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The physical world evolves. The cyber world evolves and grows with big data, with social media as a major component of information growth. Classic ML models are limited by their static training data with implicit Complete and Timeless Knowledge assumptions. In an evolving world, static training data suffer from knowledge obsolescence due to truly novel timely information. Knowledge obsolescence introduces a widening distance between static ML models and the evolving world, called cyber-physical gap. Periodic retraining of new models may restore their accuracy temporarily, but subsequently their performance will deteriorate with widening cyber-physical gap. Knowledge obsolescence affects statically trained models of any size, including LLMs. Two major research challenges arise from cyber-physical gap: (1) collection and incorporation of space-time aware ground truth training data, and (2) understanding and capturing of the varying speed of information and knowledge evolution when the physical and cyber worlds evolve. 

The University of Alabama is exploring Learning in Advance (LIA) courses to introduce engineering students to concepts and correct common misconceptions prior to encountering the complex theories and concepts in three different gateway courses. These gateway courses are circuit analysis, statics, and data-structures/algorithms. The courses were identified based on analysis of institutional data. Data indicated that greater than 90% of UA students who succeed in the three courses went on to complete their undergraduate degree. Yet, each course has individually high rates of failure and/or withdrawals. The objective and intended learning outcomes of each of the three courses is to provide students with knowledge of key concepts that will strengthen the student’s critical thinking skills and establish a strong technical foundation. In this work an overview of the LIA courses is provided along with summaries of collected student feedback and the plans for future assessment to track the effectiveness of this intervention to improve student outcomes in the gateway courses.