AI-driven educational technologies (AI-EdTech) process extensive data, raising concerns about commercial exploitation of children’s data and risks to their privacy, wellbeing, agency, and legal rights. The ‘fairness principle’ in data protection law requires fair data processing that meets children’s expectations and avoids unexpected, detrimental, discriminatory, or misleading practices. However, children’s own perspectives on what fairness means in AI-EdTech are underexplored in design. This study bridges the gap between law and design research to contextualize what fairness means through co-design workshops with 72 children (aged 10–12) and 4 teachers (N=76) in Scotland and Türkiye. We examine how children's perspectives can inform the operationalization of ‘fairness by design’ for AI-EdTech. Our contributions include: (1) an understanding of children’s perspectives on how fairness manifests (or does not) in AI-EdTech and (2) recommendations for both design and legal communities to align AI-EdTech design and data practices with children's values and rights.
https://dl.acm.org/doi/10.1145/3706598.3714402
Multi-role pedagogical agents can create engaging and immersive learning experiences, helping learners better understand knowledge in history learning. However, existing pedagogical agents often struggle with multi-role interactions due to complex controls, limited feedback forms, and difficulty dynamically adapting to user inputs. In this study, we developed a VR prototype with LLM-powered adaptive role-switching and action-switching pedagogical agents to help users learn about the history of the Pavilion of Prince Teng. A 2 x 2 between-subjects study was conducted with 84 participants to assess how adaptive role-switching and action-switching affect participants’ learning outcomes and experiences. The results suggest that adaptive role-switching enhances participants’ perception of the pedagogical agent’s trustworthiness and expertise but may lead to inconsistent learning experiences. Adaptive action-switching increases participants’ perceived social presence, expertise, and humanness. The study did not uncover any effects of role-switching and action-switching on usability, learning motivation and cognitive load. Based on the findings, we proposed five design implications for incorporating adaptive role-switching and action-switching into future VR history education tools.
https://dl.acm.org/doi/10.1145/3706598.3713109
Education technologies (edtech) are increasingly incorporating new features built on large language models (LLMs), with the goals of enriching the processes of teaching and learning and ultimately improving learning outcomes. However, the potential downstream impacts of LLM-based edtech remain understudied. Prior attempts to map the risks of LLMs have not been tailored to education specifically, even though it is a unique domain in many respects: from its population (students are often children, who can be especially impacted by technology) to its goals (providing the correct answer may be less important for learners than understanding how to arrive at an answer) to its implications for higher-order skills that generalize across contexts (e.g., critical thinking and collaboration). We conducted semi-structured interviews with six edtech providers representing leaders in the K-12 space, as well as a diverse group of 23 educators with varying levels of experience with LLM-based edtech. Through a thematic analysis, we explored how each group is anticipating, observing, and accounting for potential harms from LLMs in education. We find that, while edtech providers focus primarily on mitigating technical harms, i.e., those that can be measured based solely on LLM outputs themselves, educators are more concerned about harms that result from the broader impacts of LLMs, i.e., those that require observation of interactions between students, educators, school systems, and edtech to measure. Overall, we (1) develop an education-specific overview of potential harms from LLMs, (2) highlight gaps between conceptions of harm by edtech providers and those by educators, and (3) make recommendations to facilitate the centering of educators in the design and development of edtech tools.
https://dl.acm.org/doi/10.1145/3706598.3713210
The interdisciplinary field of Human-Computer Interaction (HCI) thrives on productive engagement with different domains, yet this engagement often breaks due to idiosyncratic writing styles and unfamiliar concepts. Inspired by the dialogic model of abstract metaphors, as well as the potential of Large Language Models (LLMs) to produce on-demand support, we investigate the use of metaphors to facilitate engagement between Science and Technology Studies (STS) and System HCI. Our reflective-style survey with early-career HCI researchers (N=48) reported that limited prior exposure to STS research can hinder perceived openness of the work, and ultimately interest in reading. The survey also revealed that metaphors enhance likelihood to continue reading STS papers, and alternative perspectives can build critical thinking skills to mitigate potential risks of LLM-generated metaphors. We lastly offer a specified model of metaphor exchange (within this generative context) that incorporates alternative perspectives to construct shared understanding in interdisciplinary engagement.
https://dl.acm.org/doi/10.1145/3706598.3713698
While AI's potential in education and professional sports is widely recognized, its application in K-12 physical education (PE) remains underexplored with significant opportunities for innovation. This study aims to address this gap by engaging 17 in-service secondary school PE teachers in group ideation workshops to explore potential AI applications and challenges in PE classes. Participants envisioned AI playing multidimensional roles, such as an operational assistant, personal trainer, group coach, and evaluator, as solutions to address unique instructional and operational challenges in K-12 PE classes. These roles reflected participants’ perspectives on how AI could enhance class management, deliver personalized feedback, promote balanced team activities, and streamline performance assessments. Participants also highlighted critical considerations for AI integration, including the need to ensure robust student data security and privacy measures, minimize the risk of over-reliance on AI for instructional decisions, and accommodate the varying levels of technological proficiency among PE teachers. Our findings provide valuable insights and practical guidance for AI developers, educators, and policymakers, offering a foundation for the effective integration of AI into K-12 PE curricula to enhance teaching practices and student outcomes.
https://dl.acm.org/doi/10.1145/3706598.3713646
Online education — given the enhanced access for diverse populations and flexible participation — has been a topic of interest for many computer science and learning science researchers. The sudden shift to online settings during the COVID-19 Emergency Remote Teaching (ERT) provided a valuable opportunity to examine the use of educational technologies on a global scale with various digital readiness skills, beyond many past works that relied on small lab studies. Following a PRISMA-inspired methodology grounded on Moore’s three types of classroom interaction, this descriptive review investigates 22 empirical research papers published during the COVID-19 ERT era focused on higher-education online classrooms. We explore the empirical evidence reported in the collected corpus, and given how ERT remains a likely future occurrence, we suggest key directions for future research, including a new learning paradigm that centralizes and augments Learner-Content interaction to balance between flexibility and structure of online learning.
https://dl.acm.org/doi/10.1145/3706598.3713995