The growing preference for private schooling in India has intensified concerns regarding educational inequality and declining trust in public education. Drawing on Pierre Bourdieu's concept of cultural capital, this qualitative study explored why parents prefer private schooling despite free education. Interviews with ten parents across India revealed that parental preference was shaped by concerns regarding educational quality, disparity, unequal opportunities, and preparedness within public schooling systems. Parents’ choice was linked with anxieties regarding children's mobility and competitiveness. The study proposes the notion of “defensive school choice,” suggesting that parental migration toward private schooling reflected educational insecurity rather than unrestricted preference.

Foundational literacy underachievement in marginalized Indian schools emerges from interacting ecological barriers rather than isolated learner shortcomings. This exploratory qualitative study draws on in-depth interviews with 17 government elementary teachers to understand how foundational language literacy challenges unfold in daily English classroom practice. Teachers identified weak preschool readiness, limited home literacy exposure, low learner motivation, irregular attendance, curriculum–learner misalignment, resource shortages, and systemic pressures as key contributors to widening early literacy gaps. Guided by a deductively informed, literature-based sensitizing framework and refined through inductive thematic analysis, the study advances the Foundational Literacy Ecosystem Model (FLEM), which integrates five interdependent domains—home ecology, early childhood readiness, learner dispositions, school ecology, and system-level structures. FLEM demonstrates how these layers interact to sustain “literacy poverty” in disadvantaged contexts. The findings highlight the need for context-responsive early learning support, strengthened teacher capacity, home–school literacy partnerships, and systemic reforms to foster equitable FL outcomes.

This study presents the development and validation of the AI Literacy Questionnaire for Children (AILQ-C), a psychometrically robust instrument designed to assess elementary students’ multidimensional awareness, attitudes, and perceived competencies related to artificial intelligence. Grounded in UNESCO’s AI Competency Framework, the AILQ-C captures human-centred, ethical, practical, and system-design dimensions aligned with the framework’s developmental progression (Understand–Apply–Create), focusing on students’ perceptions and orientations toward these competencies. An initial 30-item pool, derived from theoretical review and expert input, underwent face and content validation, cognitive interviews, and pilot testing, resulting in a refined 24-item instrument. Field testing with 480 students (Grades 4–8; Mage = 11.2, SD = 1.1; 51

Purpose School well-being has become a central goal of contemporary educational reform, yet its realisation remains challenging in resource-constrained rural schools. Guided by UNESCO's Happy Schools Framework, this study explores how rural Indian elementary school teachers experience and negotiate school well-being within their everyday professional contexts. Design/methodology/approach An interpretivist qualitative design was employed. Semi-structured interviews were conducted with seven experienced elementary school teachers from rural schools in West Bengal, India. Data were analysed using reflexive thematic analysis to identify patterns across teachers' lived experiences and generate context-sensitive interpretations of school well-being. Findings Four interrelated themes emerged: relational strain and trust erosion, pedagogical rigidity, governance and psychological safety and aspirations for context-sensitive school well-being. The findings reveal constrained happiness, where governance pressures, organisational culture, limited teacher agency, and socio-economic challenges hinder school well-being. From these insights, the study proposes the Governance–Culture–Agency–Practice (GCAP) framework, demonstrating that supportive governance, collaborative culture, enhanced teacher agency and context-responsive pedagogy jointly create the organisational conditions necessary to strengthen school well-being in rural schools. Originality/value This study contributes a context-sensitive conceptual framework that extends the Happy Schools Framework by moving beyond the identification of happy school characteristics to explaining how school well-being may be strengthened through interacting institutional processes. The GCAP framework offers an organisational perspective that integrates governance, institutional culture, teacher agency and pedagogical practice, providing practical guidance for policymakers, school leaders, and teacher educators seeking to enhance school well-being in resource-constrained rural settings.

