As generative AI becomes part of everyday writing, questions of transparency and productive human effort are increasingly important. Educators, reviewers, and readers want to understand how AI shaped the process. Where was human effort focused? What role did AI play in the creation of the work? How did the interaction unfold? Existing approaches often reduce these dynamics to summary metrics or simplified provenance. We introduce DraftMarks, an augmented reading tool that supports readers in interpreting how text was constructed with AI through familiar physical metaphors. DraftMarks employs skeuomorphic encodings such as eraser crumbs to convey the intensity of revision, and masking tape or smudges to mark AI-generated content, simulating the process within the final written artifact. By using data from writer-AI interactions, DraftMarks’ algorithm computes various collaboration metrics and writing traces. Through a formative study, we identified computational logic for different readership, and evaluated DraftMarks through a Prolific study for its effectiveness in assessing AI co-authored writing.

Live streaming platforms are rapidly emerging as popular forms of online entertainment. Yet many users report challenges, most notably a loss of autonomy in managing their spending and consumption. This erosion of autonomy is often reinforced by deceptive patterns embedded in platform design. In this paper, we examine how users experience and perceive autonomy on live streaming platforms. Through a systematic analysis of Douyin, a popular live streaming platform, we identified 27 deceptive patterns, complemented by 15 semi-structured interviews with platform users. Our findings reveal that users experience significant loss of autonomy, including compulsive overspending, disrupted daily routines, forced labor patterns, and increased anxiety, and expressed strong desires for autonomy-supportive alternatives. We provide insights into the design of future live streaming as autonomy-supportive installations, introducing an installation theory–derived framing that helps identify where autonomy breaks down and envision user-centered layered alternatives within entrenched platform infrastructures.

In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers, while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.

Motivating children to learn is a major challenge in education. One way to inspire motivation to learn is through immersion. We combine the immersive potential of augmented reality (AR), narrative, and large language models (LLMs) to bridge fantasy with reality in a mobile application, Moon Story, that teaches elementary schoolers astronomy and environmental science. Our system also builds upon learning theories such as culturally-relevant pedagogy. Using our application, a child embarks on a journey inspired by Chinese mythology, engages in real-world AR activities, and converses with a fictional character powered by an LLM. We conducted a controlled experiment (N = 50) with two conditions: one using an LLM and one that was hard-coded. Both conditions resulted in learning gains, high engagement levels, and increased science learning motivation. Participants in the LLM condition also wrote more relevant answers. Finally, participants of both Chinese and non-Chinese heritage found the culturally-based narrative compelling.

Large language models (LLMs) exhibit dynamic capabilities and appear to comprehend complex and ambiguous natural language prompts. However, calibrating LLM interactions is challenging for interface designers and end-users alike. A central issue is our limited grasp of how human cognitive processes begin with a goal and form intentions for executing actions, a blindspot even in established interaction models such as Norman’s gulfs of execution and evaluation. To address this gap, we theorize how end-users ‘envision’ translating their goals into clear intentions and craft prompts to obtain the desired LLM response. We define a process of Envisioning by highlighting three misalignments on not knowing: (1) what the task should be, (2) how to instruct the LLM to do the task, and (3) what to expect for the LLM’s output in meeting the goal. Finally, we make recommendations to narrow the gulf of envisioning in human-LLM interactions.

In this paper, we describe research conducted around a 7-week curriculum designed to introduce middle school students to computer science with a focus on algorithmic thinking and programming. The pedagogical ideas employed in this curriculum were drawn from past research. Empirical investigations over two studies in a public middle school in the US examined changes in students' understanding of algorithmic constructs and the factors affecting that learning. Multi-level analyses revealed that students in both studies (1) achieved substantial learning gains in algorithmic thinking skills and significant growth towards a more mature understanding of computing as a discipline, and (2) found certain CT ideas and constructs more difficult than others. Prior computing experiences and math and English ability were found to be predictors of learning outcomes. Extracurricular experiences with technology also appeared to impact outcomes.

In this study, we investigated the way users memorize, analyze, collaborate, and learn new concepts on a Tangible User Interface (TUI). Twenty-seven pairs of apprentices in logistics ( N = 54) interacted with an interactive simulation of a warehouse. Their task was to discover efficient design principles for building storehouses. In a between-subjects experimental design, half of the participants used 3D physical shelves, whereas the other half used 2D paper shelves. This manipulation allowed us to control for the “representational effect” of 3D tangibles: the first group saw the warehouse as a small-scale model with realistic shelves, whereas the second group had access to a more abstract layout with rectangular pieces of paper. Both groups interacted with the system in the same way. We found that participants in the first group (i.e., who used 3D realistic shelves) better memorized a warehouse layout, built a more efficient model, and scored higher on a learning test. Additionally, students wore eye-tracking goggles while completing those tasks; preliminary results suggest that 3D interfaces increased joint visual attention, which was found to be a significant predictor for participants’ task performance and learning gains. Implications for designing TUIs in collaborative settings are discussed.

