Search Results (114)

Artificial Intelligence (AI), particularly Generative AI (GenAI) and Large Language Models (LLMs), is rapidly reshaping higher education by transforming teaching, learning, assessment, research, and institutional management. This entry provides a state-of-the-art, comprehensive, evidence-based synthesis of established AI applications and their implications within the higher education landscape, emphasizing mature knowledge aimed at educators, researchers, and policymakers. AI technologies now support personalized learning pathways, enhance instructional efficiency, and improve academic productivity by facilitating tasks such as automated grading, adaptive feedback, and academic writing assistance. The widespread adoption of AI tools among students and faculty members has created a critical need for AI literacy—encompassing not only technical proficiency but also critical evaluation, ethical awareness, and metacognitive engagement with AI-generated content. Key opportunities include the deployment of adaptive tutoring and real-time feedback mechanisms that tailor instruction to individual learning trajectories; automated content generation, grading assistance, and administrative workflow optimization that reduce faculty workload; and AI-driven analytics that inform curriculum design and early intervention to improve student outcomes. At the same time, AI poses challenges related to academic integrity (e.g., plagiarism and misuse of generative content), algorithmic bias and data privacy, digital divides that exacerbate inequities, and risks of “cognitive debt” whereby over-reliance on AI tools may degrade working memory, creativity, and executive function. The lack of standardized AI policies and fragmented institutional governance highlight the urgent necessity for transparent frameworks that balance technological adoption with academic values. Anchored in several foundational pillars (such as a brief description of AI higher education, AI literacy, AI tools for educators and teaching staff, ethical use of AI, and institutional integration of AI in higher education), this entry emphasizes that AI is neither a panacea nor an intrinsic threat but a “technology of selection” whose impact depends on the deliberate choices of educators, institutions, and learners. When embraced with ethical discernment and educational accountability, AI holds the potential to foster a more inclusive, efficient, and democratic future for higher education; however, its success depends on purposeful integration, balancing innovation with academic values such as integrity, creativity, and inclusivity. Full article

Emotional intelligence (EI) and its meta-cognitive counterpart, meta-emotional intelligence (MEI), have increasingly been recognized as key factors in helping students understand, regulate, and reflect on their emotional experiences. MEI expands upon EI by incorporating meta-cognitive beliefs and awareness about one’s own emotional functioning, thereby influencing both emotional regulation and positive behavioral choices. This study examined the relationship between MEI and the knowledge of positive behavioral rules among 198 students aged 9 to 12. Participants completed the IE-ACCME-B, which assesses meta-emotional beliefs, emotional self-conceptualization, and emotional abilities, along with the PBIS-KGVE, a tool developed ad hoc to measure knowledge, generalization, and value-based understanding of school rules. Findings highlight that almost all considered variables are intercorrelated, with meta-emotional beliefs being the best predictor of the students’ knowledge, generalization, and value-based interpretation of behavioral rules. These results suggest the opportunity to establish interventions focused on meta-emotional beliefs to enhance behavioral rule knowledge and foster prosocial development within educational contexts. Full article

We investigated the impact of incorporating quantitative reasoning for deeper sense-making in a Quantum Interactive Learning Tutorial (QuILT) on students’ conceptual performance using a framework emphasizing integration of conceptual and quantitative aspects of quantum optics. In this investigation, we compared two versions of the QuILT that were developed and validated to help students learn various aspects of quantum optics using a Mach Zehnder Interferometer with single photons and polarizers. One version of the QuILT is entirely conceptual while the other version integrates quantitative and conceptual reasoning (hybrid version). Performance on conceptual questions of upper-level undergraduate and graduate students who engaged with the hybrid QuILT was compared with that of those who utilized the conceptual QuILT emphasizing the same concepts. Both versions of the QuILT focus on the same concepts, use a scaffolded approach to learning, and take advantage of research on students’ difficulties in learning these challenging concepts as well as a cognitive task analysis from an expert perspective as a guide. The hybrid and conceptual QuILTs were used in courses for upper-level undergraduates or first-year physics graduate students in several consecutive years at the same university. The same conceptual pre-test and post-test were administered after traditional lecture-based instruction in relevant concepts and after student engaged with the QuILT, respectively. We find that the post-test performance of physics graduate students who utilized the hybrid QuILT on conceptual questions, on average, was better than those who utilized the conceptual QuILT. For undergraduates, the results showed differences for different classes. One possible interpretation of these findings that is consistent with our framework is that integrating conceptual and quantitative aspects of physics in research-based tools and pedagogies should be commensurate with students’ prior knowledge of physics and mathematics involved so that students do not experience cognitive overload while engaging with such learning tools and have appropriate opportunities for metacognition, deeper sense-making, and knowledge organization. In the undergraduate course in which many students did not derive added benefit from the integration of conceptual and quantitative aspects, their pre-test performance suggests that the traditional lecture-based instruction may not have sufficiently provided a “first coat” to help students avoid cognitive overload when engaging with the hybrid QuILT. These findings suggest that different groups of students can benefit from a research-based learning tool that integrates conceptual and quantitative aspects if cognitive overload while learning is prevented either due to students’ high mathematical facility or due to their reasonable conceptual facility before engaging with the learning tool. Full article

