The integration of technology into physical education (PE) has grown significantly in recent years, aiming to enhance student engagement, learning outcomes, and long-term adherence to physical activity. This study aims to critically examine the impact of technology-enhanced pedagogy in PE by reviewing the latest research on digital tools used in teaching and learning. Specifically, it explores how these technologies affect student engagement, knowledge retention, motor skill development, and self-regulated learning. Additionally, it seeks to identify potential barriers and strategies for optimizing their implementation in PE curricula. This study conducts a literature review, applying thematic analysis to categorize findings into key areas such as wearable technology, gamification, virtual and augmented reality, and artificial intelligence in physical education. Focused on the literature published between 2010 and 2025, it was conducted using academic databases such as PubMed, Scopus, and Web of Science. Finally, 151 articles were selected involving data extraction and qualitative synthesis of findings. Inclusion criteria encompassed peer-reviewed empirical studies, systematic reviews, and meta-analyses that investigated the application of technology in PE. The findings suggest that integrating technology into PE enhances motivation, engagement, motor skills, tactical understanding, and cognitive learning. However, issues such as digital inequality, lack of teacher training, and ethical concerns regarding student data collection pose significant challenges to widespread adoption. Also, this review identifies critical gaps and ethical considerations in the use of technology in PE, offering practical recommendations for its effective integration. Technology-enhanced pedagogy in PE offers numerous advantages, but its success depends on proper implementation, adequate teacher training, and equitable access to resources. However, this study is limited by the variability of methodologies across the reviewed studies, which may impact the generalizability of the findings.

Background: Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into sport and exercise through wearable biosensing systems that enable continuous monitoring and data-driven training adaptation. However, their practical value for coaching depends on the validity of biosensor data, the robustness of analytical models, and the conditions under which these systems have been empirically evaluated. Methods: A structured narrative review was conducted using Scopus, PubMed, Web of Science, and Google Scholar (2010–2026), synthesising empirical and applied evidence on wearable biosensing, signal processing, and ML-based adaptive training systems. To enhance transparency, an evidence map of core empirical studies was constructed, summarising sensing modalities, cohort sizes, experimental settings (laboratory vs. field), model types, evaluation protocols, and key outcomes. Results: Evidence from field and laboratory studies indicates that wearable biosensors can reliably capture physiological (e.g., heart rate variability), biomechanical (e.g., inertial and electromyographic signals), and biochemical (e.g., sweat lactate and electrolytes) markers relevant to training load, fatigue, and recovery, provided that signal quality control and calibration procedures are applied. ML models trained on these data can support training adaptation and recovery estimation, with improved performance over traditional workload metrics in endurance, strength, and team-sport contexts when evaluated using athlete-wise or longitudinal validation schemes. Nevertheless, the evidence map also highlights recurring limitations, including sensitivity to motion artefacts, inter-session variability, distribution shift between laboratory and field settings, and overconfident predictions when contextual or psychosocial inputs are absent. Conclusions: Current empirical evidence supports the use of AI-driven biosensor systems as decision-support tools for monitoring and adaptive training, but not as autonomous coaching agents. Their effectiveness is bounded by sensor reliability, appropriate validation protocols, and human oversight. The most defensible model emerging from the evidence is human–AI collaboration, in which ML enhances precision and consistency in data interpretation, while coaches retain responsibility for contextual judgement, ethical decision-making, and athlete-centred care.

