In the context of high-quality educational development, the scientific and objective evaluation of educational service quality has become a key concern in educational governance and management. Traditional evaluation approaches face limitations in indicator design, data sources, and result application, which restrict their ability to reflect the dynamic and multidimensional nature of educational service processes. With the deepening application of artificial intelligence in education, its strengths in multi-source data processing, pattern recognition, and intelligent analysis offer new possibilities for improving evaluation systems. Based on a systematic review of existing studies and the foundations of AI applications, this research constructs an educational service quality evaluation system centered on data integration and intelligent analysis. The study focuses on evaluation framework design, indicator system construction, and evaluation model development, and examines how artificial intelligence supports comprehensive assessment and result feedback. A case study is conducted to validate the feasibility and effectiveness of the proposed system. The findings demonstrate that artificial intelligence can enhance the scientific rigor, dynamic adaptability, and practical value of educational service quality evaluation, contributing to the modernization of educational governance.

The emergence of the Fourth Industrial Revolution has created opportunities for the development of platforms to be integrated into English language learning (ELL). Appearing in late 2022, ChatGPT has gained widespread popularity due to its usefulness across all fields. This study investigates how ChatGPT affects students when learning essay writing and students’ attitudes towards the implementation of such chatbots in learning essay writing. Data collection involved distribution of a questionnaire to 150 university students majoring in English language. Additionally, eight participants participated in a semi-structured interview to provide deeper insights. The findings revealed that students view the generation of ideas, the enhancement of coherence and cohesion, the expansion of vocabulary, the improvement of grammatical skills, and the acquisition of suitable ideas for abstract topics as key benefits of ChatGPT. Meanwhile, cheating, excessive dependency, and unreliable replies are amongst the challenges EFL students reported. while ChatGPT holds promise as a supplementary tool in EFL writing instruction, its use must be guided by appropriate pedagogical strategies to mitigate associated risks. The findings contribute to a growing body of research on artificial intelligence in language education and suggest directions for future studies across varied educational contexts.

Adolescent emotional well-being is increasingly recognized as a fundamental determinant of academic success and holistic development within secondary education. Concurrently, inclusive education paradigms mandate the creation of learning environments that actively support the diverse emotional, cognitive, and social needs of all students. This theoretical and policy-oriented paper examines the confluence of these two imperatives through the lens of Artificial Intelligence (AI)-supported pedagogy. It argues that AI, when conceptualized and implemented as a tool for inclusive pedagogical enhancement rather than a standalone technological solution, holds significant potential to scaffold teacher practice, personalize learning experiences, and foster systemic conditions conducive to student well-being. Drawing upon an integrative narrative review of literature from education, psychology, and learning sciences (2010–2025), this analysis explores the theoretical foundations of inclusive education and Universal Design for Learning (UDL), investigates AI's role in operationalizing these frameworks within teaching and learning processes, and underscores the critical importance of teacher mediation and professional capital. The paper further addresses the essential policy, ethical, and data governance structures required to steer AI integration towards equity and human flourishing. Ultimately, it posits that the future of emotionally supportive secondary education lies in synergistic ecosystems where AI augments the professional judgment of educators within ethically governed, inclusive school systems.

AI-Enabled Reform of University Physical Education Curriculum under Modern Pedagogical Paradigms: A Deep-Learning–Driven Intelligent Teaching Framework

ABSTRACT The increasing use of artificial intelligence (AI) in social work is enhancing teaching through innovative technologies and is already changing how we prepare students for the workforce. While AI offers significant potential to revolutionize social work practice and education, its integration requires careful attention to ethical considerations to ensure its responsible and equitable use. This study explores the role of AI in social work through structured interviews with 15 social work students, uncovering five key themes: the effect of AI on social work practice, policy, and research; its applications and utility in various settings; its role in enhancing efficiency; its integration into social work education; and its limitations within the field. The findings highlight students’ optimism and curiosity about AI’s potential to improve social work, while also holding some level of skepticism about the implications of AI algorithms for marginalized client groups. This study underscores the need for thoughtful adoption of AI technologies to support the welfare of vulnerable populations and advance social work education.

The Role of Artificial Intelligence in Periodontal Patient Education: A Narrative Review

ABSTRACT The application of artificial intelligence (AI) in early childhood education has been increasingly used to support preschool children's development. However, research indicates that preschool teachers often have limited awareness and literacy regarding AI, and studies focusing on improving their AI‐related knowledge remain insufficient. Therefore, enhancing preschool teachers' understanding of AI and supporting its effective integration into educational practices is critically important. This study aimed to examine the impact of an artificial intelligence education programme on preschool teachers' AI awareness. The study was conducted using a quasi‐experimental pre‐test–post‐test control group design and included a total of 48 preschool teachers, with 24 assigned to the experimental group and 24 to the control group. Teachers in the experimental group participated in artificial intelligence education applications, while no such interventions were implemented in the control group. Data were collected using measurement tools administered before and after the intervention. The findings revealed a statistically significant increase in artificial intelligence literacy among teachers in the experimental group compared to those in the control group. These results suggest that artificial intelligence–based educational interventions are effective in improving preschool teachers' awareness and understanding of AI, and they highlight the importance of incorporating AI education into professional development programmes for early childhood educators.

