Driven by a desire to expose our students to relevant and modern materials, we conducted two surveys, one of AI instructors and one of AI practitioners. The surveys were aimed at gathering infor-mation about the current state of the art of introducing AI as well as gathering input from practitioners in the field on techniques used in practice. In this paper, we present and briefly discuss the responses to those two surveys.

The FLAIRS Artificial Intelligence Education special track is devoted to methods of teaching AI. Its purpose is to provide a forum where AI educators from diverse institutional settings can share resources, innovations, and insights to advance the quality of AI education worldwide. Topics include model assignments, course syllabi, software, or other curricular resources, implementation of the Computing Curricula 2001 Intelligent Systems area, AI classroom techniques or innovations for undergraduate or graduate instruction, intelligent applications for instruction of AI and assessment of such applications, the use of robots or other hands-on equipment for teaching AI, strategies for incorporating AI research into AI courses, strategies for encouraging wider student interest and participation in AI, and descriptions or case studies of successful class projects or other pedagogical experiences.