This study examines the challenges posed by artificial intelligence (AI) to traditional intellectual property (IP) frameworks, particularly in cases where creative or innovative outputs are generated without direct human authorship. As AI systems become increasingly capable of autonomous learning and content creation, they challenge the foundational legal assumption that authorship - and by extension, IP protection - is inherently human. The research critically explores whether existing IP laws can accommodate AI-generated works, the feasibility of recognizing AI as a legal subject, and the implications of attributing authorship or liability in such cases. Drawing on comparative legal analysis from civil law, common law, and selected Arab jurisdictions, the study highlights the inconsistencies and gaps in current legal systems. It advocates for the development of a specialized legal framework that reflects the realities of the digital age, including redefined standards of "innovation" and the implementation of the "responsible human proxy" doctrine. Ultimately, the study calls for a shift in legal philosophy - one that balances the need for legal certainty with the dynamic nature of technological advancement in the field of inteشllectual property.

This study explores the factors driving autonomous learning (AU) among undergraduate students in AI-enhanced education. It specifically examines the role of AI literacy (AI-L), critical thinking (CT), self-regulation (SR), and self-efficacy (SE). Data collected from Thai university students were analyzed using Structural Equation Modeling (SEM). The results show that AI-L demonstrated a strong and significant positive influence on all three mediating variables—SE (β = 0.99, t = 20.00), SR (β = 0.93, t = 18.53), and CT (β = 0.70, t = 7.30). SE exerted as the most powerful predictor of AU (β = 0.52, t = 6.38), while critical thinking had a smaller direct impact. The findings suggest that AI-L is a foundational competency that requires metacognitive support. Consequently, educators should utilize strategies like blended learning and reflective practice. These insights encourage a learner-centered approach to digital education, fostering future-ready, autonomous learners.

This article reports on the design, implementation, and evaluation of FACTY, a GPT-based chatbot developed to support undergraduate students in learning algebraic factoring. The study involved ten first-year engineering students who interacted with FACTY over four stages, including diagnostic and final assessments, autonomous practice, and post-intervention interviews. Guided by a framework of feedback levels in mathematics instruction, the analysis examines the nature of FACTY’s responses and their relationship to student learning outcomes. Findings indicate that the chatbot predominantly provides process-level and self-regulation-level feedback, both of which are acknowledged in the literature as critical for fostering deep and autonomous learning. Two case studies illustrate contrasting results: one student achieved substantial improvement, while another made limited progress due to foundational gaps in algebra. Interview data revealed positive perceptions of FACTY’s constant availability, adaptability, and nonjudgmental interaction style. The study concludes that FACTY can serve as an effective complement to classroom instruction, particularly when integrated with teacher oversight and used to promote critical engagement with AI-generated feedback.

Qualitative insights into EFL students’ use of ChatGPT-assisted autonomous learning for enhancing self-directed learning in Palestinian higher education

This study aims to explore the application effect of virtual assistants in deep English reading teaching in higher vocational colleges, so as to solve the current problems in teaching, such as rigid methods, insufficient students’ interest and ability, lack of resources, and flawed evaluation systems. The study constructs a four-dimensional driven English reading teaching model and systematically analyzes the application of virtual assistants through empirical research. The results show that this model significantly improves the academic performance of students in the experimental class, with a more concentrated score distribution and an increased proportion of high-scoring students; virtual assistants play a positive role in assisting vocabulary understanding, promoting preview and review, stimulating reading interest, and cultivating autonomous learning habits, with high-frequency users benefiting more significantly. However, there are problems such as unbalanced usage frequency and coverage, and insufficient functional adaptability. The study proposes hierarchical intervention strategies: expanding usage coverage, guiding autonomous learning in a hierarchical manner, and strengthening teaching intervention effects, providing an empirical basis and practical paths for virtual assistants to optimize deep English reading teaching in higher vocational colleges.