As computing curricula continue to make their way into K-12 schools, the issue of assessing student learning of computational concepts remains a thorny one. This paper describes the multiple forms of assessments used in a 6-week middle school curriculum with the goal of capturing a holistic view of student learning. A key aspect of this research is the use of instruments developed and shared in prior research. Included among these were several questions used in an Israeli nationwide exam to test middle school student learning of programming in Scratch. This paper reports on the use of the curriculum in two studies conducted in a public US middle school classroom, and compares performances of these students with those reported by the Israeli Ministry of Education in their large-scale study. It also argues for multiple modes of assessment of computational learning in K-12 settings.

Lack of teachers to teach computer science (CS) and pedagogically sound introductory CS curricula remain a significant challenge facing secondary schools attempting to teach CS. This paper describes our efforts to design and pilot an online 6-week middle/high school course using Stanford's OpenEdX platform. The pedagogy, curriculum and assessment are guided by learning theory. The course leverages OpenEdX features for contextual discussions and multiple-choice assessments that promote student learning and provide feedback. The paper reports on experiences in using instructor dashboards to identify targets of student difficulty and to aid curriculum redesign.

Past research extensively points to gross misperceptions of the discipline of Computer Science among students in middle and high school. As efforts to introduce computing education in K-12 gains traction in tandem with initiatives that address issues of interest and attitudes towards CS, misperceptions of computing as a discipline must also be addressed as early as middle school, which is known to be a key time for identity building. This paper shares the results of a curricular intervention that aims to show CS to students in a new light - in real world contexts and as a creative and problem-solving discipline; as something bigger and broader than the "computer-centric" view that students are known to harbor.

In this paper we describe preliminary applications of network analysis techniques to eye-tracking data. In a previous study, the first author conducted a collaborative learning experiment in which subjects had access (or not) to a gaze-awareness tool: their task was to learn from neuroscience diagrams in a remote collaboration. In the treatment group, they could see the gaze of their partner displayed on the screen in real-time. In the control group, they could not. Dyads in the treatment group achieved a higher quality of collaboration and a higher learning gain. In this paper, we describe how network analysis techniques can further illuminate these results, and contribute to the development of 'collaboration sensing'. More specifically, we describe two contributions: first, one can use networks to visualize and explore eye-tracking data. Second, network metrics can be computed to interpret the properties of the graph. We conclude with comments on implementing this approach for formal learning environments.

The Educational Data Scientist is currently a poorly understood, rarely sighted breed. Reports vary: some are known to be largely nocturnal, solitary creatures, while others have been reported to display highly social behaviour in broad daylight. What are their primary habits? How do they see the world? What ecological niches do they occupy now, and will predicted seismic shifts transform the landscape in their favour? What survival skills do they need when running into other breeds? Will their numbers grow, and how might they evolve? In this panel, the conference will hear and debate not only broad perspectives on the terrain, but will have been exposed to some real life specimens, and caught glimpses of the future ecosystem.

Past research on children and programming from the 1980s called for deepening the study of the pedagogy of programming in order to help children build better cognitive models of foundational concepts of CS. More recently, computing education researchers are beginning to recognize the need to apply the learning sciences to develop age- and grade-appropriate curricula and pedagogies for developing computational competencies among children. This paper presents the curriculum of an exploratory workshop that employed a discourse-intensive pedagogy to introduce middle school children to programming and foundational concepts of computer science through programming mobile apps in App Inventor for Android (AIA).

We present the design and evaluation of a technique for influencing user respiration by integrating respiration-pacing methods into the desktop operating system in a peripheral manner. Peripheral paced respiration differs from prior techniques in that it does not require the user's full attention. We conducted a within-subjects study to evaluate the efficacy of peripheral paced respiration, as compared to no feedback, in an ecologically valid environment. Participant respiration decreased significantly in the pacing condition. Upon further analysis, we attribute this difference to a significant decrease in breath rate while the intermittent pacing feedback is active, rather than a persistent change in respiratory pattern. The results have implications for researchers in physiological computing, biofeedback designers, and human-computer interaction researchers concerned with user stress and affect.

Scientific practices increasingly incorporate sensors for data capture, information visualization for data analysis, and low-cost mobile devices for field-based inquiries incorporating open Web standards. While a broad range of design approaches for developing technology-enhanced learning has been used by researchers and practitioners for the last 15 years, significant challenges for educational use remain as new technologies and user experiences continually evolve outside the classroom. We focus on the specific design challenge of how to initiate the co-design process together with teachers, researchers, scientists, designers, and developers in order to devise and develop mobile science collaboratories that support open inquiry-based learning in ecology education. The outcomes presented in this paper point towards the need for additional methods to support co-design that take into consideration future user experiences needed for developing and implementing these types of learning activities.