Introducing Multiagent Systems to Undergraduates through Games and Chocolate

The field of “intelligent agents and multi-agent systems” is maturing; no longer is it a special topic to be introduced to graduate students after years of training in computer science and many introductory courses in artificial intelligence. Instead, the time is ripe to introduce agents and multi-agents directly to undergraduate students, whether majoring in computer science or not. This chapter focuses on exactly this challenge, drawing on the co-authors’ experience of teaching several such undergraduate courses on agents and multi-agents, over the last three years at two different universities. The chapter outlines three key issues that must be addressed. The first issue is facilitating students’ intuitive understanding of fundamental concepts of multi-agent systems; the authors illustrate uses of science fiction materials and classroom games to not only provide students with the necessary intuitive understanding but with the excitement and motivation for studying multi-agent systems. The second is in selecting the right material — either science-fiction material or games — for providing students the necessary motivation and intuition; we outline several criteria that have been useful in selecting such material. The third issue is in educating students about the fundamental philosophical, ethical and social issues surrounding agents and multi-agent systems:they outline course materials and classroom activities that allow students to obtain this “big picture” futuristic vision of our science. The authors conclude with feedback received, lessons learned and impact on both the computer science students and non computer-science students.

An introductory artificial intelligence course may represent the first subject exposure for many students. Prior to taking this course, students may have formed perceptions regarding the difficulty of artificial intelligence from books, television, movies and other media featuring extrapolations of the technology. This paper analyzes what effects student perceptions of their ability to succeed in an introductory artificial intelligence course. It presents analysis of the association that exists between student characteristics and feelings of efficacy in an introductory undergraduate-level artificial intelligence course. This exploratory work demonstrates fifty-one statistically significant correlations between different characteristics and indications of efficacy or performance.

In a previous study, we investigated a framework for teaching abstraction in computer science (CS) in the context of an introductory CS course for 7th-grade students. The framework was found effective for developing CS abstraction skills. In this paper, we describe a study that investigated teaching abstraction in CS to 7th-grade students through the perspective of gender. Specifically, we compared the CS abstraction abilities of boys and girls taking the introductory CS course in 7th grade (using the Scratch environment). We also examined the difference in the effect of the teaching strategy on CS abstraction abilities between boys and girls. Our findings show a significant advantage of girls over boys regarding several aspects of CS abstraction. We also found that the framework for teaching CS abstraction, which was already established in our previous study as effective in the context of an introductory course for 7th-grade students, has an enhanced effect in this context when it comes to girls. This enhancement was manifested by a better overall performance by the girls, when comparing boys and girls that took an introductory CS course in which this framework was used. It was also manifested by the girls' deeper and more reliable perception of CS. We conjecture that this framework has the potential to affect girls' self-efficacy, and as a result to create a positive effect by encouraging and motivating girls to pursue CS education beyond this introductory course. That is, this framework has the potential to contribute to solving the issue of underrepresentation of females at the different stages of CS education, or in other words, to widening the CS pipeline for females.

The widespread application of artificial intelligence technology in various fields has made the sustainable development of artificial intelligence courses an important direction in the field of artificial intelligence education and teaching. Therefore, it is particularly important to conduct an in-depth analysis of the current research status of “artificial intelligence courses” from a global perspective. Firstly, this article clarifies the three stages of slow development, rapid development, and mature development of artificial intelligence curriculum research through the number and distribution years of the literature. It also conducts a co-authorship analysis on the distribution of countries, institutions, and authors of artificial intelligence curriculum research and identifies countries, institutions, and core authors that have made greater contributions to artificial intelligence curriculum research. Secondly, due to the involvement of artificial intelligence in many different fields of knowledge, an analysis is conducted on the journals that published papers on artificial intelligence courses. Finally, based on the analysis of keyword density and time span, the current research hotspots of artificial intelligence courses are summarized: artificial intelligence technology empowerment courses, two education directions at different stages of artificial intelligence courses, and teaching forms in the field of artificial intelligence courses. The current research trend of artificial intelligence courses is analyzed from three aspects: teaching format, teaching content, and teaching objects. This article provides a theoretical reference value and practical basis for future research and development in the field of artificial intelligence courses, while also providing experience for the efficient and sustainable development of artificial intelligence courses to a certain extent.

