Cybersecurity demands creativity, persistence, and sharp pattern recognition—strengths frequently reported among neurodivergent people (e.g., autism, ADHD, dyslexia). Yet AI-driven hiring pipelines can systematically disadvantage neurodivergent applicants by misreading communication styles or valuing narrow proxies of ''fit.'' Demand for cybersecurity talent is growing, and experts note that neurodiverse individuals are both underrepresented and highly valuable to security teams. However, progress remains uneven without targeted educational interventions [1]. We present a curricular module that simultaneously (a) centers neurodiversity as a strength in the cybersecurity workforce and (b) trains students to audit and redesign AI hiring systems using open-source fairness and explainability toolkits (AIF360 and SHAP). Students run end-to-end labs, evaluate trade-offs between performance and equity, and propose inclusive pipeline redesigns aligned with emerging policy guidance on AI and disability [7].

Although Artificial Intelligence has become an integral part of modern cybersecurity solutions, data bias and algorithmic bias have made it vulnerable to many cyberattacks, in particular, adversarial attacks where the attacker crafts input of the AI system to exploit the existence of possible bias in the data or algorithms. In this paper, we share our experiences with ongoing work to develop and evaluate a cybersecurity curricular module that demonstrates (a) data bias detection, (b) data bias mitigation, (c) algorithmic bias detection, and (d) algorithmic bias mitigation, using a network intrusion detection problem on real-world data. The module includes lectures and hands-on exercises, using state-of-the-art and open-source bias detection and mitigation software on a real-world dataset. The goal is to identify and mitigate the prevailing conscious/unconscious bias in data and algorithms that the attacker might exploit.

Data bias and algorithmic bias are the primary contributing factors for fairness-related risks in AI-based decision-making. The concept of fairness is comparatively new and is sometimes only discussed in detail in graduate-level courses such as Ethics of Artificial Intelligence, and Ethics and Governance of Artificial Intelligence. In addition, at the undergraduate level, a standalone course on fairness is not feasible due to the level of difficulty and the many other essential courses that need to be broached in a university's computer science curriculum. Therefore, instead of a standalone course, we have created a concise, high-level concept module on bias and fairness in automated decisions, with (1) lecture material, (2) demonstrations, and (3) assignments (i.e., exercises) on real-world datasets.

We present a curricular model for introducing data analytics concepts into an introductory computer science course for non-majors. This is accomplished through the design and implementation of hands-on laboratories projects using the Python programming language and associated tools. While introducing students to an important research area, we believe the use of these projects improves students' learning experiences, enabling them to apply and relate fundamental computational thinking concepts of algorithmic reasoning, data representation, and computational efficiency to data analytics problems. We present the curricular modules, as well as preliminary experiences using them.

We present a model for enhancing an introductory computer science course for non-majors through the use of the Arduino platform. We have developed and tested curricular modules and associated hands-on laboratories for this model. The use of the highly visual and interactive Arduino system has improved students' learning experiences, enabling them to apply and relate fundamental computational thinking concepts of algorithmic reasoning, data representation, and computational efficiency to real-world problems. Assessment results show that the approach has been effective. We present the curricular modules, our experiences using them, as well as assessment results.

Embedded systems pervade nearly every aspect of modern life. Moreover, the emergence of both mobile platforms and Internet of Things (IoT) is furthering their reach. Although embedded systems are one of the bodies of knowledge in the ACM/IEEE-CS Com- puter Engineering Curricula, they have only passing mention in the ACM/IEEE-CS Computer Science Curricula. Inclusion of embedded systems concepts in undergraduate computer science can facilitate many objectives: a) they are an example of Platform-Based Devel- opment, a prominent theme in the ACM 2013 CS Curricula, b) they are often a more suitable level of complexity for educational needs than other "real world" platforms (e.g., Arduinos may be used to introduce many AP CS Principles in a single course), c) they offer a novel form of engagement, which may enhance diversity, and d) emerging areas, like IoT, are increasing demand for professionals that understand the full span of systems, from low-level firmware, to middleware and cloud computing. This panel represents three methods of including embedded systems concepts in undergraduate computer science: 1) use of em- bedded systems to improve engagement in a non-major computing course, 2) a required course covering core content for both com- puter science and computer engineering majors, and 3) a degree program offering a formal emphasis in embedded systems via a complementary set of courses. The panelists will share their motiva- tions for including embedded systems concepts in their programs, their approaches to integrating the content into their curricula, the teaching methods they use, the challenges they faced, and chal- lenges that remain.

