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Demographic data pertain to people’s identities and behaviors. Analyses of demographic data are used to describe patterns and predict behaviors, to inform interface design, and even institutional decision-making processes. Demographic data thus need to be complete and correct to ensure they can be analyzed in ways that reflect reality. This study consists of interviews with 40 people in STEM and addresses how causes of relational (dis)trust in demographic data collection contribute to pervasive problems of missing and incorrect responses and disobliging responses (e.g., non-disclosure, false responses, attrition, and hesitancy to use services). The findings then guide a preliminary set of recommendations for cultivating trustworthiness based on recent developments in trust theory and designing for responsive and trustworthy systems. Specifically, we explore how demographic questionnaire design (e.g., item construction and instructions) can communicate necessary reassurances and transparency for users. The ongoing research provides interview-based recommendations for improving the quality and completeness of demographic data collection. This research adds to other recommendations on improving demographic questionnaires. 

This paper seeks to bring further attention to transgender experiences and issues in the context of software applications. Research on transgender experiences has generally been quite limited, and the majority of that work has focused on non-digital environments. However, we argue that transgender individuals encounter several unique human-computer interaction challenges that are not typically experienced (or perhaps even considered) by cisgender individuals. This paper first articulates general challenges for transgender persons, and then discusses specific manifestations in computing: name display and updating, demographic data forms, and automating gendered assumptions. Potential recommendations are offered. 

Researchers and educators have explored a variety of approaches for addressing diversity, equity, inclusion, and belonging (DEIB) challenges in engineering and design. This research builds on recommendations to teach future engineers and designers about DEIB principles and applications, and to challenge the dissociation of engineering and societal concerns. This paper analyses 25 student reflections from a course on Inclusive Engineering and Design to reveal engaging topics, perceived learning, and personal growth. Our conclusion is that such courses are meaningful and worthwhile contributions to the curriculum. 

The artificial intelligence in education (AIED) community has produced technologies that are widely used to support learning, teaching, assessment, and administration. This work has successfully enhanced test scores, course grades, skill acquisition, comprehension, engagement, and related outcomes. However, the prevailing approach to adaptive and personalized learning has two main steps. First, the process involves detecting the areas of knowledge and competencies where students are deficient. This process also identifies when or how a student is considered “at risk” or in some way “lacking.” Second, the approach involves providing timely, individualized assistance to address these deficiencies. However, a considerable body of research outside our field has established that such deficit framing, by itself, leads to reactive and less productive strategies. In deficit-based frameworks, powerful student strengths, skills, and schemas—their assets—are not explicitly leveraged... 

This panel will focus on the emerging area of Learning Engineering. Learning Engineering is a transdisciplinary area focusing on the systematic application of evidence-based principles from science of learning disciplines to create effective learning experiences, addressing the challenges of learners. During the panel, examples of Learning Engineering will be presented of interest to anyone within human factors and ergonomics with interest in education, training, or usability/design science. The panel will represent experience from both academia and industry. The goal of this panel is to foster dialog between the IEEE Industry Connections Industry Consortium on Learning Engineering (ICICLE) and HFES members in the hope of increasing knowledge of Learning Engineering and creating ties between the two organizations. 

Four "who-ristic" questions to ask yourself when designing artificially intelligent educational technologies that will actually benefit people. 

Human factors focus on taking the users’ capabilities, limitations, and environment into consideration when developing products. Thus, it is essential to have diverse perspectives and voices when designing products to be used by a variety of users. However, this is not always done and can be a missed opportunity in developing inclusive products. In this panel, we bring together researchers from different sectors to discuss challenges and strategies to engage a diverse research population at different stages in the product design process. Topics include research planning and the design process; data collection methods; and community- and participant-level recruitment. We hope that by sharing our experiences, we can prepare others to have the conversations needed that will allow them to successfully approach these topics. 

Literacy assessment is essential for effective literacy instruction and training. However, traditional paper-based literacy assessments are typically decontextualized and may cause stress and anxiety for test takers. In contrast, serious games and game environments allow for the assessment of literacy in more authentic and engaging ways, which has some potential to increase the assessment’s validity and reliability. The primary objective of this study is to examine the feasibility of a novel approach for stealthily assessing literacy skills using games in an intelligent tutoring system (ITS) designed for reading comprehension strategy training. We investigated the degree to which learners’ game performance and enjoyment predicted their scores on standardized reading tests. Amazon Mechanical Turk participants (n = 211) played three games in iSTART and self-reported their level of game enjoyment after each game. Participants also completed the Gates–MacGinitie Reading Test (GMRT), which includes vocabulary knowledge and reading comprehension measures. The results indicated that participants’ performance in each game as well as the combined performance across all three games predicted their literacy skills. However, the relations between game enjoyment and literacy skills varied across games. These findings suggest the potential of leveraging serious games to assess students’ literacy skills and improve the adaptivity of game-based learning environments. 

Human needs and goals are integral to engineering, but human-centered engineering requires understanding principles and methods from the social sciences. Instruction is needed to introduce engineering students to these concepts. This study explored (a) students’ initial attitudes toward human systems engineering (HSE) and the bridging of psychology and engineering and (b) how attitudes shifted over one semester. Undergraduate students responded to a survey of their attitudes and conceptions at the beginning (n = 215) and end (n = 146) of a semester. Students reported high perceived value for human-centered engineering, and these attitudes remained stable or increased. Students gained in their perceived understanding of psychological concepts and user-centered design, their ability to define HSE, and ability to articulate the value of human-centered engineering. Introductory coursework in Human Systems Engineering may thus help to prepare engineering students to appreciate the psychological underpinnings of their work and engage in human-centered engineering.