AI literacy research has had great success in offering competencies that capture the knowledge and skills users and developers of AI need to have for a world full of AI, helping them maximize its benefits and minimize its harms. However, recent years have witnessed other roles beyond users and developers whose responsibilities have been complicated by AI. In this work, we apply a service design approach to identify such roles and their responsibilities across various AI applications. By mapping the responsibilities to current AI literacy competencies, we exposed gaps suggesting unmet learning needs in current AI literacy research: identifying and assessing AI benefits, strategizing about AI’s benefits and risks, and monitoring and refining deployed AI to understand their changing impact. We discuss implications for future AI literacy research and its connection to Responsible AI research.
Low socioeconomic populations face severe security challenges while being unable to access traditional written advice resources. We present the first study to explore the security advice landscape of low socioeconomic people in Pakistan. With 20 semi-structured interviews, we uncover how they learn and share security advice and what factors enable or limit their advice sharing. Our findings highlight that they heavily rely on community advice and intermediation to establish and maintain security-related practices (such as passwords). We uncover how shifting social environments shape advice dissemination, e.g., across different workplaces. Participants leverage their social structures to protect each other against threats that exploit their financial vulnerability and lack of digital literacy. However, we uncover barriers to social advice mechanisms, limiting their effectiveness, which may lead to increased security and privacy risks. Our results lay the foundation for rethinking security paradigms and advice for this vulnerable population.
This research explores whether the rapid digital transformation due to COVID-19 managed to close or exacerbate the digital divide concerning users’ digital skills. We conducted a pre-registered survey with N = 1,143 German Internet users. Our findings suggest the latter: younger, male, and higher educated users were more likely to improve their digital skills than older, female, and less educated ones. According to their accounts, the pandemic helped Internet users improve their skills in communicating with others by using video conference software and reflecting critically upon information they found online. These improved digital skills exacerbated not only positive (e.g., feeling informed and safe) but also negative (e.g., feeling lonely) effects of digital media use during the pandemic. We discuss this research's theoretical and practical implications regarding the impact of challenges, such as technological disruption and health crises, on humans’ digital skills, capabilities, and future potential, focusing on the second-level digital divide.
How do individuals perceive AI systems as responsible entities in everyday collaborations between humans and AI? Drawing on psychological literature from attribution theory, praise-blame asymmetries and negativity bias, this study investigated the effects of perspective (actor vs observer) and outcome favorability (positive vs negative) on how participants (N=321) attributed responsibility for outcomes resulting from shared human-AI decision-making. Both Bayesian modelling and reflexive thematic analysis of results revealed that, overall, participants were more likely to attribute greater responsibility to the AI systems. When the outcome was positive, participants were more likely to ascribe shared responsibility to both Human and AI systems, rather than either separately. When the outcome was negative, participants were more likely to attribute responsibility to a single entity, but not consistently towards the human or the AI. These results build on the understanding of how individuals cast blame and praise for shared interactions involving AI systems.
Building technological literacy is an important topic in education today while at the same time, creativity is seen as a desirable skill for professional practice and education alike as it is considered a catalyst for innovation. In this paper, we present a case study where we aim to understand how students in a creative education appropriate technology. We analyze qualitative data collected from students in a STEAM higher education undergraduate program called Art&Technology: a program where students are introduced to an assortment of technological tools including software, programming, digital fabrication and physical prototyping which they employ in creating a variety of artifacts. We analyze how students learn and interact with these technologies by analyzing the collected data through a phenomenological lens of technology appropriation. We contribute with understandings on how technology is appropriated as it transforms from an object to a tool until it finally becomes equipment.
Despite recognizing that Large Language Models (LLMs) can generate inaccurate or unacceptable responses, universities are increasingly making such models available to their students. Existing university policies defer the responsibility of checking for correctness and appropriateness of LLM responses to students and assume that they will have the required knowledge and skills to do so on their own. In this work, we conducted a series of user studies with students (N=47) from a large North American public research university to understand if and how they critically engage with LLMs. Our participants evaluated an LLM provided by the university in a quasi-experimental setup; first by themselves, and then with a scaffolded design probe that guided them through an end-user auditing exercise. Qualitative analysis of participant think-aloud and LLM interaction data showed that students without basic AI literacy skills struggle to conceptualize and evaluate LLM biases on their own. However, they transition to focused thinking and purposeful interactions when provided with structured guidance. We highlight areas where current university policies may fall short and offer policy and design recommendations to better support students.
As Artificial Intelligence (AI) continues to influence various aspects of society, the need for AI literacy education for K-12 students has grown. An increasing number of AI literacy studies aim to enhance students' competencies in understanding, using, and critically evaluating AI systems. However, despite the vulnerabilities faced by students from underserved communities—due to factors such as socioeconomic status, gender, and race—these students remain underrepresented in existing research. To address this gap, this study focuses on leveraging the cultural capital that students acquire from their communities’ unique history and culture for AI literacy education. Education researchers have demonstrated that identifying and mobilizing cultural capital is an effective strategy for educating these populations. Through collaboration with 26 students from underserved communities—including those who are socioeconomically disadvantaged, female, or people of color—this paper identifies three types of cultural capital relevant to AI literacy education: 1) resistant capital, 2) communal capital, and 3) creative capital. The study also emphasizes that collaborative relationships between researchers and students are crucial for mobilizing cultural capital in AI literacy education research.