This paper describes our experiences using Active Learning in four first-year computer science and industrial engineering courses at the School of Engineering of the Universidad Católica de la Santísima Concepción, Chile. This work corresponds to the implementation stage of the curriculum reform using a CDIO-based approach currently underway at the School of Engineering. Before this reform, students had only one introductory course to their field of study, meeting one hour a week. After the reform, computer science students take an Introduction to Computer Science course and a Programming Laboratory. Similarly, the industrial engineering program now includes an Introduction to Industrial Engineering and an Engineering Communication course. These courses have been designed using CDIO standards 1, 4 and 8 as guidelines and have been formulated to include active learning in its many forms such as project-based learning, problem-based learning, case studies, small group discussions, oral presentations and reflective memos. The redesign impact was assessed via anonymous student surveys and reflective memos. Our results show improved student understanding of their professional endeavour and increased student motivation for their engineering programs. Student surveys registered high degrees of satisfaction with active learning techniques, working in teams, and receiving immediate feedback from instructors and peers.

This paper attempts to focus attention on the problem of providing meaningful and effective educational programs for precollege teachers. Computer science departments caught in their own staffing problems have not given much attention to precollege teacher training in computer science. Elementary and secondary schools are experiencing very little turnover in staff. Even when these schools have an open position, individuals entering the teaching field have little or no training in computer science. Yet the need for precollege teachers with a computer science background exists and is growing larger each year. This paper addresses this crisis in computer science education at the precollege level and proposes an approach which can be implemented easily and effectively.

A New Approach In Learning For Intelligent Multi Agent Systems

Most people who have taught the introductory undergraduate course in Artificial Intelligence agree that it is a difficult course to teach well. Recently attention has been directed to achieving a better understanding of the objectives, prerequisites, themes, pedagogic methodologies, topics, and support resources for this course [1]. Also, recently a number of professors who teach the course, acting independently have created and made available on the World Wide Web (WWW) simple Course Support Environments (CSEs) which include both pedagogic and administrative information.

The number of computer science (CS) courses has been dramatically expanding in U.S. high schools (HS). In comparison with well-established courses in mathematics and science, little is known about how the decisions made by HS CS teachers regarding how and what to teach impact student performance later in introductory college CS courses. Drawing on a large sample of 2,871 introductory college CS students at 115 U.S. institutions who had taken a CS course in HS, we examined the topic coverage and prevailing instructional methods in the HS course and investigated how these experiences influenced student performance in college CS. Controlling for differences in student background, we find two predictors of higher grades in college CS: greater frequency of coding-related activities in HS (programming, debugging, studying algorithms) and lower frequency of “non-coding” computer use (e.g., data analysis, computer security). Interaction models revealed a more complex story. Coding-related activity more heavily benefited students who did not have coding help available at home. In the 28% of college CS courses in which instructors employed innovative pedagogies, students with higher ACT or SAT mathematics scores had a greater advantage than in traditionally taught courses. Finally, in the innovative college courses, students whose HS CS exams had typically included testing on vocabulary did worse than students whose exams had not included such tests.

This research shows an audience of fellow computer science teachers that there is benefit in testing college student's scientific reasoning skills and that the students increase their logical thinking skills in computer science classes when they are presented with real world problems, engaged in group activities, and given hands-on experiments.Students' scientific reasoning skills from several different computer science courses are analyzed and tested before and after taking the computer science course to measure their improvement in logical thinking. The higher order thinking skills test results are compared, and the overall class and individual student improvement between the classes that use different teaching strategies are analyzed. Also, the test results are compared with students' ability to perform adequately in a computer science course. This information is valuable to advisors when placing students in computer science courses.In the assessments of students' entering knowledge and evaluations of students' thinking improvement, the students' individual higher order thinking skills as well as their overall level of cognitive development are analyzed. Through the analysis of their individual skills and level of thinking, one can expand conclusions about the students' and teacher's performance over time.

