Optimizing students' learning strategies is a crucial component in intelligent tutoring systems. Previous research has demonstrated the effectiveness of devising personalized learning strategies for students by modelling their learning processes through partially observable Markov decision process (POMDP). However, the research holds the assumption that the student population adheres to a uniform cognitive pattern. While this assumption simplifies the POMDP modelling process, it evidently deviates from a real-world scenario, thus reducing the precision of inducing individual students' learning strategies. In this article, we propose the homomorphic POMDP (H-POMDP) model to accommodate multiple cognitive patterns and present the parameter learning approach to automatically construct the H-POMDP model. Based on the H-POMDP model, we are able to represent different cognitive patterns from the data and induce more personalized learning strategies for individual students. We conduct experiments to show that, in comparison to the general POMDP approach, the H-POMDP model demonstrates better precision when modelling mixed data from multiple cognitive patterns. Moreover, the learning strategies derived from H-POMDPs exhibit better personalization in the performance evaluation.

Over the past few years, we have seen a massive advancement in technology. Every time a new tech is born, we see it getting applied to various industries. Artificial intelligence is the most popular technology of this decade, and it is helping in solving some of society's most challenging adversities and provides a safer, healthier and more prosperous world for all. We've already shared some of the exciting possibilities in the fields of Education, Businesses in our blog section. But there may be no field where the chances are more exciting than education and skills. In the last blog, we have seen how AI is helping in learning.

How are you feeling today? This is the question that a new generation of artificial intelligence is getting to grips with. Referred to as emotional AI, these technologies use a variety of advanced methods including computer vision, speech recognition and natural language processing to gauge human emotion and respond accordingly. Prof Alan Smeaton, lecturer and researcher in the school of computing, Dublin City University (DCU), and founding director of the Insight Centre for Data Analytics, is working on the application of computer vision to detect a very specific state: inattention. Necessity is the mother of invention and Help Me Watch was developed at DCU during the pandemic in response to student feedback on the challenges of online lectures.

Abstract: Recent work has explored how complementary strengths of humans and artificial intelligence (AI) systems might be productively combined. However, successful forms of human-AI partnership have rarely been demonstrated in real-world settings. We present the iterative design and evaluation of Lumilo, smart glasses that help teachers help their students in AI-supported classrooms by presenting real-time analytics about students' learning, metacognition, and behavior. Results from a field study conducted in K-12 classrooms indicate that students learn more when teachers and AI tutors work together during class. We discuss implications for the design of human-AI partnerships, arguing for participatory approaches to research in this area, and for principled approaches to studying human-AI decision-making in real-world contexts. Artificial intelligence (AI) systems are increasingly used to support human work in deeply social contexts such as education, healthcare, social work, and criminal justice. In these contexts, AI can automate routine parts of practitioners' work, while freeing up their time for activities they find more meaningful (17, 28, 39).

The original version of this article appeared in Toward Data Science. When I started teaching data science and artificial intelligence in Duke University's Pratt School of Engineering, I was frustrated by how little insight I actually felt I had into how effective my teaching was, until the end-of-semester final exam grades and student assessments came in. Being new to teaching, I spent time reading up on pedagogical best practices and how methods like mastery learning and one-on-one personalized guidance could drastically improve student outcomes. Yet even with my relatively small class sizes I did not feel I had enough insight into each individual student's learning to provide useful personalized guidance to them. In the middle of the semester, if you had asked me to tell you exactly what a specific student had mastered from the class to date and where he or she was struggling, I would not have been able to give you a very good answer.

In my last post, I talked about effective formative assessments and their powerful impact on student learning. In this post, let's explore why AI is well-suited for formative assessment. I think individualized feedback is the most powerful advantage of AI for assessment. As a teacher, I can only be in one place at a time looking in one direction at a time. That means I have two choices for feedback: I can take some time to assess how each student is doing and then address general learning barriers as a class, or I can assess and give feedback to students one at a time.

How would you feel if an algorithm determined where your child went to college? This year Covid-19 locked down millions of high school seniors and governments around the world canceled year-end graduation exams, forcing examining boards everywhere to consider other ways of setting the final grades that would largely determine the future of the class of 2020. One of these Boards, the International Baccalaureate Organization (IBO), opted for using artificial intelligence (AI) to help set overall scores for high-school graduates based on students' past work and other historic data. The experiment was not a success, and thousands of unhappy students and parents have since launched a furious protest campaign. So, what went wrong and what does the experience tell us about the challenges that come with AI-enabled solutions?

Model compression has emerged as an important area of research for deploying deep learning models on Internet-of-Things (IoT). However, for extremely memory-constrained scenarios, even the compressed models cannot fit within the memory of a single device and, as a result, must be distributed across multiple devices. This leads to a distributed inference paradigm in which memory and communication costs represent a major bottleneck. Yet, existing model compression techniques are not communication-aware. Therefore, we propose Network of Neural Networks (NoNN), a new distributed IoT learning paradigm that compresses a large pretrained 'teacher' deep network into several disjoint and highly-compressed 'student' modules, without loss of accuracy. Moreover, we propose a network science-based knowledge partitioning algorithm for the teacher model, and then train individual students on the resulting disjoint partitions. Extensive experimentation on five image classification datasets, for user-defined memory/performance budgets, show that NoNN achieves higher accuracy than several baselines and similar accuracy as the teacher model, while using minimal communication among students. Finally, as a case study, we deploy the proposed model for CIFAR-10 dataset on edge devices and demonstrate significant improvements in memory footprint (up to 24x), performance (up to 12x), and energy per node (up to 14x) compared to the large teacher model. We further show that for distributed inference on multiple edge devices, our proposed NoNN model results in up to 33x reduction in total latency w.r.t. a state-of-the-art model compression baseline.

Imagine a human-like teacher with no human flaws. The best educators in the world sometimes suffer from innate human errors, taking different forms in every one of us. They will eventually grow tired and nervous. Not even the best of them can provide personal attention to a class of 30. Computers never sleep; the knowledge they impart is available 24/7 across continents, time zones, and devices.

Education is one of the most powerful predictors of future success that human society has at its disposal. How we gather, process, and disseminate knowledge to each successive generation impacts not just individual success, but a host of other related factors such as economic growth, political empowerment, and technological innovation. It is no secret that access to more effective education for individual students is a key factor in the overall betterment of society – and to women's role in society. I've long been a proponent of better education for women – from my early career days working for CARE, to becoming the Chief Strategy Officer of Top Scholar, contributing to the book "Innovating Women" and to founding a non-profit to help the disadvantaged attain better education. Recently, I've been looking around globally for innovative solutions that can leapfrog women's education forward.