This community service activity addresses the persistent issue of low learning independence among children living in social welfare institutions, who often rely heavily on passive instructional methods and direct supervision. The primary objective is to enhance the self-regulated learning capacities of orphanage residents through structured project-based mentoring. The methodology employed a participatory service-learning approach involving thirty adolescents between the ages of twelve and fifteen. Data collection was conducted using self-reliance questionnaires, observation rubrics, and project outcome evaluations to measure shifts in attitudes and social-cultural dynamics. The findings indicate a significant transformation in the participants' learning behaviors, with a notable increase in time management skills and personal initiative. All participant groups successfully completed their assigned projects, ranging from digital content creation to simple environmental management, demonstrating a shift from dependence to proactive engagement. Furthermore, the initiative successfully established a new institutional framework for caregivers to facilitate more interactive and autonomous learning environments. In conclusion, the project-based mentoring model provides a robust solution for fostering autonomy and problem-solving skills in marginalized youth. While the program faced logistical challenges, its success suggests that empowering children with tangible responsibilities can effectively break the cycle of educational passivity. Future iterations of this program should explore the integration of digital platforms to sustain these gains and broaden the socio-economic impact for orphans transitioning to independent living.

Academic self-efficacy is a critical psychological construct influencing learners’ motivation, persistence, and achievement in foreign language learning; however, empirical evidence in secondary-level EFL contexts remains limited. This study investigated the effects of gamification on the academic self-efficacy of Grade 12 students learning English as a Foreign Language (EFL) in public secondary schools in Chonburi Province, Thailand. Employing a mixed-methods explanatory sequential design, the study first adopted a quasi-experimental pre-test–post-test approach, followed by focus group interviews to gain in-depth insights into students’ learning experiences. A total of 83 students participated, with 42 assigned to an experimental group receiving instruction integrated with Duolingo for Schools and 41 assigned to a control group receiving traditional instruction. Quantitative data were analyzed using paired-sample and independent-sample t-tests. The results revealed a statistically significant improvement in academic self-efficacy among students in the experimental group, with mean scores increasing from 2.85 (SD = 0.45) to 4.10 (SD = 0.35), while the control group obtained a lower post-test mean score of 3.40 (SD = 0.40) (p < .01). Qualitative findings corroborated the quantitative results, indicating that the gamified learning environment promoted engagement, motivation, and autonomous learning through features such as immediate feedback, progress tracking, and game-based challenges. The findings suggest that gamification, when systematically integrated into formal EFL instruction, can significantly enhance students’ academic self-efficacy. This study provides empirical support for the pedagogical value of gamified learning environments and offers practical implications for educators and policymakers seeking to strengthen learner engagement and psychological readiness in secondary-level EFL classrooms.

Instructors generally find it difficult to intuitively grasp learners' emotions and level of understanding. Therefore, there is an increasing need for individualized instruction that provides support tailored to the student's emotional state and level of understanding. To objectively assess learner states, AI-based learning support grounded in physiological indicators that interpret emotional changes is indispensable. This study estimates learner states in data science education based on emotional changes detected through conversation content and EQ. After state estimation, the study proposes a method that constructs an AI-based learning support model according to learner states. It identifies appropriate instructional strategies for instructors. Instructor shortages and the difficulty of providing individually optimized support have become apparent. This study collects conversational data and electrodermal activity to estimate learner states. A hidden Markov model (HMM) estimates learners’ internal states from the conversational behaviors of instructors and learners. This study conducts one-on-one tutoring sessions between an instructor and a learner to evaluate the effectiveness of the proposed method. The experimental results reveal three states: a trial and error state, a state of searching for understanding, and an initial state of reaching understanding. The above results indicate that learner states can generally be estimated from conversation content. The study also constructs a Random Forest model based on the estimated learner state and conversation content. The F1 score is 0.824, enabling the identification of key features strongly associated with each learning state. Furthermore, the study fine-tunes a BERT model using utterance-level dialogue data annotated with state labels to classify learner states. The F1 score of 0.9015 indicates high accuracy, demonstrating the model's ability to accurately estimate the learner's state based on conversation content. Analyzing the state transitions, the study can help instructors decide on effective teaching methods. The findings obtained in the study hold promise for enhancing the quality of individualized instruction and as a model for autonomous learning support through AI agents.

