Search for: All records

Seeking insight into AI decision-making processes to better address bias and improve accountability in AI systems. 

Abstract As generative artificial intelligence (AI) becomes increasingly integrated into society and education, more institutions are implementing AI usage policies and offering introductory AI courses. These courses, however, should not replicate the technical focus typically found in introductory computer science (CS) courses like CS1 and CS2. In this paper, we use an adjustable, interdisciplinary socio‐technical AI literacy framework to design and present an introductory AI literacy course. We present a refined version of this framework informed by the teaching of a 1‐credit general education AI literacy course (primarily for freshmen and first‐year students from various majors), a 3‐credit course for CS majors at all levels, and a summer camp for high school students. Drawing from these teaching experiences and the evolving research landscape, we propose an introductory AI literacy course design framework structured around four cross‐cutting pillars. These pillars encompass (1) understanding the scope and technical dimensions of AI technologies, (2) learning how to interact with (generative) AI technologies, (3) applying principles of critical, ethical, and responsible AI usage, and (4) analyzing implications of AI on society. We posit that achieving AI literacy is essential for all students, those pursuing AI‐related careers, and those following other educational or professional paths. This introductory course, positioned at the beginning of a program, creates a foundation for ongoing and advanced AI education. The course design approach is presented as a series of modules and subtopics under each pillar. We emphasize the importance of thoughtful instructional design, including pedagogy, expected learning outcomes, and assessment strategies. This approach not only integrates social and technical learning but also democratizes AI education across diverse student populations and equips all learners with the socio‐technical, multidisciplinary perspectives necessary to navigate and shape the ethical future of AI. 

The field of Artificial Intelligence (AI) is rapidly advancing, with significant potential to transform society. However, it faces a notable challenge: lack of diversity, a longstanding issue in STEM fields. In this context, this position paper examines the intersection of AI and identity as a pathway to understanding biases, inequalities, and ethical considerations in AI development and deployment. We present a multifaceted definition of AI identity, which encompasses its creators, applications, and their broader impacts. Understanding AI's identity involves analyzing the diverse individuals involved in AI's development, the technologies produced, and the social, ethical, and psychological implications. After exploring the AI identity ecosystem and its societal dynamics, We propose a framework that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity. This paper presents a research framework for examining the implications and changes needed to foster a more inclusive and responsible AI ecosystem through the lens of identity. 

AI is rapidly emerging as a tool that can be used by everyone, increasing its impact on our lives, society, and the economy. There is a need to develop educational programs and curricula that can increase capacity and diversity in AI as well as awareness of the implications of using AI-driven technologies. This paper reports on a workshop whose goals include developing guidelines for ensuring that we expand the diversity of people engaged in AI while expanding the capacity for AI curricula with a scope of content that will reflectthe competencies and needs of the workforce. The scope for AI education included K-Gray and considered AI knowledge and competencies as well as AI literacy (including responsible use and ethical issues). Participants discussed recommendations for metrics measuring capacity and diversity as well as strategies for increasing capacity and diversity at different level of education: K-12, undergraduate and graduate Computer Science (CS) majors and non-CS majors, the workforce, and the public. 

This research-to-practice paper presents a curriculum, “AI Literacy for All,” to promote an interdisciplinary under-standing of AI, its socio-technical implications, and its practical applications for all levels of education. With the rapid evolution of artificial intelligence (AI), there is a need for AI literacy that goes beyond the traditional AI education curriculum. AI literacy has been conceptualized in various ways, including public literacy, competency building for designers, conceptual understanding of AI concepts, and domain-specific upskilling. Most of these conceptualizations were established before the public release of Generative AI (Gen-AI) tools such as ChatGPT. AI education has focused on the principles and applications of AI through a technical lens that emphasizes the mastery of AI principles, the mathematical foundations underlying these technologies, and the programming and mathematical skills necessary to implement AI solutions. The non-technical component of AI literacy has often been limited to social and ethical implications, privacy and security issues, or the experience of interacting with AI. In AI Literacy for all, we emphasize a balanced curriculum that includes technical as well as non-technical learning outcomes to enable a conceptual understanding and critical evaluation of AI technologies in an interdisciplinary socio-technical context. The paper presents four pillars of AI literacy: understanding the scope and technical dimensions of AI, learning how to interact with Gen-AI in an informed and responsible way, the socio-technical issues of ethical and responsible AI, and the social and future implications of AI. While it is important to include all learning outcomes for AI education in a Computer Science major, the learning outcomes can be adjusted for other learning contexts, including, non-CS majors, high school summer camps, the adult workforce, and the public. This paper advocates for a shift in AI literacy education to offer a more interdisciplinary socio-technical approach as a pathway to broaden participation in AI. This approach not only broadens students' perspectives but also prepares them to think critically about integrating AI into their future professional and personal lives. 

