Search Results (65)

Mathematical creativity (MC) plays an important role in mathematics and education; however, its conceptualization and assessment remain inconsistent across empirical studies. This systematic review examined how MC has been defined, conceptualized, and assessed across 80 empirical studies involving K-12 populations. Through thematic analysis, the study identified three definition types: divergent thinking, problem-solving, and problem-posing, as well as affective–motivational emphasis. We organized theoretical frameworks into three categories: domain-general, domain-specific, and multidimensional frameworks. Results showed that the most common definitions emphasized divergent thinking components while fewer studies highlighted affective and dispositional factors. Domain-specific frameworks were the most frequently used, followed by multidimensional frameworks. Regarding assessment, studies predominantly relied on divergent-thinking scoring. Most assessments used criterion-referenced rubrics with norm-based comparisons. They were delivered mainly in paper-pencil format. Tasks were typically open-ended multiple-solution problems with fewer studies using self-reports or observational methods. Overall, the field prioritizes product-based scoring (e.g., fluency, flexibility, originality) over evidence about students’ solution processes (e.g., reasoning, metacognitive monitoring). To improve cross-context comparability, future work should standardize and transparently report age, grade, and country coding and scoring practices. Full article

While interleaved learning has been shown to enhance young adults’ acquisition of confusable natural categories, its effects on children’s natural category learning remain underexplored. The present study investigated the effects of study schedule (interleaving vs. blocking) on both categorization accuracy and the accuracy of metacognitive judgments during the learning of natural rock categories, comparing children and young adults. In Experiment 1, participants studied under blocked or interleaved conditions and subsequently provided global judgments of their learning. In Experiment 2, we employed a self-paced learning paradigm that required learners to regulate their own study time. Additionally, participants made item-by-item judgments of their learning during the study phase. Across both experiments, we found that interleaved learning significantly improved categorization accuracy, with young adults benefiting more than children. Regarding metacognitive monitoring, interleaving reduced overconfidence in children but led to underconfidence in young adults, as reflected in both global and item-level judgments. These findings suggest that the benefits of interleaved learning for category performance and metacognitive monitoring vary with age, highlighting age-related differences in the effectiveness of interleaved learning. Full article

ASD poses special difficulty in both cognitive and metacognitive development, necessitating specialized educational strategies. This research proposes LearnMate, a web-based application powered by machine learning techniques that aims to improve the abilities of children with autism. Utilizing classification models learned from medical data, LearnMate forecasts skill acquisition and suggests personalized learning activities according to the strengths and developmental requirements of the child. The system permits instructors to monitor progress through real-time feedback, enabling adaptive learning approaches. Pilot application to more than 100 children showed significant gains in their skills. The results demonstrate the immense potential for change through machine learning in special education to facilitate data-driven, personalized learning opportunities that enhance the capabilities of both autistic students and teachers. Full article

As large language model (LLM) tutors evolve from scripted helpers into adaptive educational partners, their capacity for self-regulation, ethical decision-making, and internal monitoring will become increasingly critical. This paper introduces the Needs-Driven Consciousness Framework (NDCF) as a novel, integrative architecture that combines Dennett’s multiple drafts model, Damasio’s somatic marker hypothesis, and Tulving’s tripartite memory system into a unified motivational design for synthetic consciousness. The NDCF defines three core regulators, specifically Survive (system stability and safety), Thrive (autonomy, competence, relatedness), and Excel (creativity, ethical reasoning, long-term purpose). In addition, there is a proposed supervisory Protect layer that detects value drift and overrides unsafe behaviours. The core regulators compute internal need satisfaction states and urgency gradients, feeding into a softmax-based control system for context-sensitive action selection. The framework proposes measurable internal signals (e.g., utility gradients, conflict intensity Ω), behavioural signatures (e.g., metacognitive prompts, pedagogical shifts), and three falsifiable predictions for educational AI testbeds. By embedding these layered needs directly into AI governance, the NDCF offers (i) a psychologically and biologically grounded model of emergent machine consciousness, (ii) a practical approach to building empathetic, self-regulating AI tutors, and (iii) a testable platform for comparing competing consciousness theories through implementation. Ultimately, the NDCF provides a path toward the development of AI tutors that are capable of transparent reasoning, dynamic adaptation, and meaningful human-like relationships, while maintaining safety, ethical coherence, and long-term alignment with human well-being. Full article