ABSTRACT The online interactive learning environment provides learners with a more autonomous and personalized learning pattern, while the forgettable causal relationships might associate various factors of dynamic and sustainable interactive learning processes. For learning effectiveness, the forgettable causal relationships are not only potential risks, but also contain implicit data values. This study obtains the learning behavior instances generated by one interactive learning environment, flexibly constructs a novel diagnostic analysis process for forgettable causal relationships, and evaluates the learning trend through the unified calculation of attributes, features, and learning behavior routing. Sufficient experiments have proved this analysis process is feasible and reliable, key values from massive data are mined to guide and improve learning behaviors, which unifies the learning needs, learning tasks, learning contents, and learning interests, as well as different learning preferences. Then the forgettable causal relationships might be adaptively transformed into potential self-awareness and positive attitudes, from “Passive Learning” to “Proactive Learning”, which has important practical significance for guiding the adaptive learning motivation, as well as optimizing the learning method and learning attention, and the flexible enhancement mechanism of forgettable causal relationships is explored to form the learning behavior routes and improving learning effectiveness.

The Flipped Classroom methodology has emerged as an innovative strategy that redefines the traditional teaching dynamic by empowering students with a more active role in their learning process. This review aims to examine the key benefits of the flipped classroom, particularly in relation to promoting autonomous learning, healthy habits, and physical education. Through a systematic analysis of academic literature published across various academic databases between 2015 and 2025, the findings suggest that the Flipped Classroom model facilitates more interactive and personalized learning, enhancing the understanding of concepts and encouraging more active student participation in their education. Furthermore, this approach promotes the development of essential skills such as autonomy, critical thinking, and self-regulation, while also increasing motivation to learn. In disciplines such as physical education and sports, the Flipped Classroom contributes to the assimilation of theories related to health and physical performance, enabling students to apply this knowledge practically and effectively. However, challenges were also identified, such as the need for proper preparation and use of educational materials, equitable access to the necessary technologies, and teacher training. In conclusion, when implemented properly, the Flipped Classroom has the potential to transform learning by not only promoting academic knowledge but also fostering the development of healthy habits and life skills.

This study extends the Theory of Planned Behavior (TPB) to explore how students’ behavioral intentions toward using generative artificial intelligence (GenAI) are associated with their reflective engagement and self-directed learning (SDL) in higher education. As GenAI tools such as ChatGPT increasingly mediate learning, understanding how learners’ intentions are linked to autonomous and reflective learning behaviors becomes essential. Data were collected from 149 first-year university students (predominantly female) in Vietnam who had prior experience with GenAI for academic purposes. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study examined relationships among attitudes, subjective norms, perceived behavioral control, behavioral intention, actual use, reflection, and two dimensions of SDL, including intentional learning and self-management. The results reveal that attitudes and perceived behavioral control significantly predict students’ intentions and actual use of GenAI, whereas subjective norms have no significant effect. Behavioral engagement is positively associated with reflection and both dimensions of SDL, while reflection is positively related to intentional learning and self-management, confirming its mediating role within the proposed model linking motivation-related constructs with autonomous learning outcomes. These findings highlight reflection as a metacognitive mechanism that links students’ behavioral engagement with GenAI and their SDL-related outcomes. Theoretically, the study advances TPB by positioning reflection and SDL as outcome constructs within the proposed model, rather than fixed learner traits. Practically, it suggests that educators and institutions working with first-year university students or similar learner populations should integrate reflective activities and AI literacy into curricula to promote critical, ethical, and autonomous engagement with GenAI. Designing learning environments that position AI as a reflective partner, rather than merely a content generator, supports learners’ self-regulation and reflective engagement. Overall, this research contributes to understanding how intentional and reflective interaction with GenAI is associated with deeper and more autonomous learning of students among first-year university students in a GenAI-supported learning context.

To address the predicament in cultivating college students' autonomous learning ability in basic mathematics courses amid the digital transformation of higher education, this study constructs a multi-dimensional influencing factor model based on Bandura's tripartite reciprocal determinism theory and Zimmerman's self-regulated learning theory. An empirical analysis is conducted using structural equation modeling (SEM) with 595 valid questionnaires as samples. The results show that teacher support and digital literacy are core driving factors: they not only directly and positively affect autonomous learning ability but also exert indirect effects through the chain mediating path of "mathematical self-efficacy → learning strategies and self-regulation". Neither the direct nor indirect effects of the learning environment are significant. The conclusions deepen the application of autonomous learning theory in disciplinary contexts and provide theoretical references for colleges and universities to implement targeted teaching interventions and optimize basic mathematics courses.

