Personalized learning has gained attention in English as a Foreign Language (EFL) education, where engagement and motivation play crucial roles in reading comprehension. We propose a novel approach to generating personalized English reading comprehension tests tailored to students' interests. We develop a structured content transcreation pipeline using OpenAI's gpt-4o, where we start with the RACE-C dataset, and generate new passages and multiple-choice reading comprehension questions that are linguistically similar to the original passages but semantically aligned with individual learners' interests. Our methodology integrates topic extraction, question classification based on Bloom's taxonomy, linguistic feature analysis, and content transcreation to enhance student engagement. We conduct a controlled experiment with EFL learners in South Korea to examine the impact of interest-aligned reading materials on comprehension and motivation. Our results show students learning with personalized reading passages demonstrate improved comprehension and motivation retention compared to those learning with non-personalized materials.

Across the U.S., more and more students are enrolling in introductory A.I. and machine learning classes, according to The A.I. Index 2019 Annual Report (PDF) produced by Stanford University. That's good news for students everywhere, because it means that more schools will inevitably begin offering this sort of coursework. It's also good for employers desperate for A.I. and machine learning specialists, because it means that pool of talent will likely expand over the next few years as these students enter the workforce. At Stanford itself, enrollment in the school's "Introduction to Artificial Intelligence" course has grown "fivefold" between 2012 and 2018, according to the report. That's not even the most rapid uptake: At the University of Illinois at Urbana-Champaign, an "Introduction to Machine Learning" course grew twelvefold between 2010 and 2018, with the largest part of that spike occurring after 2015.

Machine learning can potentially redefine not only how education is delivered, but also foster quality learning on the students' part. Probably the most important part of the role of machine learning in teaching is customized teaching. With machine learning, we are moving away from the one-size-fits-all methodology. Machine learning promises to deliver custom in-class teaching by providing real-time feedback based on individual student behavior and other factors. This improves the chances of better learning. Machine learning also plays an important role in assessments or evaluations by removing biases.