Metacognitive monitoring plays a crucial role in self-regulated learning, as accurate monitoring enables effective control, which in turn impacts learning outcomes. Most studies on metacognitive monitoring have focused on learners’ monitoring abilities when they are explicitly prompted to monitor. However, in real-world educational settings, learners are more often prompted to control their learning, such as deciding whether to allocate additional time to a learning target. The primary goal of this study was to investigate whether retrieval is engaged when learners are explicitly prompted to control their learning processes by making study decisions. To address this, three experiments were conducted. In Experiment 1, participants (N = 39) studied 70 Swahili–English word pairs in a learning task. Each trial displayed a word pair for 8 s, followed by a distractor task (a two-digit mental addition) and a study decision intervention (choose “Study Again” or “Next”). After learning, participants provided a global judgment of learning (JOL), estimating their overall recall accuracy. Finally, they completed a cued recall test (Swahili cue). Responses were scored for accuracy and analyzed alongside study decisions, study decision reaction time (RT), and metacognitive judgments. Reaction times (RTs) for study decisions correlated positively with test accuracy, global judgments of learning (JOLs), and judgments of confidence (JOCs), suggesting retrieval likely underlies these decisions. Experiment 2 (N = 74, between-subjects) compared memory performance and intervention response time between single-study, restudy, retrieval (explicit recall prompt), and study decision (study decision prompt) groups to have better control over study time and cognitive processes. Although no significant group differences in test accuracy emerged, the retrieval group took longer to respond than the study decision group. Within-subject analyses revealed similar recall accuracy patterns: participants recalled successfully retrieved or “no restudy” items better than failed-retrieval or “restudy” items, implying shared cognitive processes underlying retrieval and study decision interventions. Experiment 3 (N = 74, within-subject, three learning conditions: single-study, retrieval, and study decision) replicated these findings, with no condition effects on test accuracy but longer RT for retrieval than study decisions. The similar recall accuracy patterns between retrieval and study decision interventions further supported shared cognitive processes underlying both tasks. Self-reports across experiments confirmed retrieval engagement in both retrieval and study decision interventions. Collectively, the results suggest that retrieval likely supports study decisions but may occur less frequently or less deeply than under explicit monitoring prompts. Additionally, this study explored objective, online measures to detect retrieval-based metacognitive monitoring. Full article

Depression affects millions of people worldwide and remains a significant challenge in mental health care. Despite advances in pharmacological and psychotherapeutic treatments, there is a critical need for accessible and engaging tools that help individuals manage their mental health in real time. This paper presents a novel gamified, AI-driven system embedded within Internet of Things (IoT)-enabled environments to address this gap. The proposed platform combines micro-games, adaptive surveys, sensor data, and AI analytics to support personalized and context-aware depression monitoring and self-regulation. Unlike traditional static models, this system continuously tracks behavioral, cognitive, and environmental patterns. This data is then used to deliver timely, tailored interventions. One of its key strengths is a research-ready design that enables real-time simulation, algorithm testing, and hypothesis exploration without relying on large-scale human trials. This makes it easier to study cognitive and emotional trends and improve AI models efficiently. The system is grounded in metacognitive principles. It promotes user engagement and self-awareness through interactive feedback and reflection. Gamification improves the user experience without compromising clinical relevance. We present a unified framework, robust evaluation methods, and insights into scalable mental health solutions. Combining AI, IoT, and gamification, this platform offers a promising new approach for smart, responsive, and data-driven mental health support in modern living environments. Full article

Background: Metacognition refers to the ability to reflect on and regulate cognitive processes. Despite advances in neuroimaging and lesion studies, its neural correlates, as well as their interplay with other cognitive domains, remain poorly understood. The orbitofrontal cortex (OFC) is proposed as a potential substrate for metacognitive processing due to its contribution to evaluating and integrating reward-related information, decision-making, and self-monitoring. Methods: This study examined OFC involvement in metacognition using transcranial direct current stimulation (tDCS) while participants performed a two-alternative forced choice task with confidence ratings to assess their metacognitive sensitivity. Before stimulation, the subjects completed the Metacognitions Questionnaire-30 and a monetary intertemporal choice task for the quantification of delay discounting. Results: Linear mixed-effects models showed that anodal tDCS over the left OFC reduced participants’ metacognitive sensitivity compared to sham stimulation, leaving perceptual decision-making accuracy unaffected. Moreover, real stimulation increased self-reported confidence ratings compared to the sham. Significant correlations were found between metacognitive sensitivity and negative beliefs about thinking. Conclusions: These results highlight the potential involvement of the OFC in the processing of retrospective second-order judgments about decision-making performance. Additionally, they support the notion that OFC overstimulation contributes to metacognitive dysfunctions detected in clinical conditions, such as difficulties in assessing the reliability of one’s thoughts and decision outcomes. Full article