This article examines the theoretical and practical foundations for fostering giftedness and supporting scientific research activities among students within STEM education. It analyzes factors influencing learning effectiveness, emphasizing the integration of cognitive, technological, and pedagogical components that develop systematic thinking, problem-solving, and interdisciplinary competencies. Special attention is given to semantic networks as tools for structured knowledge representation, enabling students to organize information, enhance analytical and critical thinking, and evaluate research data effectively. A STEM-oriented model for supporting gifted students’ research activities is proposed, including personalized learning trajectories, task adaptation to individual cognitive and motivational characteristics, and integration of modern digital services. The model also emphasizes independent research, collaborative problem-solving, and project-based learning to stimulate creativity and deepen understanding of complex concepts. The research highlights technological approaches and digital tools that engage students with educational objects and research processes, including robotics kits, virtual and augmented laboratories, cloud platforms, IoT sensors, cognitive services, and knowledge graphs. These tools provide handson interaction, support data collection and analysis, and foster autonomous learning and interdisciplinary thinking. Research support tools are designed using semantic networks to model knowledge and research processes, combined with digital technologies promoting self-directed exploration and analytical reasoning. The proposed approaches enhance students’ scientific competence, develop innovative potential, and build transferable skills necessary for addressing complex real-world problems. Overall, the study offers a practical framework for implementing STEMbased strategies that nurture giftedness and research skills, preparing students for academic and professional success in a knowledge-driven society.

The rapid expansion of digital education in the 21st century has positioned Virtual Reality Learning Environments (VRLEs) as promising spaces for fostering greater learner autonomy. As immersive technologies become more accessible and pedagogically versatile, they offer students opportunities to regulate their learning processes, experiment in interactive scenarios, and progress at their own pace. This review examines how autonomous learning has been conceptualized and investigated within VRLE research through a comprehensive bibliometric analysis of studies published between 2000 and 2025. The results reveal a research field shaped by two major orientations: one focused on human and pedagogical dimensions (learner diversity, instructional design, and evidence-based strategies) and another on technological innovation (artificial intelligence, machine learning, and simulation-based systems). Topic analyses show that digital and immersive education dominate current scholarly production, while areas directly related to autonomy, personalized learning, and student-centered methodologies remain comparatively less developed. Accordingly, it is crucial to reinforce pedagogical structures that enable autonomous learning in VR environments and to integrate technological advancements in a manner that translates into tangible improvements in educational quality across different settings.

With the rapid development of artificial intelligence technology, AI is gradually penetrating into students’ learning and daily lives, exerting a profound impact on learning methods and concepts. Taking the actual usage scenarios of student groups as the entry point, this paper analyzes the application of AI in knowledge acquisition and understanding, the improvement of autonomous learning ability, and the construction of human-machine collaborative learning models. The research holds that the rational application of AI technology helps alleviate learning difficulties, improve learning efficiency, and enhance students’ sense of subjectivity in learning. At the same time, the application of AI has also promoted the transformation of students’ role cognition and learning concepts to a certain extent. On the basis of affirming its practical value, this paper conducts necessary reflections on relevant issues, aiming to provide a reference for the scientific application of AI technology among student groups.

Substation robots face significant challenges in path planning due to the complex electromagnetic environment, dense equipment layout, and safety-critical operational requirements. This paper proposes a path planning algorithm based on deep reinforcement learning enhanced by ant colony optimization, establishing a synergistic optimization framework that combines bio-inspired algorithms with deep learning. The proposed method addresses critical path planning issues in substation inspection and maintenance operations. The approach includes: 1) designing a pheromone-guided exploration strategy that transforms environmental prior knowledge into spatial bias to reduce ineffective exploration; 2) establishing a high-quality sample screening mechanism that enhances Q-network training through ant colony path experience to improve sample efficiency; 3) implementing dynamic decision weight adjustment that enables gradual transition from heuristic guidance to autonomous learning decisions. Experimental results in complex environments demonstrate the method's superiority. Compared to state-of-the-art baselines including PPO, DDQN, and A*, the proposed method achieves 24% higher sample efficiency, 18% reduction in average path length, and superior dynamic obstacle avoidance. Field validation in a 2,500-square-meter substation confirms a 14.8% improvement in task completion rate compared to standard DRL approaches.