Predicting future events is crucial for temporal reasoning, providing valuable insights for decision-making across diverse domains. However, the intricate global interactions and temporal–causal relationships at the document level event present significant challenges. This study introduces a novel document-level future event prediction method that integrates an event knowledge graph and a large language model (LLM) reasoning framework based on metacognitive theory. Initially, an event knowledge graph is constructed by extracting event chains from the original document-level event texts. An LLM-based approach is then used to generate diverse and rational positive and negative training samples. Subsequently, a future event reasoning framework based on metacognitive theory is introduced. This framework enhances the model’s reasoning capabilities through a cyclic process of task understanding, reasoning strategy planning, strategy execution, and strategy reflection. Experimental results demonstrate that the proposed approach outperforms baseline models. Notably, the incorporation of the event knowledge graph significantly enhances the performance of different reasoning methods, while the proposed reasoning framework achieves superior performance in document-level future event prediction tasks. Furthermore, the interpretability analysis of the prediction results validates the effectiveness of the proposed method. This study advances research on document-level future event prediction, highlighting the critical role of event knowledge graphs and large language models in temporal reasoning. It offers a more sophisticated future event prediction framework for government management departments, facilitating the enhancement of government safety management strategies. Full article

The integration of artificial intelligence (AI) into education is a defining challenge for achieving a sustainable digital future. This study addresses this challenge by exploring the psychological foundations necessary for teacher readiness, framing this preparation as a matter of social sustainability for the teaching profession. Employing a correlational research design, this study investigates the relationships among key psychological constructs as perceived by pre-service educators. Specifically, it examines how pre-service preschool teachers’ self-reported levels of self-regulation and social-emotional expertise relate to their self-assessed AI—Technological Pedagogical Content Knowledge (AI-TPACK). The findings were revealing: multiple linear regression analyses confirmed perceived self-regulation as a robust predictor of the self-assessed core and composite knowledge elements of AI-TPACK. Counterintuitively, social-emotional expertise did not show a significant correlation with any aspect of AI-TPACK. This suggests that the metacognitive skills inherent in self-regulation are fundamental for empowering educators to engage in the lifelong learning required for a sustainable career. Therefore, teacher education programs must strategically cultivate these skills to foster a resilient teaching workforce, capable of ethically shaping the future of AI in inclusive and sustainable learning environments. Full article

