Neural Information Processing Systems http://nips.cc/

Teaching forgetful learners requires repetition.

This paper explores integrating microlearning strategies into university curricula, particularly in computer science education, to counteract the decline in class attendance and engagement in US universities after COVID. As students increasingly opt for remote learning and recorded lectures, traditional educational approaches struggle to maintain engagement and effectiveness. Microlearning, which breaks complex subjects into manageable units, is proposed to address shorter attention spans and enhance educational outcomes. It uses interactive formats such as videos, quizzes, flashcards, and scenario-based exercises, which are especially beneficial for topics like algorithms and programming logic requiring deep understanding and ongoing practice. Adoption of microlearning is often limited by the effort needed to create such materials. This paper proposes leveraging AI tools, specifically ChatGPT, to reduce the workload for educators by automating the creation of supplementary materials. While AI can automate certain tasks, educators remain essential in guiding and shaping the learning process. This AI-enhanced approach ensures course content is kept current with the latest research and technology, with educators providing context and insights. By examining AI capabilities in microlearning, this study shows the potential to transform educational practices and outcomes in computer science, offering a practical model for combining advanced technology with established teaching methods.

Flashcard schedulers rely on 1) student models to predict the flashcards a student knows; and 2) teaching policies to pick which cards to show next via these predictions. Prior student models, however, just use study data like the student's past responses, ignoring the text on cards. We propose content-aware scheduling, the first schedulers exploiting flashcard content. To give the first evidence that such schedulers enhance student learning, we build KARL, a simple but effective content-aware student model employing deep knowledge tracing (DKT), retrieval, and BERT to predict student recall. We train KARL by collecting a new dataset of 123,143 study logs on diverse trivia questions. KARL bests existing student models in AUC and calibration error. To ensure our improved predictions lead to better student learning, we create a novel delta-based teaching policy to deploy KARL online. Based on 32 study paths from 27 users, KARL improves learning efficiency over SOTA, showing KARL's strength and encouraging researchers to look beyond historical study data to fully capture student abilities.

GUI testing checks if a software system behaves as expected when users interact with its graphical interface, e.g., testing specific functionality or validating relevant use case scenarios. Currently, deciding what to test at this high level is a manual task since automated GUI testing tools target lower level adequacy metrics such as structural code coverage or activity coverage. We propose DroidAgent, an autonomous GUI testing agent for Android, for semantic, intent-driven automation of GUI testing. It is based on Large Language Models and support mechanisms such as long- and short-term memory. Given an Android app, DroidAgent sets relevant task goals and subsequently tries to achieve them by interacting with the app. Our empirical evaluation of DroidAgent using 15 apps from the Themis benchmark shows that it can set up and perform realistic tasks, with a higher level of autonomy. For example, when testing a messaging app, DroidAgent created a second account and added a first account as a friend, testing a realistic use case, without human intervention. On average, DroidAgent achieved 61% activity coverage, compared to 51% for current state-of-the-art GUI testing techniques. Further, manual analysis shows that 317 out of the 374 autonomously created tasks are realistic and relevant to app functionalities, and also that DroidAgent interacts deeply with the apps and covers more features.

Hi, we are a two-student group that will be trying to create an ML model for their AIN311 course. This is the first of the many blog posts we will publish regarding this project. Stay tuned for a new post every Sunday. As everybody knows AI changes our lives for the better day by day. As two AI Engineering students, we thought we could kill two birds with one stone and create a project for our course which could help us study better while saving us time.

Data annotation is simply the process of labeling information so that machines can use it. It is especially useful for supervised machine learning (ML), where the system relies on labeled datasets to process, understand, and learn from input patterns to arrive at desired outputs. In ML, data annotation occurs before the information gets fed to a system. The process can be likened to using flashcards to teach children. A flashcard with the picture of an apple and the word "apple" would tell the children how an apple looks and how the word is spelled.

Nothing seemed suspect when Jill Watson, a teaching assistant at the Georgia Institute of Technology (Georgia Tech), emailed students about assignments and answered questions during Professor Ashok Goel's online knowledge-based artificial intelligence course. In fact, it wasn't until the end of the semester that the students realized they hadn't been emailing a human at all -- they'd been corresponding with a chatbot. Goel had built an artificially intelligent teaching assistant that could answer routine questions so that he and the human teaching assistants could focus on responding to more complex issues, Business Insider reports. And he isn't the only person using artificial intelligence to improve education. The teams at companies including Thinkster Math, Brainly, Content Technologies Inc., and Gradescope are creating artificial intelligence tools to aid students and educators.

Neural networks are used in machine learning algorithms to do the actual classification. Each layer has several neurons, and each of them processes a fragment of the input data, starting at the input layer, which splits apart the input data into chunks in an application-defined way. Each layer of the hidden layers processes each chunk to make an output which is eventually transmitted to the output layer. Actually, a neuron's computation is very simple. It takes a numerical input, multiplies it by a weight value, and then passes it as an output to a neuron at the next layer.

Try to learn Machine Learning breadth-first, not depth-first. Meaning, don't go too deep into a certain topic, because you'd get discouraged quickly.