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Inclusive design appears rarely, if at all, in most undergraduate computer science (CS) curricula. As a result, many CS students graduate without knowing how to apply inclusive design to the software they build, and go on to careers that perpetuate the proliferation of software that excludes communities of users. Our panel of CS faculty will explain how we have been working to address this problem. For the past several years, we have been integrating bits of inclusive design in multiple courses in CS undergraduate programs, which has had very positive impacts on students' ratings of their instructors, students' ratings of the education climate, and students' retention. The panel's content will be mostly concrete examples of how we are doing this so that attendees can leave with an in-the-trenches understanding of what this looks like for CS faculty across specialization areas and classes. We 

Motivations:Recent research has emerged on generally how to improve AI products’ Human-AI Interaction (HAI) User Experience (UX), but relatively little is known about HAI-UX inclusivity. For example, what kinds of users are supported, and who are left out? What product changes would make it more inclusive? Objectives:To help fill this gap, we present an approach to measuring what kinds of diverse users an AI product leaves out and how to act upon that knowledge. To bring actionability to the results, the approach focuses on users’ problem-solving diversity. Thus, our specific objectives were: (1) to show how the measure can reveal which participants with diverse problem-solving styles were left behind in a set of AI products; and (2) to relate participants’ problem-solving diversity to their demographic diversity, specifically gender and age. Methods:We performed 18 experiments, discarding two that failed manipulation checks. Each experiment was a 2x2 factorial experiment with online participants, comparing two AI products: one deliberately violating one of 18 HAI guideline and the other applying the same guideline. For our first objective, we used our measure to analyze how much each AI product gained/lost HAI-UX inclusivity compared to its counterpart, where inclusivity meant supportiveness to participants with particular problem-solving styles. For our second objective, we analyzed how participants’ problem-solving styles aligned with their gender identities and ages. Results & Implications:Participants’ diverse problem-solving styles revealed six types of inclusivity results: (1) the AI products that followed an HAI guideline were almost always more inclusive across diversity of problem-solving styles than the products that did not follow that guideline—but “who” got most of the inclusivity varied widely by guideline and by problem-solving style; (2) when an AI product had risk implications, four variables’ values varied in tandem: participants’ feelings of control, their (lack of) suspicion, their trust in the product, and their certainty while using the product; (3) the more control an AI product offered users, the more inclusive it was; (4) whether an AI product was learning from “my” data or other people’s affected how inclusive that product was; (5) participants’ problem-solving styles skewed differently by gender and age group; and (6) almost all of the results suggested actions that HAI practitioners could take to improve their products’ inclusivity further. Together, these results suggest that a key to improving the demographic inclusivity of an AI product (e.g., across a wide range of genders, ages, etc.) can often be obtained by improving the product’s support of diverse problem-solving styles. 

Computer science (CS) and information technology (IT) curricula are grounded in theoretical and technical skills. Topics like equity and inclusive design are rarely found in mainstream student studies. This results in graduates with outdated practices and limitations in software development. A research project was conducted to educate the faculty to integrate inclusive software design into the CS undergraduate curriculum. The objective is to produce graduates with the ability to develop inclusive software. This experience report presents the results of teaching inclusive design throughout the four-year CS and IT curriculum, focusing on the impact on faculty. This easy-to-adopt, high-impact approach improved student retention and classroom climate, broadening participation. Research questions address faculty understanding of inclusive software design, the approach's feasibility, improvement in students’ ability to design equitable software, and assessment of the inclusiveness culture for students in computing programs. Faculty attended a summer workshop to learn about inclusive design and update their teaching materials to include the GenderMag method. Beginning in CS0 and CS1 and continuing through Senior Capstone, faculty used updated course assignments to include inclusive design in 10 courses for 44 sections taught. Faculty outcomes are positive, with the planning to include inclusive design and working with other department faculty most engaging. Faculty were impressed by student ownership and adoption of inclusive design methods, particularly in the culminating capstone senior project. 

What if “regular” CS faculty each taught elements of inclusive design in “regular” CS courses across an undergraduate curriculum? Would it affect the CS program's climate and inclusiveness to diverse students? Would it improve retention? Would students learn less CS? Would they actually learn any inclusive design? To answer these questions, we conducted a year-long Action Research investigation, in which 13 CS faculty integrated elements of inclusive design into 44 CS/IT offerings across a 4-year curriculum. The 613 affected students’ educational work products, grades, and/or climate questionnaire responses revealed significant improvements in students’ course outcomes (higher course grades and fewer course fails/incompletes/withdrawals), especially for marginalized groups; revealed that most students did learn and apply inclusive design concepts to their CS activities; and revealed that inclusion and teamwork in the courses significantly improved. These results suggest a new pathway for significantly improving students’ retention, their knowledge and usage of inclusive design, and their experiences across CS education—for marginalized groups and for all students.