Background: Physical activity, metacognitive awareness, and empathy are interconnected factors that play a significant role in the overall well-being of university students. Together, these elements contribute to the development of more self-aware, emotionally intelligent, and academically engaged students. The aim of this study is to explore the relationship between physical activity, empathy, and metacognition among physiotherapy students, as well as determining whether differences exist between undergraduate and graduate students. Methods: A cross-sectional study evaluated 468 physiotherapy students using the International Physical Activity Questionnaire—short version, the Metacognitive Awareness Inventory, and a shortened version of the Empathy Quotient supplemented with demographic questions. The respondents were students of undergraduate and graduate studies of physiotherapy, were male and female, and were between the ages of 18 and 25 years. Results: Higher levels of vigorous-intensity physical activity, walking, and total physical activity are significantly associated with increases in Declarative Knowledge (p = 0.000; p = 0.001; p = 0.000), Empathy Quotient (p = 0.029; p = 0.000; p = 0.006), and Cognitive Empathy (p = 0.002; p = 0.000; p = 0.001). Undergraduate students demonstrated higher levels of Declarative Knowledge (p = 0.000), whereas graduate students scored higher in Procedural Knowledge (p = 0.020), Planning (p = 0.000), Information Management Strategies (p = 0.000), and Evaluation (p = 0.005). Undergraduate students demonstrated higher overall empathy, cognitive empathy, and social skills (p = 0.000). Conclusions: This is the first study to examine this issue in the context of physiotherapy students. Our findings highlight the importance of creating integrated programs that promote physical activity, metacognitive awareness, and empathy concurrently among physiotherapy students. Enhancing metacognitive skills through targeted educational strategies helps students strengthen their critical thinking and self-regulation, enhance academic outcomes, and better prepare students for their professional role. Full article

Background: Unlike medical training, science teacher training in Brazil does not include PBL as a curricular methodology. However, there is a Pedagogical Residency Program (PRP) that allows teaching experiences that are different from those provided in the undergraduate course. Thus, in this research, we propose a formative intervention in PBL for scholarship holders in the Pedagogical Residency Program (hereinafter Residents), aiming to answer the following question: “What are the perceptions of pre-service teachers about the planning, implementation, and evaluation of a PBL intervention in physics teaching?”. Methods: Five Residents taught an elective course specially designed for the application of PBL to teach secondary school physics. The training of the Residents in PBL occurred almost simultaneously with the offering of the elective subject. To reveal their perceptions, we collected Residents’ teaching plans, problem scenarios, and reflective analyses. Results: The results demonstrate that the Residents encountered several difficulties in developing and implementing the PBL methodology when teaching physics. Regarding development, the difficulties lie in coherently aligning the learning objectives with the highly complex active methodology of PBL. In addition, another clear difficulty is developing a problem situation appropriate to the knowledge that one wishes to develop. During the intervention, the Residents realized how difficult it is to implement PBL to allow students to develop skills and knowledge in a reflective way. Conclusions: The results indicate that PRP is necessary to develop methodologies such as PBL, as it allows supervision and reflection on practice. However, we also observed that the results point to the urgent need to introduce PBL in the initial training of science teachers; this process can be established in three stages: strategically studying lesson planning for the implementation of PBL, developing problem situations that align with the knowledge that one wishes to develop, and developing metacognitive regulation and argumentation skills to conduct interventions based on PBL. Full article

The integration of gamification into digital learning environments is reshaping educational models, advancing towards more adaptive and personalized teaching evolution. However, within large Chinese corpora, the transition mechanism from passive participation to adaptive gamified learning remains underexplored in a systematic manner. This study fills this gap by utilizing LDA topic modeling and sentiment analysis techniques to delve into user comment data on the Bilibili platform. The results extract five major themes, which include multilingual task-driven learning, early-age programming thinking cultivation, modular English competency certification, cross-domain cognitive integration and psychological safety, as well as ubiquitous intelligent educational environments. The analysis reveals that most themes exhibit highly positive emotions, particularly in applications for early childhood education, while learning models that involve certification mechanisms and technological dependencies tend to provoke emotional fluctuations. Nevertheless, learners still experience certain challenges and pressures when faced with frequent cognitive tasks. In an innovative manner, this study proposes a theoretical framework based on Self-Determination Theory and Connectivism to analyze how motivation satisfaction drives cognitive restructuring, thereby facilitating the process of adaptive learning. This model demonstrates the evolutionary logic of learners’ cross-disciplinary knowledge integration and metacognitive strategy optimization, providing empirical support for the gamification learning transformation mechanism in China’s digital education sector and extending the research framework for personalized teaching and self-regulation in educational technology. Full article