Abstract Background The rapid integration of artificial intelligence (AI) in higher education has shifted from voluntary engagement to mandatory usage, introducing psychological and emotional strain. Traditional technology adoption models often fail to address the challenges posed by compulsory digital environments, where system design flaws can lead to cognitive overload and student resistance. Objectives This study aims to develop and validate a student-centered framework that examines the influence of AI system design—specifically system reliability, task routinization, and information richness—on students’ negative technological experiences, and how these experiences subsequently affect techno-resistance and academic performance. Methods A quantitative research design was employed, using data collected from 229 undergraduate students across Malaysian universities. Exploratory factor analysis, correlation analysis, and multiple regression were used to examine relationships among key constructs and to validate the hypothesized model. Results Findings reveal that system reliability and information richness significantly reduce students’ negative experiences, while task routinization does not. Negative technological experience emerged as a key mediator, amplifying techno-resistance and decreasing academic performance. The results emphasize that the mandatory implementation of AI technologies does not automatically translate into improved educational outcomes. Conclusions This study reconceptualizes techno-resistance as an adaptive response to cognitive and emotional strain resulting from the poor design of AI systems in compulsory educational settings. It advances theoretical understanding by integrating digital dissonance with sociotechnical and cognitive load perspectives, and offers practical implications for the design of more student-aligned AI educational technologies.

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

This article investigates the transformations in teaching work in the face of the rise of generative artificial intelligence (GAI) and the new configurations of pedagogical practice in the digital context. Based on a bibliographic review supported by contemporary authors in the fields of education and technology, it seeks to understand how AI-based tools influence pedagogical practices, teaching and learning processes, and the interactions between teachers and students. It also examines the impact of digital culture on teachers’ professional identity and on the formative demands imposed by technological society. The results indicate that, although GAI does not replace the educator’s role, it redefines their functions, expanding the need for critical, ethical, and creative competencies for knowledge mediation. It is concluded that the effective and responsible integration of artificial intelligence in education depends on continuous teacher training and the appreciation of educators as central agents in the educational process.

The rapid expansion of artificial intelligence (AI) and digitalization in contemporary education has intensified global debates on sustainable education, frequently framed around efficiency, personalization, and technological innovation. At the same time, these developments have accelerated processes of technologization and commodification, raising concerns about the erosion of educational values and human-centered purposes. This tension calls for a critical reassessment of what sustainability should mean in AI-mediated educational contexts. The objective of this study is to examine under what conditions AI contributes to sustainable education as a value-based and human-centered project, and under what conditions it undermines it. Methodologically, the article adopts a qualitative, value-critical analysis of contemporary scholarly literature and policy-oriented debates, employing the distinction between sustainable education, sustainability in education, and education for sustainable development as a heuristic entry point within a broader theoretical dialogue. The analysis demonstrates that AI does not exert a uniform or inherently progressive influence on education. While AI can enhance access, personalization, and instructional support in ethically grounded and well-governed contexts, it may also intensify educational inequalities, reinforce the commodification of knowledge, weaken academic integrity, and marginalize the formative and human dimensions of education under market-driven and weakly regulated conditions. These dynamics are particularly visible in culturally and religiously grounded educational contexts, where AI reshapes epistemic authority and educational meaning. The study concludes that achieving sustainable education in the digital age depends not on AI adoption per se, but on subordinating AI and digitalization to coherent normative, ethical, and governance frameworks that prioritize educational purpose, social justice, and human dignity.

In the era of artificial intelligence, teachers role transformation is a core issue in teachers professional development under the background of educational digitalization, which is directly related to the quality of education and teachers own practical adaptation. Currently, teachers roles are shifting from mere knowledge transmitters to learning guides and technological collaborative constructors. Meanwhile, teachers are confronted with multiple challenges such as insufficient technical integration capabilities, vague role cognition, and lagging external support systems. To address these issues, at the individual level, teachers should enhance their digital skills and ethical awareness to improve their transformation competence; at the teaching practice level, efforts should be made to integrate technology empowerment with emotional education to construct a new teaching model; at the external support level, it is necessary to improve the training system and evaluation mechanism, and optimize the institutional environment for transformation. These measures aim to help teachers adapt to the new requirements of intelligent education and provide practical references for the digital transformation of education.