Background Generation Alpha (born after 2010) requires pedagogical approaches aligned with their digital-native characteristics and Society 5.0 demands. Although digital integration advances in curriculum, many Indonesian vocational schools persist with teacher-centered methods that prioritize technical skills while neglecting essential soft skills teamwork, creativity, and adaptability. This study developed and validated the Alpha Generation Learning Style Scale (ALSS), the first instrument measuring Generation Alpha’s integrated learning preferences in digital vocational settings. Grounded in social constructivism, connectivism, heutagogy, and cybergogy, ALSS comprises four interconnected dimensions: Visual-Digital Learning (VDL), Collaborative Learning (CL), Self-Directed Learning (SDL), and Gamified Learning (GL). Methods Using Cantabrana et al.’s (2019) four-phase framework, the study executed expert validation (n=5), pre-testing (n=35), and empirical testing with 285 stratified vocational students using Classical Test Theory and Confirmatory Factor Analysis (CFA). Results ALSS demonstrated excellent content validity (S-CVI/Ave = 0.981; Aiken’s V > 0.85) and high internal consistency (Cronbach’s α = 0.96). CFA confirmed a strong second-order factor structure (CFI = 0.996; TLI = 0.996; RMSEA = 0.020; SRMR = 0.030), with all dimensions significantly loading onto ALSS (λ = 0.59–0.65, p < 0.001). Self-Directed Learning showed the strongest influence (0.645), followed by Collaborative Learning (0.640) and Visual-Digital Learning (0.627), explaining 35–42% of variance per dimension. Convergent validity (AVE = 0.821–0.864), discriminant validity, and composite reliability (CR > 0.96) were confirmed. Conclusions ALSS is a valid, reliable instrument capturing Generation Alpha’s technology-mediated learning identity. It provides an evidence-based diagnostic framework for personalizing Cooperative Project-Based Learning (Co-PjBL) and transforming Indonesian vocational education toward adaptive, student-centered methods. Integration with computational thinking skills including algorithmic thinking, decomposition, pattern recognition, and abstraction strengthens pedagogical effectiveness. This approach aligns TVET with Society 5.0 demands, enhancing preparation of digitally proficient, computationally literate, collaborative, and autonomous learners.

The Role of Generative AI in Enhancing or Undermining Autonomous Learning in Higher Education

This study examines university students' impressions of Duolingo as a gamified tool for improving English vocabulary acquisition. This study used a mixed-method methodology, integrating quantitative data from 58 first-semester students at Universitas Muhammadiyah Kalimantan Timur with qualitative insights derived from five interviews. The study seeks to examine Duolingo's impact on vocabulary acquisition, learner motivation, and autonomous learning. Findings suggest that students predominantly view Duolingo as stimulating and beneficial for vocabulary acquisition, facilitated by its interactive elements like points, streaks, and prompt feedback. Nonetheless, constraints were observed, including reliance on the internet, restricted classroom integration, and insufficient teacher engagement. Although Duolingo seems to promote motivation and autonomous learning, its effect on vocabulary acquisition is contingent upon context. These data indicate that Duolingo serves more effectively as an ancillary learning resource rather than a substitute for classroom education

This paper proposes a reinforcement learning–based approach toward intelligent supply chains capable of adaptive and data-driven decision-making in dynamic environments. Traditional supply chain optimization methods often rely on static assumptions and predefined rules, which limit their effectiveness under uncertainty. In contrast, the proposed framework models supply chain operations as a sequential decision-making process, allowing an agent to learn optimal policies through continuous interaction with the environment. Key operational components, including inventory control, transportation planning, and demand fulfillment, are integrated into a unified reinforcement learning model. Simulation-based experiments demonstrate that the proposed approach outperforms conventional optimization and rule-based methods in terms of total operational cost, service level, and system adaptability. The results indicate that reinforcement learning provides a promising foundation for building intelligent supply chains that can autonomously respond to changing operational conditions. Each supply chain entity, such as suppliers, warehouses, and distributors, is modeled as an autonomous learning agent that interacts with other agents and the shared environment. Through cooperative learning, the agents gradually develop coordinated policies that improve overall system performance. The proposed approach addresses the limitations of centralized decision-making by enabling decentralized yet coordinated control. The learning-based framework enables supply chain systems to adapt decisions dynamically without explicit mathematical modeling of uncertainties. Experimental results obtained from simulated logistics scenarios show that deep reinforcement learning significantly improves decision quality compared to traditional heuristics, particularly in volatile environments.