A primary expectation of college is that students in all majors and disciplines will learn content, skills, and knowledge that support individual growth, job placement, or continued academic endeavors. In short, being a student implies an expectation to learn. Effective learning directly impacts student academic success, and this success has downstream effects on student retention and graduation rates. However, the process of learning is often taken for granted, and, too often, student learning is not successful because students have not received any guidance on the methods of effective learning. Across higher education, students are often left on their own to learn about learning, and their improvised methods frequently involve ineffective techniques such as cramming for exams or rereading assigned materials without deeper engagement. To counter such observations, the University of Iowa implemented a campus-wide learning framework, Learning at Iowa. The initiative is grounded in empirically validated practices from the cognitive and learning sciences, which have been organized around the Three Ms: mindset, metacognition, and memory. This article briefly reviews the relevant literature supporting each of the Three Ms and then discusses the implementation of the framework with students, student-facing staff, and instructors and how the framework supports effective educational practices. Full article

Metacognition and self-regulated learning are widely understood to offer significant benefits for pupils’ mathematical problem solving; however, the existing literature highlights that the under-representation of these concepts in curriculum, policy, and teacher professional development means that their potential for impact remains unfulfilled. This article, therefore, examines the potential value of an innovative fading professional development programme—“Stepping Stones”—in enhancing teachers’ understanding and use of metacognitive strategies for mathematical problem solving. Adopting a convergent mixed methods design, this pilot evaluation involved Year 2 teachers across five primary schools. The results from both qualitative and quantitative data demonstrate that, as the scaffolding provided by programme materials faded and teachers assumed greater responsibility for session planning, they incorporated metacognitive strategies into their planning and delivery with increased independence. The results also indicate the acceptability of this professional development model, suggesting that, when combined with peer collaboration, the fading design was associated with improvements in knowledge and confidence regarding both metacognition and mathematical problem solving, alongside increased ownership and buy in. The conclusions advocate further examination and implementation of fading models of professional development to promote the understanding and use of metacognition for mathematical problem solving and recommend exploration into different professional development contexts. Full article

The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy outcomes within a cultural–historical activity system. The agency competence assessments yielded four profiles of student agency, ranging from fully empowered to largely disempowered. The cluster membership explained significant additional variance in AI literacy scores, supporting the additive empowerment model in an AI-rich vocational education and training context. The predictive modeling revealed that while self-efficacy, mastery-oriented motivations, and metacognitive self-regulation contributed uniquely—though small—to improving AI literacy, an unexpectedly negative relationship was identified for internal locus of control and for behavioral self-regulation focused narrowly on routines, with no significant impact observed for grit-like perseverance. These findings underscore the importance of fostering reflective, mastery-based, and self-evaluative learning dispositions over inflexible or solely routine-driven strategies in the development of AI literacy. Addressing these nuanced determinants may also be vital in narrowing AI literacy gaps observed between diverse disciplinary cohorts, as supported by recent multi-dimensional literacy frameworks and disciplinary pathway analyses. Embedding autonomy-supportive, mastery-oriented, student-centered projects and explicit metacognitive training into AI curricula could shift control inward and benefit students with low skills, helping to forge an agency-driven pathway to higher levels of AI literacy among high school students. The most striking and unexpected finding of this study is that students with a strong sense of competence—manifested as high self-efficacy—can achieve foundational AI literacy levels equivalent to those possessing broader, more holistic agentic profiles, suggesting that competence alone may be sufficient for acquiring essential AI knowledge. This challenges prevailing models that emphasize a multidimensional approach to agency and has significant implications for designing targeted interventions and curricula to rapidly build AI literacy in diverse learner populations. Full article

With the emergence of AI technology, its adoption in higher education has become an interesting field for researchers. The present study explores the acceptance of AI for learning the English language by Pakistani EFL students using the UTAUT-2 and Metacognition theory. The UTAUT-2 questionnaire was adapted with minor changes to make it suitable for the EFL context. Data were collected from the English departments of the top ten general universities in Pakistan to make the findings generalizable. Another step taken to ensure generalizability was the sampling of 611 students randomly from both undergraduate (BS and ADP) and postgraduate (MPhil and PhD) programs studying in different semesters. PLS-SEM was employed for data analysis. In the first step, the PLS algorithm was run for the measurement model, which confirmed the reliability, validity, and fitness of the model. Second, the bootstrapping method was used for hypothesis testing. The findings reveal that six of the ten hypotheses for direct relationships are supported. Habit (0.489) was found to be the strongest contributor to BI, followed by PE (0.141), SI (0.100), and FC (0.093). Moreover, actual use behaviour was predicted by habit (0.325) instead of BI and FC. These findings are supported by metacognition theory, as the habit of AI seems to shape the metacognitive knowledge of EFL learners in place of traditional learning methods, and other factors seem to reinforce the metacognitive experience of using AI language. The study suggests implications for EFL experts, academia, and policymakers to strategically integrate AI into language learning by informing them of its potential benefits and risks. Full article

