It has been suggested that the functional significance learners ascribe to virtual rewards in gamified systems can have a significant impact on intrinsic motivation. Yet, to date, there has been a lack of research examining this relationship empirically. In the present study, we therefore applied Cognitive Evaluation Theory to examine how learners’ (n = 162) interpretation of virtual rewards in Duolingo affected autonomy satisfaction, competence satisfaction, and intrinsic motivation. Based on a structural equation modeling approach, our findings suggests that the informational significance learners ascribe to virtual rewards positively affects competence satisfaction, and that autonomy and competence satisfaction together affect intrinsic motivation. For the HCI field, the results point towards the importance of moving beyond treating virtual rewards in gamified learning as a use-or-avoid decision, and instead consider how such design elements can be modified to provide increased encouragement and positive feedback.
Assessing children’s cognitive development in early mathematics is vital for effective teaching. Compared to closed-ended questions, which may fail to capture nuanced developmental spectrum, open-ended elicitation tasks (e.g., asking students to manipulate objects or draw to represent numbers) serve as a promising approach to reveal deeper cognitive processes. However, their diverse and unstructured nature makes systematic analysis challenging for teachers. We present OpenCD, a teacher-facing system that automatically analyzes multimodal student responses to capture individualized insights. Based on Evidence-Centered Design, it combines Vision-Language Models (VLMs) and expert models to generate interactive diagnostic graphs and reports with traceability back to behavioral evidence. In our two-part evaluation, a validation study found 90.3% of the system’s diagnoses “completely reasonable,” and a user study showed that OpenCD reduced teachers’ analysis burden and enhanced their insights into student thinking. Our work contributes to scalable process-based assessment for mathematical literacy.
Assessment literacy (AL) is essential for personalized education, yet difficult to cultivate in pre-service teachers. Conventional teacher preparation programs focus on theoretical knowledge, while digital assessment tools commonly provide opaque scores or parameters. These limitations hinder reflection and transfer, leaving AL underdeveloped. We propose XIA, an eXplainable Intelligent Assessment platform that extends statistics-informed support with visualized cognitive diagnostic reasoning, including contrastive and counterfactual explanations. In a pre-post controlled study with 21 pre-service teachers, we combined quantitative tasks and questionnaires with qualitative interviews. The findings offer preliminary evidence that XIA supported reflection, self-regulation, and assessment awareness, and helped reduce assessment errors. Interviews further showed a shift from score-based judgments toward evidence-based reasoning. This work contributes insights into the design of intelligent assessment tools, showing how explanatory scaffolding can bridge assessment theory and classroom practice and support the cultivation of AL in teacher education.
Data storytelling workflows ask learners to integrate analytical, design, and narrative skills, but instructors rarely have the capacity to provide detailed feedback at each step. Computational and AI-assisted storytelling offers opportunities to support student learning, but how feedback should be structured effectively remains unclear. To address this gap, we conducted a two-phase participatory design study. Through participant observations (N=8) and interviews (N=6), the first phase explored learners and educators' feedback needs and challenges in a data storytelling course. The second phase conducted two design workshops (N=8/10) to design and evaluate feedback strategies (frequency, seamlessness, accountability) for Story Studio: an AI-assisted narrative storytelling application. Our findings show that participants perceived on-demand and process feedback modes as effective, but automatic and outcome feedback as slightly more persuasive. We discuss implications for designing AI-augmented storytelling systems that adapt their feedback modes to the diverse needs and expectations of students.
Open-ended writing assignments are central to higher education, yet heterogeneous submissions and scale make evaluation difficult. Automated writing evaluation (AWE) promises speed but often trades away transparency and sidelines human judgment. This paper repositions AI as an on-demand collaborator that can provide specific, targeted support. In a formative study, we expose leverage points in three cognitive dimensions: evidence identification, comparative judgment, and feedback composition. Guided by these insights, we build EvaluAId, which supports interactive rubric-content mapping, adaptive benchmarking and self-calibration, and personalized, rubric-aligned feedback synthesis. Through a within-subjects study with 12 TAs, we evaluate how this approach supports grading compared with a rubric+LLM chatbot and an LLM-based AWE; EvaluAId improved alignment with expert ratings and increased graders' satisfaction. Finally, interviews with TAs, instructors, and students underscored the value of thoughtfulness supported by EvaluAId while surfacing practical considerations for integration into classroom. Together, our results argue for deliberate, evidence-first, human-in-the-loop evaluation.
Maintaining motivation and sustained engagement in pediatric neurorehabilitation remains a significant challenge, particularly for children with neuromotor impairments. Traditional therapy methods often lack personalized adaptability, which can limit adherence and effectiveness. The proposed framework addresses this gap by integrating multimodal sensor data, including EEG, posture, and gaze, to continuously monitor emotional, cognitive, and behavioral engagement during therapy sessions. This real-time assessment enables dynamic adaptation of game-based exercises with the aim of optimizing motivation, reducing disengagement, and promoting functional recovery. This concept was implemented in a clinical setting with children diagnosed with coordination disorders and neuropsychomotor delays. Preliminary results with four participants indicate that by tracking engagement levels and supporting session personalization, it is possible to stimulate the child’s motivation across multiple sessions. These findings suggest that incorporating adaptive, engagement-driven frameworks can provide a useful tool to improve rehabilitation efficacy, offering a way toward more personalized and responsive therapeutic strategies in pediatric neurorehabilitation.