AI is increasingly framed as a solution to societal challenges, particularly educational inequities. Yet, AI systems often reproduce the very exclusionary structures they claim to address. Despite this, there is limited research on empowering minoritized youth to shape AI in ways that align with their communities’ needs. This systemic issue is especially significant for Child–Computer Interaction research, which must examine how young people critically engage with AI technologies. Drawing on data science projects, surveys, and semi-structured interviews, we document how youth develop anti-AI solutionist perspectives alongside data science practices, and how they re-imagine more justice-oriented roles for AI. Our primary contribution shows how our critical AI fluency curricula enable youth to determine when AI should be adopted or refused because they understand racial inequities as sociotechnical rather than technological problems. We position this capacity for AI refusal as essential for redistributing power under algorithmic governance.

Children regularly negotiate questions of authority and control in home and school life, but little is known about how they believe robots should fit into these dynamics. We conducted a 75-minute design session with 17 children (ages 6-9) to examine when robots should take, share, or defer control, and how expectations shift when robots are framed as teachers, classmates, or mentees. Children resisted robot control, particularly in adult-regulated domains and areas tied to personal skill or self-expression. They were more open to robot control in domains where they felt less competent, or where robots, perceived as less legitimate authorities than humans, could substitute for adult control. Role framing further shaped expectations: teacher robots were granted autonomy, classmate robots were expected to act as peers, and mentee robots were expected to defer. These findings show that children apply context- and role-sensitive rules when negotiating control with robots. We conclude with design considerations for robots in children's everyday lives that respect children's agency, calibrate autonomy by domain, and align behavior with children's context-sensitive expectations.

How do people react to a robot that lies to them? Current robot deception work only explores the effects of deception within the same context that the deception occurs. However, how does deception in one scenario influence how a robot will be predicted to act in a different situation? This work presents a large-scale (N=1296), online, text-based scenario experiment that described an agent tasked with helping with a business deal. We examine how factors of agent embodiment, agent truthfulness, and the outcomes of the agent’s decisions influence people’s trust in the system and how likely participants are to predict that the agent will act maliciously in another scenario. This work finds that when an agent lies to a participant, even when the participant benefits from this deception, this decreases trust and results in an increased prediction that the agent will act with malicious intent in a different scenario. Our results also show that for our scenario, participants evaluate a human more harshly than they do a robot or an AI system without an embodiment. These results add further nuance to evaluating the advantages and disadvantages of designing systems that deceive and, hopefully, encourage more investigations into an otherwise understudied area.

This work presents an empirical study of repeated robot deception and its effects on changes in behavior and trust in a human-robot interaction scenario. 715 online and 50 in-person participants completed a multitrial driving simulation in which the car's robot assistant repeatedly lies and apologizes. Through a mixed-method approach, our results show that apologies that offer justifications for deception in our scenario mitigate the negative effects on trust over multiple trials. However, given the time-sensitive, high-risk nature of our scenario, none of the apologies caused people to significantly change their decision to exceed the speed limit while rushing their dying friend to the hospital. These results add much needed knowledge to the understudied area of robot deception and could inform designers and policymakers of future practices when considering deploying robots that may learn to deceive.

If a robot tells you it can lie for your benefit, how would that change how you perceive it? This paper presents a mixed-methods empirical study that investigates how disclosure of deceptive or honest capabilities influences the perceived social intelligence and construal level of a robot. We first conduct a study with 198 Mechanical Turk participants, and then a replication of it with 15 undergraduate students in order to gain qualitative data. Our results show that how a robot introduces itself can have noticeable effects on how it is perceived–even from just one exposure. In particular, when revealing having ability to lie when it believes it is in the best interest of a human, people noticeably find the robot to be less trustworthy than a robot that conceals any honesty aspects or reveals total truthfulness. Moreover, robots that are forthcoming with their truthful abilities are seen in a lower construal than one that is transparent about its deceptive abilities. These results add much needed knowledge to the understudied area of robot deception and could inform designers and policy makers of future practices when considering deploying robots that deceive.

Online education is an important tool for supporting the growing number of teachers and students in computer science. We created two eBooks containing interactive content for Advanced Placement Computer Science Principles, one targeted at teachers and one at students. By comparing the eBook usage patterns of these populations, including activity usage counts, transitions between activities, and pathways through the eBook, we develop a characterization of how student use of the eBook differs from teacher use. We offer design recommendations for how eBooks might be developed to target each of our populations. We ground our recommendations in a theory of teachers as expert learners who possess a greater ability to regulate their own learning process.

Several countries are trying to provide access to computing education for all secondary students. However, there are not enough teachers who are prepared to teach computer science. Interactive electronic books (ebooks) are a promising approach for providing low-cost professional development in computer science. Over the last four years, our research group has been conducting design-based research by iteratively developing and testing versions of a teacher ebook to help secondary teachers with no programming experience learn to teach an introductory programming course. The interactive elements in the ebook were designed based on research results from educational psychology and are intended to make learning more efficient and effective. Our goals for this effort are to increase teachers' knowledge of computer science concepts and to improve teachers' confidence in their ability to teach computer science. In this paper we summarize our previous work and report on a large-scale study of version two of the teacher ebook. We also recommend several design principles for interactive ebooks for computing teachers based on feedback from teachers, log file analyses, and randomized controlled studies.