This study investigates Indian school teachers' AI literacy across cognitive, pedagogical, ethical, and contextual dimensions. Using an exploratory qualitative design, semi-structured interviews with 16 teachers from diverse school types and regions reveal that AI understanding is often superficial and tool-centric, with minimal hands-on exposure - especially in rural and government schools - indicating a critical cognitive-technical gap. Pedagogical use largely remains at substitution-level tasks (e.g. grading, content delivery), with little evidence of Substitution, Augmentation, Modification, and Redefinition (SAMR)-informed transformation. Ethical dilemmas - spanning data privacy, algorithmic bias, misinformation, and cultural misalignment - are heightened in settings lacking training and digital governance. Contextual disparities, including infrastructure, language accessibility, and regional policy support, further mediate engagement, with urban private schools showing relatively higher, though uneven, adoption. Findings highlight the need for equity-driven, culturally responsive, and ethically grounded professional development. Recommendations include embedding AI fundamentals and ethics into pre-service curricula, designing low-bandwidth multilingual training tools with simulations, piloting school-level data governance frameworks, and advancing regionally adaptive AI integration strategies - offering implications for other Global South contexts facing similar challenges.

This study explores teachers' perspectives on student absenteeism in India's elementary schools, with the goal of informing interventions and policies to tackle the issue of empty desks. It examines insights from 10 elementary teachers, gathered through in-depth interviews, focus group discussions, and observations. The findings highlight a concerning trend in students' school absenteeism, revealing alarmingly low daily attendance rates of just 25-30% in many state government-run schools, particularly in rural areas. In stark contrast, private and central government schools enjoy significantly higher student attendance rates. Despite government initiatives addressing historical socio-economic factors like poverty, communication, or distance, absenteeism persists primarily due to stakeholders' attitudinal integrity issues and a 'poverty of minds', including parental indifference, student demotivation, and falsified school reports. Teachers identified significant academic and behavioural impacts on students, alongside challenges in maintaining continuity and morale in classrooms. It reveals that addressing absenteeism requires a multi-dimensional approach, including school-level interventions, systemic reforms, parental engagement, awareness generation, incentivizing attendance, promoting extracurricular activities, flexible schooling, and ensuring stricter monitoring and accountability. The study underscores the necessity for collaborative efforts to transform schools into inclusive spaces, addressing absenteeism to ensure equitable access to quality education for students and society.

PurposeThe purpose of this study is to compare artificial intelligence (AI)-integration strategies in school education across China, Singapore, Finland and the USA, aiming to uncover shared patterns and localized innovations that could inform a globally responsive AI education framework.Design/methodology/approachThe qualitative desktop study draws on secondary data from five recent government policy documents: China's New Generation Artificial Intelligence Development Plan, Singapore's EdTech Masterplan 2030, Finland's Age of Artificial Intelligence, California's Computer Science Strategic Implementation Plan and Massachusetts' Digital Literacy and Computer Science Standards. These were analyzed using the SMART criteria and a researcher-constructed "Nine-point framework of operational components in AI policy for schools."FindingsDespite varying governance models and socio-cultural contexts, all four countries share a common intent to integrate AI into school education. Nine thematic propositions emerged: "SMART" policy design, balanced vision, curriculum and ethics integration, dynamic teacher training, equitable funding, multi-stakeholder partnerships, adaptive monitoring, localized implementation and contextual alignment. Finland and Singapore demonstrate strong ethical and human-centered policies, while China and the USA lean toward innovation and workforce development. Implementation remains challenged by equity gaps, teacher readiness and contextual mismatches.Research limitations/implicationsThese diverse models offer critical lessons: future global frameworks must prioritize ethical safeguards, localized adaptability, inclusive training and dynamic monitoring systems to ensure AI supports equity and relevance across school contexts.Originality/valueThis study offers original insights derived from systematic, comparative analysis of the national AIEd policies using a robust evaluative framework.