Background/Objectives: The global aging population faces rising rates of cognitive decline and neurodegenerative disorders. This review explores how physical exercise influences brain health in aging, focusing on mechanisms, moderators, and personalized strategies to enhance cognitive resilience. Methods: A narrative review methodology was applied. Literature published between 2015 and 2025 was retrieved from PubMed, Scopus, and Web of Science using keywords and MeSH terms related to exercise, cognition, neuroplasticity, aging, and dementia. Inclusion criteria targeted peer-reviewed original studies in humans aged ≥60 years or aged animal models, examining exercise-induced cognitive or neurobiological outcomes. Results: Evidence shows that regular physical activity improves executive function, memory, and processing speed in older adults, including those with mild impairment or genetic risk (e.g., APOE ε4). Exercise promotes neuroplasticity through increased levels of BDNF, IGF-1, and irisin, and enhances brain structure and functional connectivity. It also improves glymphatic clearance and modulates inflammation and circadian rhythms. Myokines act as messengers between muscle and brain, mediating many of these effects. Cognitive benefits vary with exercise type, intensity, and individual factors such as age, sex, chronotype, and baseline fitness. Combined interventions—physical, cognitive, nutritional—show synergistic outcomes. Digital tools (e.g., tele-exercise, gamification) offer scalable ways to sustain engagement and cognitive function. Conclusions: Physical exercise is a key non-pharmacological strategy to support cognitive health in aging. It acts through diverse systemic, molecular, and neurofunctional pathways. Tailored exercise programs, informed by individual profiles and emerging technologies, hold promise for delaying or preventing cognitive decline.

Artificial intelligence (AI) is transforming higher education, although most research addresses its integration in terms of frequency of use or technological acceptance, without examining how it translates into specific curricular and instructional decisions. That is why this study has a dual aim: to develop and validate the AI Instructional Design Questionnaire for Critical Thinking (AID-CTQ) and to analyze how university faculty integrate AI into instructional design practices in higher education. The sample included 144 faculty members from a university in Madrid, selected by convenience. Exploratory and confirmatory factor analyses of the questionnaire supported a three-factor structure: Activity Design (F1), Critical Thinking Assessment (F2), and Self-Regulation and Reflection (F3). The final 12-item model shows good model fit (CFI = 0.98, TLI = 0.98, RMSEA = 0.05, SRMR = 0.05) and adequate overall reliability (α = 0.86). At the item level, responses related to assessment and reflective practices showed consistently high agreement, whereas items linked to activity design displayed greater variability. Faculty members with more than 10 years of experience obtained significantly higher scores, indicating that the educational value of AI depends less on the tools used and more on the quality of instructional decisions. Reported use of AI was high, with ChatGPT and Copilot being the most frequently used tools. Overall, the findings indicate that the integration of AI in higher education is evolving from predominantly instrumental uses toward more pedagogical and curriculum-oriented forms of implementation. Accordingly, the educational value of AI lies less in the tool itself than in the quality of the instructional decisions through which it is meaningfully embedded in the curriculum.

This chapter explores the role of artificial intelligence (AI) in transforming Physical Education (PE), highlighting its potential to personalize learning, enhance motor skill development, and improve motivation. AI tools such as motion analysis systems, virtual trainers, and gamified applications offer real-time feedback and individualized instruction, increasing student engagement and performance. Empirical evidence shows improvements in fitness, technique accuracy, and motivational climate when using AI-driven platforms. However, challenges remain, including teacher preparedness, ethical concerns about data use, and digital inequality. The chapter argues for a responsible and inclusive integration of AI in PE through training, infrastructure, and ethical guidelines, while emphasizing the need for further research into its long-term impact

Artificial intelligence (AI) is commonly used in university academic work, yet existing instruments mainly assess AI literacy, readiness, or levels of permitted use rather than the critical-thinking processes students activate when working with AI. This study developed the Critical Thinking with AI Scale for Students (CTAI-S) and provided initial psychometric evidence for its use. An initial 15-item questionnaire was administered to 271 university students, and one item was removed after item analysis. Parallel analysis favored one predominant factor (KMO = 0.927), and a theory-guided three-factor representation was also examined. Ordinal confirmatory factor analyses (WLSMV; n = 257) yielded scaled CFI = 0.964 and RMSEA = 0.072 for the one-factor model, and CFI = 0.977 and RMSEA = 0.059 for the equivalent correlated-three-factor and second-order models; robust indices were less favorable, and latent correlations were very high (0.85–0.88). Total-score reliability was high (ω = 0.898), whereas the third grouping showed low reliability (ω = 0.557). Mean responses were above the scale midpoint (M = 3.97), with Reflective Judgment and Self-Regulation being highest (M = 4.07). The CTAI-S is best interpreted as a global self-report measure of critical thinking in AI-mediated academic tasks; independent validation is needed before dimensional or diagnostic applications.