This study explored innovative methods for teaching mathematics to seventh-grade students with persistently low performance by using an AI-driven neural network approach, specifically focusing on solving first-degree inequalities. Guided by the Response to Intervention (RTI) framework, the intervention aimed to reduce math anxiety and build academic resilience through the development of cognitive and metacognitive strategies. A rigorous pre- and post-test design was employed to evaluate changes in performance, anxiety levels, and resilience. Fifty-six students participated in the 12-week program, receiving personalized instruction tailored to their individual needs. The AI tool provided real-time feedback and adaptive problem-solving tasks, ensuring students worked at an appropriate level of challenge. Results indicated a marked decrease in math anxiety alongside significant gains in cognitive skills such as problem-solving and numerical reasoning. Students also demonstrated enhanced metacognitive abilities, including self-monitoring and goal setting. These improvements translated into higher academic performance, particularly in the area of inequalities, and greater resilience, highlighting the effectiveness of AI-based strategies in supporting learners who struggle persistently in mathematics. Overall, the findings underscore how AI-driven teaching approaches can address both the cognitive and emotional dimensions of mathematics learning. By offering targeted, adaptive support, educators can foster a learning environment that reduces stress, promotes engagement, and facilitates long-term academic success for students with persistently low performance in mathematics. Full article

To elucidate the role of metacognitive reflection in the development of children’s executive function (EF) skills, the current study examined relations among implicit and explicit forms of metacognition in 7- to 9-year-olds during performance based on the Dimensional Change Card Sort (DCCS), while experimentally manipulating the propensity to reflect on the task. Results showed that instructions to reflect led to improved task accuracy and better metacognitive control, but only in younger children, likely because older children were already engaging in reflection. Individual differences in trait mindfulness were related to a similarly reflective mode of responding, characterized by improved task accuracy and metacognitive control. In contrast, articulatory suppression impaired children’s task accuracy and metacognitive monitoring. Additionally, simply asking children to make metacognitive judgments without extra instructions decreased the amplitude of event-related potential (ERP) indices of error detection (the error-related negativity; ERN) and conflict detection (the N2). Finally, individual differences in trait anxiety were related to larger Pe amplitudes. Taken together, the current findings reinforce theoretical frameworks integrating metacognition and EF and highlight the shared influence of metacognitive reflection across multiple levels of analysis. Full article

The Thoughtseeds Framework introduces a novel computational approach to modeling thought dynamics in meditative states, conceptualizing thoughtseeds as dynamic attentional agents that integrate information. This hierarchical model, structured as nested Markov blankets, comprises three interconnected levels: (i) knowledge domains as information repositories, (ii) the Thoughtseed Network where thoughtseeds compete, and (iii) meta-cognition regulating awareness. It simulates focused-attention Vipassana meditation via rule-based training informed by empirical neuroscience research on attentional stability and neural dynamics. Four states—breath_control, mind_wandering, meta_awareness, and redirect_breath—emerge organically from thoughtseed interactions, demonstrating self-organizing dynamics. Results indicate that experts sustain control dominance to reinforce focused attention, while novices exhibit frequent, prolonged mind_wandering episodes, reflecting beginner instability. Integrating Global Workspace Theory and the Intrinsic Ignition Framework, the model elucidates how thoughtseeds shape a unitary meditative experience through meta-awareness, balancing epistemic and pragmatic affordances via active inference. Synthesizing computational modeling with phenomenological insights, it provides an embodied perspective on cognitive state emergence and transitions, offering testable predictions about meditation skill development. The framework yields insights into attention regulation, meta-cognitive awareness, and meditation state emergence, establishing a versatile foundation for future research into diverse meditation practices (e.g., Open Monitoring, Non-Dual Awareness), cognitive development across the lifespan, and clinical applications in mindfulness-based interventions for attention disorders, advancing our understanding of the nature of mind and thought. Full article