As the Internet of Things (IoT) continues its expansion into homes, businesses and industries, the impact for Computer Science educators is increasingly evident. In 2017, the ITiCSE IoT working group identified relevant content, tools for teaching, and four IoT course types. The resulting report provided an entry point for educators challenged with setting up a new IoT course. The 2018 working group will build on this prior work by addressing feedback on the 2017 report and by examining the rapidly changing state-of-the-art in IoT. In particular, the working group will extend educators' ability to integrate IoT into curriculum by investigating additional content areas and pedagogical approaches.

Smart devices are everywhere, and the Internet of Things (IoT) revolution is only in its infancy. In the Internet of Things, everyday objects share data over networks, with or without human intervention. Self-driving cars, sensing thermostats, door locks, pet feeders, light bulbs, wearables of all kinds, and smart materials for manufacturing all belong to the new Internet of Things, applying sensors and cloud computing to allow for object-to-object communication. As computer science educators, we will soon be teaching students how to develop and maintain IoT technologies. This presents enormous challenges and even greater opportunities. How will we integrate IoT concepts and technologies into existing curricula? How will we handle the mix of software and hardware topics that most IoT projects involve? How will we deal with the legal, social, and ethical issues? How will we choose from the growing number of IoT industry standards? What kinds of equipment and lab spaces are optimal for small, medium, and large-scale programs, and how will we budget for all this? What are the opportunities for interdisciplinary studies? How will we leverage the enthusiasm students feel when they create projects that go beyond text, beyond graphics, beyond virtual reality, and into the tactile, three-dimensional, realm of moving real-world objects? In this working group, we study and document the current state of IoT education and interview educators with IoT teaching experience. We will then make recommendations to help educators integrate IoT topics in computer science curricula.

The workshop introduces participants to Weka, an open source Data Mining software package written in Java and available from www.cs.waikato.ac.nz/~ml/weka/. The goal of the workshop is to present the basic functionality of Weka that may be used in the undergraduate computer science and engineering curricula. The Weka system provides a rich set of powerful Machine Learning algorithms for Data Mining tasks, along with a comprehensive set of tools for data pre-processing, statistics and visualization, all available through an easy to use graphical user interface. Weka is widely used for educational purposes. Recently, with the increasing popularity of Big Data, it becomes a popular tool for Analytics and Data Science. Weka's rich functionality also allows its use for Text and Web document pre-processing and mining. All this makes it a suitable platform for enhancing the CS curriculum with hands-on exercises and practical projects. The workshop will present examples of such projects and exercises in the area of Web document classification and clustering. The basic steps of document collection, creating the vector space model, data preprocessing, attribute selection, and applying classification and clustering algorithms will be discussed. These topics will be covered in a way that will allow participants with no particular background in machine learning or data mining to appreciate the use of Weka in computer science education.

We present preliminary experiences in designing a Computer Science Principles undergraduate course for all majors that is based on physical computing with the Arduino microprocessor platform. The course goal is to introduce students to fundamental computing concepts in the context of developing concrete products. This physical computing approach is different from other existing CS Principles courses. Students use the Arduino platform to design tangible interactive systems that are personally and socially relevant to them, while learning computing concepts and reflecting on their experiences. In a previous publication [1], we reported on assessment results of using the Arduino platform in an Introduction to Digital Design course. We have introduced this platform in an introductory computing course at the University of Hartford in the past year as well as in a Systems Fundamentals Discovery Course at the University of New Hampshire to satisfy the general education requirements in the Environment, Technology, and Society category. Our goal is to align the current curriculum with the CS Principles framework to design a course that engages a broader audience through a creative making and contextualized learning experience.

We present preliminary experiences using the Arduino microprocessor platform in the undergraduate computing curricula, at both the upper and lower levels. The goal is to enhance student learning by engaging them in a contextualized project-based learning experience and introducing them to fundamental computing and engineering concepts in the context of a highly visual and easy to use environment.

