Artificial intelligence (AI) technologies are increasingly adopted into everyday life, with most investment and development concentrated in the U.S. In response to rapid AI integration and scant federal guidelines, U.S. states have formed AI committees charged with studying AI-related societal trade-offs. We analyzed the 18 existing state-level AI committee reports to understand how policymakers discuss AI-related benefits and risks. We then compared the risks surfaced by policymakers to an established taxonomy of AI risks aggregated from literature and examined how policymakers’ concerns align—or misalign—from those of HCI scholars. These insights provide important mileposts for shaping currently ongoing policy initiatives and future research. Our findings reveal important gaps: while committees invoke responsible AI, their framings often omit broader socio-technical concerns emphasized in HCI. We discuss opportunities for HCI to support socio-technical perspectives, employ participatory design, and close the gap between research and policy.

This paper describes the instructor and student experience of a ''just-in-time'', blended approach to prerequisite review, implemented in a machine learning course but applicable elsewhere. Although pre-requisites are commonly used to structure university curricula, both the literature and our experience show that students sometimes forget prerequisite knowledge when it is needed in subsequent courses. This challenge is especially pronounced in courses where diverse prerequisite concepts are applied throughout the semester, with different concepts required for different units. Our approach consisted of short prerequisite review quizzes due before each lecture, where the quiz questions assessed mastery of key prerequisite concepts needed for that lecture. Moreover, each quiz was accompanied by a brief instructional video that provided a targeted review of the content. We evaluated this approach across two course implementations, based on perspectives from 2 instructors and 353 students. Both instructors and students felt positively: a reduction in prerequisite related questions during lectures was observed, and students reported that the approach helped bridge gaps in preparedness, improved self-efficacy, and was efficient. More interestingly, the responses showed key tradeoffs regarding the timing, modality, and level of support in a prerequisite review intervention. While we believe this approach to be applicable for other courses with diverse requirements, our results lead us to believe that there is no one-size-fits-all for prerequisite review, and that it is highly context-dependent.

Machine learning (ML) has become increasingly important for students, yet university-level ML courses are often perceived as challenging and time-intensive. This study explores the perceived challenges and motivations of students in a university ML course to inform curricular and teaching strategies. Through 5 surveys conducted in two instances of a 12-week introductory ML course, we examined students' engagement with both theoretical and practical aspects of ML. Results indicate that while students initially express strong interest in applying ML concepts, their reported interests can shift toward theoretical foundations. Challenges in both theory and practice are reported, including difficulties in mathematical notation and vectorization of gradient components, as well as model implementation. Students also discuss the time commitment required in a course with both theoretical and practical content. We recommend aligning course content with student motivations, providing targeted support for mathematical notation and vectorization, and balancing theoretical depth with practical application.

One of the less explored approaches to foster equity, diversity, and inclusion (EDI) in Computer Science (CS) is through changes to the curriculum. Despite sporadic work on the adoption of Culturally Responsive Computing (CRC) and Universal Design for Learning (UDL), the inclusion of equity-minded courses, or modifications on specific elements of the curriculum such as introductory programming courses, there has never been a wide exploration or adoption of a successful equity-minded undergraduate CS curriculum.

Students have experienced incredible shifts in the in their learning environments, brought about by the response of universities to the ever-changing public health mandates driven by waves and stages of the coronavirus pandemic (COVID-19). Initially, these shifts in learning (mode of course delivery, course availability, etc.) were considered emergency responses. However, as the pandemic presses on, students have had to repeatedly adapt to the continuously evolving educational landscape as this global health crisis forced an "unprecedented global shift within higher education in the ways that we communicate with and educate students". This working group builds upon foundations and structure created by a 2021 ITICSE Working Group exploring the effects of COVID-19 on teaching and learning from a faculty perspective. That Working Group identified the incorporation of some pandemic-induced changes into future teaching practices. In this Working Group, we explore existing literature regarding the student experience in response to the evolving teaching practices catalyzed by COVID-19). Traditionally, computing is a subject full of experiential learning opportunities, rich with in-person labs and exercises. We explore how the changes within the COVID-affected academic landscape have altered that student experience. The current group of computing students will have had experiences under both typical (i.e. pre-pandemic) and COVID-affected teaching practices. It is, therefore, timely that we understand how each has impacted how they perceive their learning environment and educational experience. In turn, identifying those practices that have most benefited the student learning experience will help computing faculty improve their practices going forward.

Background and Objectives: This study aims to assist rapid accurate diagnosis of COVID-19 based on chest x-ray (CXR) images to provide supplementary information, leading to screening program for early detection of COVID-19 based on CXR images by developing an interpretable, robust and performant AI system. Methods: A case-based reasoning approach built upon autoencoder deep learning architecture is applied to classify COVID-19 from other non-COVID-19 as well as normal subjects from chest x-ray images. The system integrates the interpretation and decision-making together by producing a set of profiles that in appearance resemble the training samples and hence explain the outcome of classifications. Three classes are studied, which are COVID-19 (n=250), other non-COVID-19 diseases (NCD) (n=384), including TB and ARDS, and normal (n=327). Results: This COVID-CBR system sustains the average sensitivity and specificity of 93.1±3.58% and 96.1±4.10% respectively for classification of these three classes. In comparison with the current state of the art, including COVID-Net, VGG-16 and other explainable AI systems, the developed COVID-CBR system appears to perform similar or better when classifying multi-class categories. Conclusion: This paper presents a case-based reasoning deep learning system for detection of COVID19 from chest x-ray images. Comparison with several state of the art systems is conducted. Although the improvement tends to be marginal, especially for VGG-16, the novelty of this work manifests its interpretable feature building upon case-based reasoning, leading to revealing this viral insight and hence ascertaining more effective treatment and drugs while maintaining being transparent. Furthermore, different from several other current explainable networks that highlight key regions or the points of an input that activate the network, i.e. heat maps, this work is constructed upon whole training images, i.e. case-based, whereby each training image belongs to one of the case clusters.