Generative AI makes creative work faster, but its fluent outputs can encourage premature acceptance, shallow evaluation, and loss of orientation across extended chats. We present ReasonScape, a redesign of the familiar chat user-interface that supports creative judgement during GenAI co-creation. ReasonScape replaces answer-first interaction with a guided workflow that (1) introduces brief reflection checkpoints to surface goals and constraints, (2) triggers sensemaking interventions for recurring evaluation gaps when reasoning shows signs of one-sidedness, inconsistency, or unexamined assumptions, and (3) externalises users’ evolving rationale through ReflectRail, a persistent rationale trail with direct navigation. A lightweight status view (ReflectMeter) shows which reflective checks are open without implying correctness. We report findings from two rounds of expert inquiry (n=16) indicating that the approach can keep evaluation lightweight and supportive, while highlighting calibration risks around frequency, tone, and stability. We conclude with design implications for creativity-support tools that balance reflective scaffolding with creative flow.

In digitalized plants, control room operators experience cognitive overload, and literature emphasizes that multimodal physiological integration can better capture operators’ cognitive states. In chemical process operations, current methods often overlook cross-modal interactions. This study used a formaldehyde production simulation with 42 participants exposed to failure scenarios, assessing performance by recovery time and plant status. A novel framework for multimodal physiological integration is proposed, modeling high/low levels of eye-based, skin-related, and cardiovascular metrics using Gaussian distributions. Unique combinations of these metrics are formed, and the overlapping coefficient (OVL) is computed to identify consistent physiological combinations across participants. High-OVL combinations appeared in all optimal, 79% of good, and were negligible in the poor class. Successful participants exhibited distinct cognitive strategies, from low-arousal focus to high-arousal compensation. The Bayesian network estimated participants’ performance-level probabilities, achieving 91% accuracy and robustness to missing data. The framework supports reflective learning, supervisory support, and adaptive training systems.

This study investigates how structured interventions—specifically pausing at natural breakpoints and guided note-taking—affect cognitive performance, memory retention, and task continuity in digital reading. We introduce SmartPause , a context-aware bookmarking system that nudges users to pause at meaningful points and externalise insights through lightweight notes. In a controlled experiment (N = 51), participants were assigned to one of three conditions: (1) interruption at an arbitrary point, (2) guided pause at a natural breakpoint, or (3) guided pause with note-taking. Results reveal that guided pauses, particularly when combined with note-taking, significantly enhance long-term memory retention while having no measurable impact on perceived cognitive load or selective attention. These findings highlight the potential of digital tools to support cognitive load management and task continuity by structuring interruptions in alignment with natural cognitive rhythms. The high usability rating of SmartPause underscores its practical applicability across e-reading platforms. This study contributes to human-computer interaction (HCI) by integrating principles from cognitive science into design solutions that enhance comprehension and information retention. Future research should explore personalised interventions, extended retention intervals, and real-world deployment to further optimise cognitive load in digital reading contexts.

In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time which resulted in a 95.8% prediction accuracy using hidden Markov model. These predictions enable the development of more effective intervention strategies.

Situational awareness and decision support tools such as procedures and alarm systems are vital for effective interaction among control room operators, especially in safety-critical situations. In safety-critical environments such as process plants, there remains a gap in evaluating specific tools during actual operations, or ”work-as-done.” Additionally, the underlying factors that might impact operators’ cognitive states and performance concerning safety have not been thoroughly explored. The need for such an evaluation is further bolstered by current interaction configurations where operators are more passive than active, thus reducing their cognitive performance. Therefore, this experimental study addresses the highlighted evaluation gap by introducing and comparing three human system interfaces/decision support tools in four human-in-the-loop configurations. The supports include two alarm design formats (prioritized vs. non-prioritized) and three procedure representation formats (paper, screen-based digitized, and an AI-based support system built with an integrated Bayesian network and reinforcement learning model). Ninety-two people (n = 92) participated voluntarily in the test. They were divided equally into four groups. Each group tested three safety-related events in a simulated formaldehyde production facility. Individuals belonging to the group with prioritized alarms and utilized paper procedures rated procedural support slightly higher on average than others in different groups. Unlike the other groups, their assessment of alarm prioritization support remained consistent across all scenarios. Further analysis of the impact of the setup on cognitive states and actual performance will be performed.

This study presents a novel methodology incorporating safety constraints into a robotic simulation during the training of deep reinforcement learning (DRL). The framework integrates specific parts of the safety requirements, such as velocity constraints, as specified by ISO 10218, directly within the DRL model that becomes a part of the robot’s learning algorithm. The study then evaluated the efficiency of these safety constraints by subjecting the DRL model to various scenarios, including grasping tasks with and without obstacle avoidance. The validation process involved comprehensive simulation-based testing of the DRL model’s responses to potential hazards and its compliance. Also, the performance of the system is carried out by the functional safety standards IEC 61508 to determine the safety integrity level. The study indicated a significant improvement in the safety performance of the robotic system. The proposed DRL model anticipates and mitigates hazards while maintaining operational efficiency. This study was validated in a testbed with a collaborative robotic arm with safety sensors and assessed with metrics such as the average number of safety violations, obstacle avoidance, and the number of successful grasps. The proposed approach outperforms the conventional method by a 16.5% average success rate on the tested scenarios in the simulations and 2.5% in the testbed without safety violations.

Mass customization and shorter manufacturing cycles are becoming more important among small and medium-sized companies. However, classical industrial robots struggle to cope with product variation and dynamic environments. In this paper, we present CoBT, a collaborative programming by demonstration framework for generating reactive and modular behavior trees. CoBT relies on a single demonstration and a combination of data-driven machine learning methods with logic-based declarative learning to learn a task, thus eliminating the need for programming expertise or long development times. The proposed framework is experimentally validated on 7 manipulation tasks and we show that CoBT achieves ≈ 93% success rate overall with an average of 7.5s programming time. We conduct a pilot study with non-expert users to provide feedback regarding the usability of CoBT. More videos and generated behavior trees are available at: https://github.com/jainaayush2006/CoBT.git.

The impact of digital technology on human cognition has become a topic of significant interest in recent years, with various studies highlighting the adverse effects on cognition, particularly attention. While the negative impact of digital applications on our attentional processes is well-documented, practical solutions to mitigate these detrimental effects are rare. In this paper, we propose Attention Mode as a design solution that aims to minimise the negative impacts of digital technology on attention by creating easy-to-understand and navigate user interfaces. This approach can help users focus on tasks, reduce cognitive load, and minimise distractions, ultimately improving their overall experience. We developed interaction mock-ups incorporating the Attention Mode and conducted a preliminary one-to-one sharing with 5 participants to analyse to get early feedback. It resulted in valuable feedback on how such a design focus could help users to focus on the content without distractive elements. By integrating the issues from the start of the design process instead of handling it as an afterthought, this work offers new insights into crafting user interfaces in a way that the negative impact of digital technology on attention is mitigated.