In the context of higher education strongly transforming itself under the impact of technology, Artificial Intelligence (AI) has become an important support tool in the learning process, especially for Computer Science (CS) students. Integrating a structured literature review and conceptual analysis, this paper evaluates the role of AI as a self-learning assistant, focusing on outstanding benefits such as personalized learning, source code debugging, and explaining complex algorithms, as well as challenges related to academic integrity, ethics, and independent thinking skills of students. Through analysis, the paper proposes a comprehensive recommendation framework, including: (1) an in-depth analysis of the five most effective AI tools; (2) smart strategies for AI-use through Prompt Engineering; (3) new pedagogical approaches for instructors; and (4) support policies from institutions. The results indicate that although AI cannot substitute human instructors completely, it can act as a powerful lever to enhance self-learning capabilities. When applied appropriately, it contributes to elevating the quality of Computer Science education in universities. Keywords: AI-assisted learning, Artificial Intelligence, Computer Science students, higher education, self-learning assistant.

Students enrolled in introductory computer science courses tend towards individual work because of pedagogical practices discouraging collaboration and a focus on individual assignments. This can discourage new computer science students and may negatively affect persistence in computer science. In contrast, social learning theory research suggests a connection between student success and their level of involvement with peers, instructors, and in the greater learning community. Motivated by these contrasting conclusions, the research presented in this paper puts forth a framework based on social learning theories and teaching and learning methodologies to leverage social computing as a learning tool. This framework’s primary objective is use as the foundation for the development and testing of tools used to stimulate social interaction in problem-solving environments for introductory programming students and, as a result, building stronger social networks within learning communities. Following the implications of social learning theory, this paper theorizes that use of tools using this framework will not only result in a stronger, more connected, social network but will also contribute to greater success in student learning outcomes. The research presented in this paper follows a mixed-methods approach with meta-analysis used to develop the framework and an iterative, user- and learner-centered design approach to develop the software tools. User-centered design is the base of our design approach, but learner-centered design helps maintain focus on the important questions we must keep in mind in the design and assessment of this tool: how can we make people more effective learners, adopt our tools (for students and instructors), and promote peer collaboration? How should learning opportunities be scaffolded in a social computing environment? How should we motivate learners to remain engaged and form connections? The framework presented in this paper emphasizes the following requirements for tools to stimulate social interaction in a learning environment. 1) Motivation generating techniques to increase user interaction with the software tools. 2) Scaffolded activities to structure and stimulate community engagement to encourage interaction with the tool and the community. 3) Focused social interaction and gamification features to foster learner success by encouraging competition and community building. Meeting these requirements should promote higher levels of interaction and may lead to improved learning outcomes, attitudes, and social connectedness. In essence, tools founded on this framework will be the focus of a social hub for learning activity and build on the social computing experience. The chosen platform and delivery for this universal tool, as presented in this research, is via a Visual Studio Code extension due to its wide adoption and cross-platform support. Tools built from this framework are based on the necessity of a universal tool focused on encouraging and building a social learning environment; one that supports collaboration and problem solving, provides support for instructor guidance and constraints, and are structured to support many problems new computer science students face in the classroom. Further, the framework developed in this paper sets the foundation for future research testing the efficacy of interventions built around a social computing hub where problem solving takes place.