The rapid integration of ChatGPT into English language classrooms has introduced a troubling pedagogical shift in which learners increasingly outsource cognitive effort to artificial intelligence, bypassing processes of intellectual struggle, retention, and independent language development. This article critically examines how generative artificial intelligence, while often celebrated for accessibility and linguistic support, can foster learner dependency and undermine the development of sustained cognitive engagement. Drawing on extended classroom observations and reflective teaching practice within a United Arab Emirates higher education context, the study identifies recurring patterns of disengagement, superficial linguistic understanding, and a growing reluctance to grapple with language complexity. These behaviors are conceptualized through the notion of the Brat Brain, a metaphor used to describe a learner mindset that resists effort and critical thinking, demands instant solutions, and privileges convenience over intellectual growth. Rather than asking how to improve their writing or speaking, students increasingly question why such effort is necessary when artificial intelligence can produce faster and seemingly superior outputs. Reframing artificial intelligence use through the good, the bad, and the (educationally) evil, the article offers a critical lens for evaluating both the affordances and the unintended consequences of artificial fluency in language education. The study’s significance lies in its contribution to current debates on artificial intelligence in education by highlighting the pedagogical risks of uncritical adoption and arguing for a recalibration of instructional practices grounded in critical digital literacy, intellectual responsibility, and human-centered learning.

This study develops and validates an AI-driven Spanish reading and writing curriculum aimed at enhancing Chinese university students’ linguistic proficiency and critical thinking ability. Guided by the AI-Driven Learning (AIDL) model established in this research, the curriculum systematically integrates AI into pre-class, in-class, and post-class stages to promote personalized learning, multimodal engagement, and continuous feedback. The development of the curriculum involves expert review and content validation to ensure theoretical consistency and pedagogical feasibility. Five experts in Spanish language education and curriculum design evaluate the course objectives, content, methods, activities, and assessments. The results yield a high content validity index (S-CVI = .96), confirming the curriculum’s coherence, clarity, and alignment with CEFR B1-level descriptors. The study demonstrates how AI can function as an instructional facilitator at the curriculum level, providing a structured framework for the integration of artificial intelligence in Spanish language education. These findings offer practical guidance for future curriculum innovation and AI-driven language pedagogy in higher education.

This article introduces a framework for scientific and professional language training tailored for STEM (Science, Technology, Engineering and Mathematics) specialists, emphasising the integration of digital technologies and artificial intelligence (AI) in language education. The framework aims to develop students’ research communication skills and digital competencies, which are essential for effective participation in both national and international scientific discourse. The article discusses contemporary trends in STEM education, emphasising the importance of interdisciplinary approaches, project-based learning, and the utilisation of digital tools to boost language skills and scientific literacy. The article outlines the development and deployment of a digital platform aimed at supporting personalised and adaptive learning experiences, integrating various educational technologies and approaches. Empirical research conducted through a pedagogical experiment demonstrates the effectiveness of the framework, showing significant improvements in students’ academic and linguistic competencies across multiple modules. The findings highlight the importance of combining language training with STEM education to equip future engineers for the challenges of a globalised and digitalised professional world. This work reports on the “Enhancing Scientific and Professional Language Learning for Engineering Students in Kazakhstan through Digital Technologies” project conducted at Saginov Technical University (STU) in Kazakhstan and funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19678460). The research contributes to the ongoing discussion on improving language teaching in STEM fields, offering a framework that aligns with current educational demands and technological progress.

Algorithmic dependence and digital colonialism: a conceptual framework for artificial intelligence in education and knowledge systems of the Global South

