Rina Azoulay

The integration of large language models (LLMs) into educational settings represents a significant technological breakthrough, offering substantial opportunities alongside profound ethical challenges. Higher education institutions face the widespread use of these tools by students, requiring them to navigate complex decisions regarding their adoption. This includes determining whether to allow the use of LLMs, defining their appropriate scope, and establishing guidelines for their responsible and ethical application. In the context of computer science education, these challenges are particularly acute. On the one hand, the capabilities of LLMs significantly enhance the tools available to developers and software engineers. On the other hand, students’ over-reliance on LLMs risks hindering their development of foundational skills. This study examines these challenges and proposes strategies to regulate the use of LLMs while upholding academic integrity. It focuses on the specific impact of LLMs in programming education, where dependence on AI-generated solutions may erode active learning and essential skill acquisition. Through a comprehensive literature review and drawing on teaching experience and guidelines from global institutions, this study contributes to the broader discourse on the integration of these advanced technologies into educational environments. The goal is to enhance learning outcomes while ensuring the development of competent, ethical software professionals.

Power control and scheduling are among the most well-known resource allocation challenges in wireless networks, and are often solved as optimization problems with constraints. However, solving these optimization challenges by using optimal algorithms often incurs a significant time complexity, which creates considerable discrepancies between the theoretical results and real-time processing required. In this study, we propose a novel machine learning–based perspective to address this issue. We propose a scheduling and power control deep neural network SPCDNet method and its modification SPCDNet^R . SPCDNet solves the scheduling problem for point-to-point transmission requests while SPCDNet^R solves the more complex problem, where the input transmission list is composed of ordered routes which should be satisfied. Both SPCDNet and SPCDNet^R are trained in a supervised manner and show near-optimal performance on the test set. Our results demonstrate that SPCDNet and SPCDNet^R can serve as a computationally inexpensive solution (regarding time complexity), compared with state-of-the-art schemes, while showing to be near-optimal approximation solutions to the time scheduling and power control challenges. Moreover, we found that both SPCDNet and SPCDNet^R reach efficient solutions for large problem instances, even though they were trained on small problems.

Children with special needs may struggle to identify uncomfortable and unsafe situations. In this study, we aimed at developing an automated system that can detect such situations based on audio and text cues to encourage children’s safety and prevent situations of violence toward them. We composed a text and audio database with over 1891 sentences extracted from videos presenting real-world situations, and categorized them into three classes: neutral sentences, insulting sentences, and sentences indicating unsafe conditions. We compared insulting and unsafe sentence-detection abilities of various machine-learning methods. In particular, we found that a deep neural network that accepts the text embedding vectors of bidirectional encoder representations from transformers (BERT) and audio embedding vectors of Wav2Vec as input attains the highest accuracy in detecting unsafe and insulting situations. Our results indicate that it may be applicable to build an automated agent that can detect unsafe and unpleasant situations that children with special needs may encounter, given the dialogue contexts conducted with these children.

In recent years, conversational agents (CAs) have become ubiquitous and are a presence in our daily routines. It seems that the technology has finally ripened to advance the use of CAs in various domains, including commercial, healthcare, educational, political, industrial, and personal domains. In this study, the main areas in which CAs are successful are described along with the main technologies that enable the creation of CAs. Capable of conducting ongoing communication with humans, CAs are encountered in natural-language processing, deep learning, and technologies that integrate emotional aspects. The technologies used for the evaluation of CAs and publicly available datasets are outlined. In addition, several areas for future research are identified to address moral and security issues, given the current state of CA-related technological developments. The uniqueness of our review is that an overview of the concepts and building blocks of CAs is provided, and CAs are categorized according to their abilities and main application domains. In addition, the primary tools and datasets that may be useful for the development and evaluation of CAs of different categories are described. Finally, some thoughts and directions for future research are provided, and domains that may benefit from conversational agents are introduced.

The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost. UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation. It may be useful to group the UAVs into clusters/flocks in certain domains, and various challenges associated with UAV usage can be alleviated by clustering. Several computational challenges arise in UAV flock management, which can be solved by using machine learning (ML) methods. In this survey, we describe the basic terms relating to UAVS and modern ML methods, and we provide an overview of related tutorials and surveys. We subsequently consider the different challenges that appear in UAV flocks. For each issue, we survey several machine learning-based methods that have been suggested in the literature to handle the associated challenges. Thereafter, we describe various open issues in which ML can be applied to solve the different challenges of flocks, and we suggest means of using ML methods for this purpose. This comprehensive review may be useful for both researchers and developers in providing a wide view of various aspects of state-of-the-art ML technologies that are applicable to flock management.

One of the challenges of an intelligent tutoring system (ITS) is adapting the difficulty level of the questions posed to the student to suit the student’s academic level. Our study examines the task of adjusting the system’s level of challenges to the level of the learner and addresses the questions of how best to do so and whether there is any benefit from such adjustment. To answer these questions, we developed reading comprehension courseware that includes three adaptive algorithms for adjusting the level of the questions presented to the students: the random selection algorithm, the Q-learning based algorithm, and the Bayesian inference algorithm. We conduct a real-world experiment in which real high school students used the courseware to improve their reading comprehension skills. In order to compare and evaluate the performance of the algorithms, the courseware used by each student utilized one of the three adaptive algorithm alternatives. Our results demonstrate that when considering all of the students, there was significant improvement (learning gain) using each of the methods.

In this paper we concentrate on ad exchange mechanisms that take into consideration the publishers’ preferences concerning the attributes of ads published in their ad space, in addition to their desire to earn money from the ads. We suggest allocation (ad placement) and pricing protocols which take into account preferences of both the publishers and the advertisers. The most promising protocol, the Weighted Bipartite Hungarian VCG protocol, collects the preferences of the auction’s participants and uses the Hungarian algorithm to maximize a weighted function of their preferences. Simulations show that this advantageous protocol can maintain a balanced budget over time while preserving most of the desired economic properties (e.g., individual rational, and truth telling), and reaches near optimal solutions.

There are special markets where not all buyers are symmetric from the seller's perspective and similarly, there are cases where not all sellers are symmetric from the buyer's perspective. For example when a person attempts to acquire some information it most definitely matters who the information provider/seller is. The higher the reputation an information provider has the more valuable his information is from the advertisers' perspective. The main challenges in such scenarios are the ability to (i) elicit true information from the participants, and (ii) find the most efficient allocation. The VCG could have been a good mechanism for this purpose, however, it is not budget balanced, making it impractical. In this paper we propose the weighted bilateral VCG mechanism which comprises most of the desired economic properties for being strategy proof, and individually rational. Moreover, our mechanism has been shown to be (i) budget balanced for the long term, (ii) does not add complexity overhead to the optimization problem complexity, (iii) may be tuned by the auctioneer using the weight parameter to decide about the level of profit it decides on, and (iv) produces suboptimal allocations which are very close to the optimal ones.