The ability of participants to monitor the correctness of their own decisions by rating their confidence is a form of metacognition. This introspective act is crucial for many aspects of cognition, including perception, memory, learning, emotion regulation, and social interaction. Researchers assess the quality of confidence ratings according to bias, sensitivity, and efficiency. To do so, they deploy quantities such as $meta-d’$-$d^{\prime }$ or the $M-ratio$ These measures compute the expected accuracy level of performance in the primary task (Type 1) from the secondary confidence rating task (Type 2). However, these measures have several limitations. For example, they are based on unwarranted parametric assumptions, and they fall short of accommodating the granularity of confidence ratings. Two recent papers by Dayan and by Fitousi have proposed information-theoretic measures of metacognitive efficiency that can address some of these problems. Dayan suggested $meta-\mathcal{I}$ and Fitousi proposed $meta-\mathcal{U}$, $meta-\mathcal{KL}$, and $meta-\mathcal{J}$. These authors demonstrated the convergence of their measures on the notion of metacognitive efficiency using simulations, but did not apply their measures to real empirical data. The present study set to test the construct validity of these measures in a concrete behavioral task—the face-matching task. The results supported the viability of these novel indexes of metacognitive efficiency, and provide substantial empirical evidence for their convergence. The results also adduce considerable evidence that participants in the face-matching task acquire valuable metaknowledge about the correctness of their own decisions in the task. Full article

The rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the safety, ethics, and responsibility of AI systems. To address these challenges, the principles of the robustness, interpretability, controllability, and ethical alignment framework are essential. This paper explores the integration of metacognition—defined as “thinking about thinking”—into AI systems as a promising approach to meeting these requirements. Metacognition enables AI systems to monitor, control, and regulate the system’s cognitive processes, thereby enhancing their ability to self-assess, correct errors, and adapt to changing environments. By embedding metacognitive processes within AI, this paper proposes a framework that enhances the transparency, accountability, and adaptability of AI systems, fostering trust and mitigating risks associated with autonomous decision-making. Additionally, the paper examines the current state of AI safety and responsibility, discusses the applicability of metacognition to AI, and outlines a mathematical framework for incorporating metacognitive strategies into active learning processes. The findings aim to contribute to the development of safe, responsible, and ethically aligned AI systems. Full article

This systematic review answers questions about whether or not a model of metacognition is well accepted and if it can be used in cognitive architecture design. Self-planning, self-monitoring, and self-evaluation are the model concepts, which are viewed as metacognitive experiences. A newly formulated theoretical approach named Attention as Action was targeted, as it is shown to be used in cognitive architecture design. In order to link the model to the theoretical approach, specific concepts like mental imagery and learning experience were researched. The method includes the statistical analysis of key phrases in articles that were collected based on a system of criteria. Data were retrieved from 91 scientific papers to allow statistical analysis of the relationship between the model of metacognition and the theoretical approach to cognitive architecture design. Several observations from the data show that the model is applicable for designing cognitive monitoring systems that depict experiences of metacognition. Furthermore, the results point out that the researched fields require explanations about the concepts defined in the theoretical approach of Attention as Action. Systematically formulated as types of internal attentional experiences, new relations are provided for researching cognitive and metacognitive concepts in terms of the cognitive cycle. Full article

Utilization of monetary rewards in educational settings remains contentious due to its potential adverse effects such as performance-related anxiety, metacognitive inaccuracy, and diminished intrinsic motivation. The current study developed a novel reward-based learning paradigm wherein rewards are granted based on monitoring accuracy rather than learning performance. Specifically, learners receive rewards for items that they predict they will remember and subsequently successfully remember them during the final test. Two experiments were conducted to assess the efficacy of this paradigm: Experiment 1 focused on learning Chinese medicine images, while Experiment 2 examined the transfer of math knowledge in classroom settings. The results indicated that rewarding the alignment between performance and metacognitive accuracy improved learning performance compared to both a baseline group and a group receiving performance-based rewards. Furthermore, this paradigm effectively mitigated performance-related anxiety and preserved intrinsic motivation. Overall, our findings highlight the critical role of reward-based learning design and emphasize the importance of addressing metacognitive accuracy alongside performance in educational practice. Full article