Serendipitous recommendations have emerged as a compelling approach to deliver users with unexpected yet valuable information, contributing to heightened user satisfaction and engagement. This survey presents an investigation of the most recent research in serendipity recommenders, with a specific emphasis on deep learning recommendation models. We categorize these models into three types, distinguishing their integration of the serendipity objective across distinct stages: pre-processing, in-processing, and post-processing. Additionally, we provide a review and summary of the serendipity definition, available ground truth datasets, and evaluation experiments employed in the field. We propose three promising avenues for future exploration: (1) leveraging user reviews to identify and explore serendipity, (2) employing reinforcement learning to construct a model for discerning appropriate timing for serendipitous recommendations, and (3) utilizing cross-domain learning to enhance serendipitous recommendations. With this review, we aim to cultivate a deeper understanding of serendipity in recommender systems and inspire further advancements in this domain. 

This full paper presents the Collaborative Active Learning and Inclusiveness (CALI) inventory, and an analytical model using the CALI inventory, demographic data, mindset surveys, and knowledge mastery assessment, to explore relationships between classroom climate and student experiences. The CALI inventory enables the investigation of the impact of the student experience in an active learning classroom by distinguishing the factors that characterize the structure, social learning, and inclusive practices. The Structure Index includes components related to course setup, organization, assessment, grading, and communications. The Sociality Index includes components related to opportunities for students to interact with each other. The Inclusiveness Index includes components related to how the instructor communicates a sense of belonging to the students through a growth mindset and inclusive policies and practices. A CS Mindset Instrument was developed based on research that measured students' self-efficacy by evaluating the extent of variation in their self-perceived ability to accomplish a task, sense of belonging in computing, and professional identity development. Demographic data is collected that allows for an analysis using an intersectional lens to acknowledge the complexity of social and cultural contexts. The knowledge and mastery assessments capture changes in competency through pre-post mastery quizzes. The combination of CALI with other instruments, including those that characterize student mindset, identity, and levels of mastery, enables investigation of how various practices of inclusive and collaborative active learning have differential effects on students with different identities in computer science. 

This work-in-progress paper presents a study that sheds light on the concerns that students may not develop sufficient programming skills and as a result, be less competent with the use of ChatGPT. The potential benefits for students are significant: Access to ChatGPT increases the ability for students to work constructively on their own schedule. The ease of use of ChatGPT may engage students who might otherwise hesitate in asking for support. Before these tools can be meaningfully introduced into a course, work must be done to study the impact of these AI tools on a student's ability to learn. In this study, participants are recruited from introductory Java programming courses at a large public university in the United States. This paper presents preliminary findings from a mixed method study design that consists of a pre-task assessment quiz; and a programming task in one of three conditions: (1) with no external help, (2) with the help of an AI chatbot, or (3) with the help of a generative AI tool like GitHub Copilot; followed by a post-task assessment and an interview on their experience and perceptions of the tools. Our preliminary findings describe our data collection, thematic analysis of the students' prompts and chatGPT responses, and a summary of the experience for 3 students. Our findings demonstrate a range of students' attitudes and behaviors towards chatGPT that provides insight for future research and plans for incorporating such AI tools in a course.