The research focuses on methods and software tools for decision support for the organization of distributed production of unmanned aerial vehicles (UAVs) based on digital twin and multi-agent systems technologies. The purpose of the work is to provide a critical review of existing approaches to the organization of distributed production of UAVs, as complex technical objects, and to substantiate an approach to building a multi-agent decision support system for the synthesis of a rational production structure based on digital twins of participants in production cooperation. The tasks include: classifying unmanned aerial vehicles according to the main features that determine their production specifics; analysing modern scientific publications in the field of distributed production informatization, multi-agent systems, digital twins and intelligent decision support; identifying the limitations of existing solutions; and proposing an approach to the organization of distributed production of UAVs using digital twins in the form of intelligent agents. The applied methods are based on system and classification analysis, generalization of modern scientific approaches to the organization of production, decision-making theory, the concept of multi-agent systems, digital twin technologies, production logistics and artificial intelligence. The following results were obtained: UAVs were classified by mass, purpose and level of autonomy, which made it possible to determine the specifics of their production and life cycle management. An analysis of scientific publications was performed, which showed the active development of areas related to the use of multi-agent systems, digital twins and artificial intelligence to address complex tasks in UAV life cycle management. It was established, in particular, that the works of recent years consider the issues of cooperative UAV production, the use of deep learning algorithms for decision-making in dynamic environments and the integration of digital twins into relevant production systems. At the same time, a number of limitations on the implementation of existing solutions were identified: insufficient formalization of business processes between production participants, weak integration of digital twins into production logistics, and the lack of self-organization mechanisms. An approach to solving the problem of effective organization of distributed UAV production is proposed. The architecture of a multi-agent decision support system for distributed UAV production is developed, which includes modules for coordination, self-organization, and the evaluation of the effectiveness of production configuration options, considering the initial risk tolerance indicator. Conclusions. The scientific novelty of the results obtained is as follows: an approach to solving the problem of effective organization of distributed UAV production is proposed, which, unlike existing ones, is based on the representation of production participants as digital twins in the form of intelligent agents, this enables the generation of multiple options and the subsequent selection of the most rational configuration for organizing distributed UAV production in terms of business process efficiency and the reduction of production risks. The research results create a basis for further integration of multi-agent technologies into real-world manufacturing platforms and the expansion of their application to other types of complex machinery.

Various factors controlling health care in any country, but still there is a weakness in the infrastructure of health care systems in some developing countries, including many Arab countries, due to the shortage of medical specialists personnel, global economic crisis, and wars and disasters that struck many countries. A crying need for a tele-medical care becomes a necessity, and in order to overcoming this weakness we have proposed a multi-agent intelligent Telemedicine system in this research. The proposed system can help solve the shortage of specialists urgently and at lower costs. Furthermore, it can save the efforts of the patients' expensive travel for treatment abroad. Moreover, it can avoid infections from social closeness in times of disasters and pandemics.
 Nowadays Telemedicine systems have gradually become a popular medical model which have always attracted much attention due to the continuous development of the multi-agents' structure and capabilities of the artificial intelligence systems. So we will focus in the prototype design of our system on the algorithms, techniques and devices that support the multi-agents' structure and AI technologies. The Multi-agent approach happens to be a suitable structure for the development of Telemedicine systems. Worth mentioning it can solve problems that are difficult or impossible for an individual agent or a monolithic system, and the AI capabilities will minimize human errors and improve health care outcomes.
 We will start by a state-of-the-art literature survey as conducted on the E-health system architecture, AI capabilities, and multi-agent platform, and then we will illustrate prototype design of the suggested Multi-agent intelligent system for Telemedicine which will contribute in the development of the health care systems in the developing countries.
 Keywords: Healthcare system; Medical System; Artificial Intelligence; Multi-Agent, Telemedicine; Telehealth.

The evaluation of student projects is a difficult task, especially when they involve both a technical and a creative component. We propose an artificial intelligence (AI)-based methodology to help in the evaluation of complex projects in engineering and computer science courses. This methodology is intended to evaluate the assessment process itself allowing to analyze the influence of each variable in the final grade, to discover possible biases, inconsistencies and discrepancies, and to generate appropriate rubrics that help to avoid them. As an example of its application, we consider the evaluation of the projects submitted in an undergraduate introductory course on computer science. Using data collected from the evaluation during five academic years, we follow the proposed methodology to create AI models and analyze the main variables which are involved in the assessment of the projects. The proposed methodology can be applied to other courses and degrees, where both technical and creative components are considered to evaluate the projects.