Matthijs Smakman

Higher education students routinely engage in complex digital information tasks that require information problem-solving (IPS) competence. While generative AI (GenAI) tools can support IPS, they also introduce risks of overreliance and diminished agency. Yet, educators lack practical tools to guide students toward critical GenAI use. Existing IPS rubrics, developed for pre-AI contexts, mainly assess final products and offer limited insight into students' agency in their use of GenAI throughout the IPS process. This study developed and validated a formative rubric characterizing human-AI agency across IPS phases. Development followed five educational design research cycles: indicator derivation from think-aloud protocols with pre-service teachers and teacher educators; validation with experts; usability testing with pre-service teachers; inter-rater reliability assessment; and pattern identification. The resulting rubric contains 55 behavioral indicators across six IPS phases, operationalized via the DIPS model, and three agency levels, from substitution through complementation to augmentation. Pattern analysis identified nine prototypical human-AI agency patterns within tasks, including Offloader, Critical Verifier, or Creative Director. Results indicate that human-AI agency is context-and phase-dependent rather than a fixed characteristic: substitutive use is not inherently inferior to augmentative use. The rubric scaffolds a reflective cycle of awareness of current GenAI use, recognition of dilemmas each AI-use decision entails, and informed decision-making about whether, when, and how to use AI. It makes variation in human-AI agency visible, equipping students, teachers, and researchers with a shared vocabulary to open the conversations that matter: not whether AI was applied, but rather how deliberately and purposefully it was used.

Developing students’ information problemsolving (IPS) competence in higher education is imperative. However, existing theoretical frameworks describe IPS learning outcomes without guiding effective learning environment design. This systematic review and meta-analysis synthesized empirical evidence to formulate design principles for developing IPS competence. A systematic search across seven academic databases yielded 69 peer-reviewed articles from 2000–2023 with controlled pretest-posttest designs targeting (under)graduate students. Analysis of these studies yielded seven design principles: learning task, instruction, modeling, practice, learning activities, support, and feedback, with meta-analyses validating key relationships. The IPS educational design principles (IPS-EDP) model summarizes how these principles address learning outcomes, teaching and learning activities, and assessment strategies. While our review covered all IPS components, empirical evidence predominantly addressed information search and selection, potentially limiting generalizability to broader IPS competence. The IPS-EDP model provides a theoretical foundation for investigating and designing effective IPS learning environments in higher education, bridging research and practice.

Healthcare practitioners often use distraction techniques to ease children’s anxiety during blood draws. This study explored the effectiveness of a social robot, Softbank Robotics NAO, in reducing anxiety and pain for children aged 4-12 during blood draws. The robot engaged with children using behaviors tailored to the children’s age. Interestingly, results indicated the robot significantly increased anxiety, but pain levels remained unchanged. We argue that this effect may result from the robot’s novelty and that, over the long term, the robot could have a positive impact on anxiety, consistent with other studies. This research underscores the potential of social robots to alleviate children’s anxiety in medical procedures, enhancing patient experience in pediatric healthcare.

Maintaining the child-robot relationship after a significant break, such as a holiday, is an important step for developing sustainable social robots for education. We ran a four-session user study (n = 113 children) that included a nine-month break between the third and fourth session. During the study, participants practiced math with the help of a social robot math tutor. We found that social personalization is an effective strategy to better sustain the child-robot relationship than the absence of social personalization. To become reacquainted after the long break, the robot summarizes a few pieces of information it had stored about the child. This gives children a feeling of being remembered, which is a key contributor to the effectiveness of social personalization. Enabling the robot to refer to information previously shared by the child is another key contributor to social personalization. Conditional for its effectiveness, however, is that children notice these memory references. Finally, although we found that children's interest in the tutoring content is related to relationship formation, personalizing the topics did not lead to more interest in the content. It seems likely that not all of the memory information that was used to personalize the content was up-to-date or socially relevant.