The goal of Project MLeXAI, Machine Learning Experiences in Artificial Intelligence, is to develop a project-based framework for teaching core AI topics with a unifying theme of machine learning. In this paper, we provide an overview of Project MLeXAI and the curricular material being developed. We present experiences during the second phase of the project that involves its implementation at several diverse institutions involving twenty instructors.

Our approach to teaching introductory artificial intelligence (AI) unifies its diverse core topics through a theme of machine learning, and emphasizes how AI relates more broadly with computer science. Our work, funded by a grant from the National Science Foundation, involves the development, implementation, and testing of a suite of projects that can be closely integrated into a one-term AI course. Each project involves the development of a machine learning system in a specific application. These projects have been used in six different offerings over a three-year period at three different types of institutions. While we have presented a sample of the projects as well as limited preliminary experiences in other venues, this article presents the first assessment of our work over an extended period of three years. Results of assessment show that the projects were well received by the students. By using projects involving real-world applications we provided additional motivation for students. While illustrating core concepts, the projects introduced students to an important area in computer science, machine learning, thus motivating further study.

This is a proposal for a half day tutorial on Weka, an open source Data Mining software package written in Java and available from www.cs.waikato.ac.nz/~ml/weka/index.html. The goal of the tutorial is to introduce faculty to the package and to the pedagogical possibilities for its use in the undergraduate computer science and engineering curricula. The Weka system provides a rich set of powerful Machine Learning algorithms for Data Mining tasks, some not found in commercial data mining systems. These include basic statistics and visualization tools, as well as tools for pre-processing, classification, and clustering, all available through an easy to use graphical user interface.

An introductory Artificial Intelligence (AI) course provides students with basic knowledge of the theory and practice of AI as a discipline concerned with the methodology and technology for solving problems that are difficult to solve by other means. It is generally recognized that an introductory Artificial Intelligence course is challenging to teach. This is, in part, due to the diverse and seemingly disconnected core AI topics that are typically covered. Recently, work has been done to address the diversity of topics covered in the course and to create a theme-based approach. Russell and Norvig present an agent-centered approach [9]. Others have been working to integrate Robotics into the AI course [1, 2, 3].We present work on a project funded by the National Science Foundation with a goal of unifying the artificial intelligence course around the theme of machine learning. This involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Machine learning is inherently connected with the AI core topics and provides methodology and technology to enhance real-world applications within many of these topics. Machine learning also provides a bridge between AI technology and modern software engineering. In his article, Mitchell discusses the increasingly important role that machine learning plays in the software world and identifies three important areas: data mining, difficult-to-program applications, and customized software applications [6].We have developed a suite of adaptable, hands-on laboratory projects that can be closely integrated into the introductory AI course. Each project involves the design and implementation of a learning system which will enhance a particular commonly-deployed application. The goal is to enhance the student learning experience in the introductory artificial intelligence course by (1) introducing machine learning elements into the AI course, (2) implementing a set of unifying machine learning laboratory projects to tie together the core AI topics, and (3) developing, applying, and testing an adaptable framework for the presentation of core AI topics which emphasizes the important relationship between AI and computer science in general, and software development in particular. Details on this project as well as samples of course materials developed are published in [4, 5, 7, 8] and are available at the project website at http://uhaweb.hartford.edu/compsci/ccli.We present an overview of our work along with a detailed presentation of one of these projects and how it meets our goals.The project involves the development of a learning system for web document classification. Students investigate the process of classifying hypertext documents, called tagging, and apply machine learning techniques and data mining tools for automatic tagging. Our experiences using the projects are also presented.

No abstract available.

In this article, we describe some of the important currently used methods for solving classification problems, focusing on feature selection and extraction as parts of the overall classification task. We then go on to discuss likely future directions for research in this area, in the context of the other articles from this special issue. We propose that the next major step is the elaboration of a theory of how the methods of selection and extraction interact during the classification process for particular problem domains, along with any learning that may be part of the algorithms. Preferably this theory should be tested on a set of well-established benchmark challenge problems. Using this theory, we will be better able to identify the specific combinations that will achieve best classification performance for new tasks.