Tomohiro Nagashima

Conrad Borchers, Lijin Zhang, Kexin Yang et al.

Adaptive learning systems can produce substantial learning gains, yet many students engage for too brief or too superficial a period to benefit. A central obstacle is measuring effort. Effort during multi-step problem solving is rarely directly observed, and common log-based proxies, such as time on task, cannot distinguish between a student working carefully and a student encountering a harder problem. We examine step-to-step response time as a scalable effort signal by modeling trait-like differences in students' typical response timing during tutoring (while adjusting for skill difficulty). Using step-level logs from eight classroom deployments of algebra tutoring systems (2020 to 2023) across six U.S. schools (794 students), we estimate student- and knowledge-component-level propensities using hierarchical models and relate them to learning efficiency, defined as performance improvement per completed solution step. Response-time propensities show moderate to strong stability within students, supporting their use as an individual differences measure beyond correctness. At the same time, their relationship to learning is not uniform but conditional on the learner and context. Slower propensities predict greater learning efficiency for higher-proficiency students, consistent with constructive processing, whereas for lower-proficiency students, slower propensities are weakly related or even negative, consistent with unproductive struggle or idling. These associations are strongest early in practice sequences and attenuate later in the class period, highlighting an actionable window for detecting emerging disengagement and low persistence. Overall, response-time propensities provide a practical way to incorporate temporal process data into learner models and to target adaptive supports when effort is most diagnostic.

Tomohiro Nagashima, Mirella Hladký, Vera Rief

Recent work in Technology-Enhanced Learning and Human-Computer Interaction highlights the importance of transparency and trust calibration in AI-supported learning environments as they pose a risk of hallucinations. In this study, we investigate whether a simple transparency intervention that warns students that a pedagogical agent may make mistakes affects learner behavior in a math intelligent tutoring system. We conducted a classroom experiment with 252 school students using two system versions: one including a warning message about potential system errors, and one that does not mention potential errors. Using log data, we analyzed students' problem-solving performance data, including help-seeking behavior, error rate, and time-on-task. Results show that students who were warned about potential AI errors requested significantly more hints than those in the other condition, even though the actual system behavior was exactly the same. This finding suggests that lightweight transparency interventions can influence learners' interaction strategies without necessarily improving or impairing immediate performance.

Zekun Wu, Man Su, Huiyong Li et al.

Proactive assistance with large language models (LLMs) has received growing attention in the human computer interaction (HCI) community. However, most past work on proactive LLMs' assistance has focused on adult users and task-oriented settings, leaving open how such systems could support children, whose interests and needs are often expressed through gaze and other nonverbal behaviors rather than explicit requests. In this study, we focus on two key challenges of proactive assistance in children's picture exploration: when to provide assistance and what assistance to provide based on children's nonverbal behaviors. To address these challenges, we introduce Ollie, a gaze-informed proactive artificial intelligence (AI) assistant that offers short narrative descriptions based on where a child is looking. Ollie uses children's gaze to estimate their attention, identify their current visual focus, and select a related picture region for the LLM to verbally describe. In a within-subject experiment, we compared gaze-informed assistance with random assistance. Results show that gaze-informed assistance kept children's attention on their current focus for a longer period of time, and guided them more effectively to related picture regions. Children, parents, and a participating kindergarten teacher viewed Ollie positively and consider that it better matched children's interests when compared with the random assistance. This work shows the feasibility of using gaze as an implicit input for proactive AI assistance for children and provides design implications for future child-centered AI systems.

Tomohiro Nagashima, Lisa Siegrist, Niklas Scholz et al.

As Artificial Intelligence (AI)-based technologies have been integrated into school classrooms where multiple stakeholders (with different roles) interact with each other, it is critical to deeply understand stakeholder views in the classroom. In particular, prior work has not fully uncovered how teachers' and school students' views might or might not align well with each other, especially in K-12 classrooms. We conducted a speed-dating study using storyboards with 16 school students and 15 school teachers in Germany to investigate alignments and misalignments between their views on student-AI decision-making control in K-12 classroom. Through an explicit pair-matching analysis, we found that students and teachers had misaligned views on several key topics, including how much they trust AI and social and emotional aspects of student learning with AI. Findings also revealed the importance of teacher-student relationships outside of AI use that shape stakeholders' views and interactions. We discuss potential reasons for the observed misaligned views and strategies to fill the perspective gaps. This study illustrates the complexities of preferences in teacher-student-AI interactions that depend on the dynamic relations among the stakeholders.

Vincent Aleven, Conrad Borchers, Yun Huang et al.

Intelligent tutoring systems (ITSs) are effective in helping students learn; further research could make them even more effective. Particularly desirable is research into how students learn with these systems, how these systems best support student learning, and what learning sciences principles are key in ITSs. CTAT+Tutorshop provides a full stack integrated platform that facilitates a complete research lifecycle with ITSs, which includes using ITS data to discover learner challenges, to identify opportunities for system improvements, and to conduct experimental studies. The platform includes authoring tools to support and accelerate development of ITS, which provide automatic data logging in a format compatible with DataShop, an independent site that supports the analysis of ed tech log data to study student learnings. Among the many technology platforms that exist to support learning sciences research, CTAT+Tutorshop may be the only one that offers researchers the possibility to author elements of ITSs, or whole ITSs, as part of designing studies. This platform has been used to develop and conduct an estimated 147 research studies which have run in a wide variety of laboratory and real-world educational settings, including K-12 and higher education, and have addressed a wide range of research questions. This paper presents five case studies of research conducted on the CTAT+Tutorshop platform, and summarizes what has been accomplished and what is possible for future researchers. We reflect on the distinctive elements of this platform that have made it so effective in facilitating a wide range of ITS research.