The pressing need to address climate change and environmentally related challenges highlights the importance of reimagining educational approaches to equip students with the skills required for innovation and sustainability. This study proposes a novel holistic pedagogic framework for architectural education that integrates biomimicry, systems thinking, and Bloom’s Revised Taxonomy to advance innovation, sustainability, and transformative learning. Developed through a triangulated methodological approach—combining reflective practitioner inquiry, design-based research, and conceptual model development—the framework draws from multiple theoretical perspectives to create a cognitively structured, interdisciplinary, and ecologically grounded educational model. Bloom’s Taxonomy provides a scaffold for learning progression, while the Function–Structure–Behavior (FSB) schema enhances the establishment of cross-disciplinary bridges to enable students to address complex design challenges. The framework is informed by insights from the literature and patterns observed in bio-inspired studios, student projects, and interdisciplinary workshops. These examples highlight how the approach supports systems thinking, ecological literacy, and ethical decision-making through iterative, experiential, and metacognitive learning. Rather than offering a fixed intervention, the framework is presented as a flexible, adaptable model that aligns learning outcomes with real-world complexity. It enables learners to navigate interdisciplinary knowledge, reflect critically on design processes and co-create regenerative solutions. By positioning nature as mentor, model, and measure, this pedagogic framework reimagines architectural education as a catalyst for sustainability and systemic change in the built environment. Full article

The use of advanced adaptive algorithms in Augmented Reality (AR) systems works to advance spatial skills with valuable relevance in many professional spheres by providing personalized feedback in an immersive environment. This study combines Bayesian Knowledge Tracing (BKT) and Human Plausible Reasoning (HPR) to design an AR system that can adapt to dynamic simulations with quantitative as well as qualitative cognitive methodologies. The system records a broad range of interactions from users, such as objects being rotated, changes in viewing perspective, and time spent on tasks, which are later analyzed through probabilistic updates with respect to skill building along with rule-based reasoning for determining behavioral patterns. Results from an in-depth case study show that the BKT module properly tracks improvement in spatial skills, while the HPR application highlights suboptimal approaches that hide underlying conceptual understanding. The adaptive system used then provides metacognitive hints that adjust by optimizing task difficulty levels, leading to improved student performance compared to standard non-adaptive AR techniques. Results show that using BKT and HPR in an AR environment not only enables accurate task performance but supports greater insight in approach strategies, leading to better and transferable spatial skills. Full article

When students choose their own learning strategies, they often rely on ineffective methods, such as rereading and cramming, which have limited long-term benefits. To improve learning outcomes, previous interventions have utilized explicit instruction about effective strategies and direct experience with those strategies, though with mixed success. Yüksel et al. demonstrated that combining both approaches could foster initial improvements in students’ understanding and use of effective learning strategies. In Study 1, we examined the long-term effects of this combined intervention by contacting participants six months later to assess the stability of outcomes. In Study 2, we extended the scope by surveying all students who had enrolled in the intervention section over the past five years. Participants were asked about their use and perceived effectiveness of various strategies. In both studies, quantitative measures were complemented with open-ended questions to gain deeper insights into study behaviors and obstacles to adopting effective strategies. While students retained an understanding of the effectiveness of various strategies and reported using ineffective strategies less frequently, the adoption of more effective strategies did not show a significant increase. However, compared to the business-as-usual group, the intervention group did not experience a decline in their use of effective strategies. These results suggest that while explicit instruction and experience can enhance knowledge, long-term behavior change remains difficult. Reported obstacles—such as time constraints, limited resources, procrastination, and prioritizing short-term gains—align with metacognitive theories of desirable difficulties and help explain why students still favor less effortful strategies, despite knowing more effective ones that require greater effort and delayed rewards. Full article