The rapid adoption of generative artificial intelligence (AI) in higher education has intensified a pedagogical dilemma: while AI tools can increase immediate classroom engagement, they do not necessarily foster the self-regulated learning (SRL) capacities required for ethical and reflective professional practice, particularly in human-service fields. In this two-time-point, pre-post cohort-level (repeated cross-sectional) evaluation, we examined a six-week AI-integrated curriculum incorporating explicit SRL scaffolding among social work undergraduates at a Taiwanese university (pre-test N = 37; post-test N = 35). Because the surveys were administered anonymously and individual responses could not be linked across time, pre-post comparisons were conducted at the cohort level using independent samples. The participating students completed the AI-Enhanced Learning Attitude Scale (AILAS); this is a 30-item instrument grounded in the Technology Acceptance Model, Attitude Theory and SRL frameworks, assessing six dimensions of AI-related learning attitudes. Prior pilot evidence suggested an engagement regulation gap, characterized by relatively strong learning process engagement but weaker learning planning and learning habits. Accordingly, the curriculum incorporated weekly goal-setting activities, structured reflection tasks, peer accountability mechanisms, explicit instructor modeling of SRL strategies and simple progress tracking tools. The conducted psychometric analyses demonstrated excellent internal consistency for the total scale at the post-test stage (Cronbach’s α = 0.95). The independent-samples t-tests indicated that, at the post-test stage, the cohorts reported higher mean scores across most dimensions, with the largest cohort-level differences in Learning Habits (Cohen’s d = 0.75, p = 0.003) and Learning Process (Cohen’s d = 0.79, p = 0.002). After Bonferroni adjustment, improvements in the Learning Desire, Learning Habits and Learning Process dimensions and the Overall Attitude scores remained statistically robust. In contrast, the Learning Planning dimension demonstrated only marginal improvement (d = 0.46, p = 0.064), suggesting that higher-order planning skills may require longer or more sustained instructional support. No statistically significant gender differences were identified at the post-test stage. Taken together, the findings presented in this study offer preliminary, design-consistent evidence that SRL-oriented pedagogical scaffolding, rather than AI technology itself, may help narrow the engagement regulation gap, while the consolidation of autonomous planning capacities remains an ongoing instructional challenge.

ABSTRACT This article examines the perception of artificial intelligence (AI) in religious education, comparing the views of Catholic religion teachers in Germany and Poland. The analysis focuses mainly on generative AI, particularly large language models (LLMs) such as ChatGPT or Claude, which have recently transformed educational and communicative practices. As one of the most rapidly advancing technologies, generative AI evokes both hope and apprehension in the context of faith transmission, spiritual development and religious education. Quantitative research was conducted to identify current similarities and differences in the perception of AI in these two countries, and to determine the factors influencing the readiness of RE teachers ( n = 236) to incorporate AI into their teaching approaches. The results reveal ambivalence: while teachers recognize AI's potential to personalize teaching and engage students, they also highlight risks such as the oversimplification of religious content, ethical threats and the potential weakening of the spiritual dimension of religious education. Cultural and systemic differences influence the degree of AI acceptance, with digital competence and professional experience proving to be key determinants of openness. The authors make recommendations regarding teacher training and support, emphasizing the importance of consciously and critically integrating AI into religious education theory and practice.

To address the core issues of low accuracy, poor cultural adaptation, and insufficient efficiency in learning motivation prediction in cross-cultural Chinese second language acquisition scenarios, this paper proposes the ED-CM-MP model, which integrates dynamic sentiment recognition, cultural adjustment modeling, and lightweight temporal prediction. This model uses DistilBERT+Gated TCN to construct a dynamic sentiment module to extract temporal sentiment features, GraphSAGE to adjust for cross-cultural differences, and Temporal Fusion Transformer to achieve efficient motivation prediction. Experiments on the HSK and VIDAS cross-cultural datasets show that the model achieves the best core prediction performance: MAE of 0.28–0.29 and F1 Score of 0.91–0.92, representing a 10.2% improvement in accuracy compared to the best baseline model; inference latency as low as 38.5–39.2 ms and FLOPs of only 12.6–13.1 G, representing a 20.3% improvement in efficiency compared to MobileNetV3; and a cultural adaptation score of 0.94–0.95, representing a 21.8% improvement in cross-cultural generalization ability compared to U-Cast. Ablation experiments validated the necessity of the three modules working together; removing any module resulted in a performance decrease of 3.6%-7.2%. Stability tests showed that the model exhibited excellent robustness with performance fluctuations of ≤ 5.4% in a small sample scenario with 10% labeled noise and 2000 training samples. This research demonstrates that the ED-CM-MP model achieves a triple breakthrough in motivation prediction–accuracy, efficiency, and generalization–providing an efficient and feasible technical solution for intelligent teaching intervention in cross-cultural Chinese second language acquisition.

ABSTRACT This study aimed to develop and validate the Generative Artificial Intelligence Ethical Awareness and Responsibility Scale (GenAI-EARS) for use in educational contexts. A quantitative cross-sectional design was adopted, and the scale development process included item generation, pilot testing, exploratory factor analysis, confirmatory factor analysis, item discrimination analyses, and assessments of convergent, discriminant, and internal validity, as well as measurement invariance across gender. The analyses supported an 18-item, four-factor structure (autonomy, transparency, privacy, and fairness) with adequate model fit, strong item discrimination, and satisfactory reliability indices. Correlational findings indicated a significant negative relationship between GenAI-EARS scores and age, whereas no significant gender differences were observed. These results provide evidence that the GenAI-EARS is a psychometrically sound instrument for evaluating ethical awareness and responsibility related to the use of generative AI in education.