The human cognitive structure is very uncertain and ever-elusive to arrest. The purpose of this study was to formulate a mathematical model to evade response bias latent in the quantification process in any decision-making by applying intuitionistic fuzzy logic, potent in arresting uncertainties. Following this research aim, a sample problem was adopted from the school setting regarding the election of a class monitor based on an opinion survey among five teachers on a Likert scale. The numerical decision values were converted to intuitionistic fuzzy. Findings revealed a palpable difference between Likert values and their Fuzzified corresponding values wherefrom the authors empirically deduced that fuzzified result is more precise over the quantified Likert values considering respondents' biases, uncertainties, inter-rater agreements, or disagreements. Finally, the researchers proposed the intuitionistic fuzzy score function evolved in this study, needs to be investigated with a larger sample size to draw more authentication.

This PRISMA-guided systematic review examines how Artificial Intelligence (AI) supports the development of student autonomy in school-based English as a Foreign Language (EFL) learning across the Global South. Drawing on 22 peer-reviewed empirical studies (2020-2024), the synthesis highlights both the promise and the constraints of AI integration in diverse, resource-variable contexts. Three core mechanisms emerged: (1) AI-enhanced personalized learning and feedback, enabling learners to progress at self-determined paces across speaking, reading, writing, listening, and vocabulary learning; (2) opportunities for self-directed learning through intelligent tutoring systems, fostering greater ownership over learning trajectories; and (3) cultural and contextual adaptation, whereby locally responsive AI tools improve engagement and relevance. These mechanisms are shaped by critical contextual moderators, including infrastructure limitations, disparities in device access, and varying levels of teacher preparedness. Emotional and pedagogical implications further underscore the need for reflective, ethical integration. Overall, the review concludes that AI has significant potential to empower student autonomy in EFL education within the Global South, provided its use is context-sensitive, equity-driven, and supported by targeted teacher training and adaptive policy frameworks.

Background: Artificial Intelligence (AI) is increasingly integrated into school-based English as a Foreign Language (EFL) instruction, yet the mechanisms through which it shapes learners’ engagement, achievement, and satisfaction remain insufficiently theorised. Although prior studies report positive effects, they rarely explain how AI influences learning processes within authentic classroom conditions. Purpose: This systematic review synthesizes empirical evidence to explain how and under what conditions AI technologies shape engagement, achievement, and satisfaction in school EFL contexts. Specifically, it aims to identify the mediating cognitive, affective, and behavioural mechanisms through which AI operates, examine contextual moderators influencing its effectiveness, and develop an integrative AI Impact Pathways Framework to guide theory-driven research and context-sensitive pedagogical design. Method: A comprehensive search across seven databases yielded 99 records; following PRISMA 2020 procedures, 23 empirical studies involving direct AI use by K–12 EFL learners were retained. Thematic synthesis was employed to identify cross-study patterns and inductively develop a multi-pathway explanatory framework. Findings: AI tools—including NLP-based feedback systems, intelligent tutoring systems, conversational agents, gamified applications, and adaptive learning platforms—enhanced engagement by increasing interactivity, reducing anxiety, and sustaining time-on-task. Achievement gains were associated with personalised scaffolding, iterative feedback loops, and opportunities for authentic language use across speaking, reading, writing, and vocabulary learning. Satisfaction improved when AI supported autonomy, emotional reassurance, and perceptions of usefulness. Three interrelated pathways—cognitive, affective, and behavioural—mediated these outcomes, while teacher readiness, digital infrastructure, cultural–linguistic fit, student digital literacy, and cognitive load served as five significant contextual moderators. Implications: The review advances theoretical understanding by proposing the AI Impact Pathways Framework in School EFL Learning, which clarifies how AI affordances interact with pedagogical processes and contextual conditions to shape learner outcomes. The findings provide guidance for designing equitable, context-sensitive AI integration in schools and highlight the need for longitudinal, cross-cultural, and theory-driven research.

This study investigates the impact of music-integrated instruction on English reading development among early-grade learners in Indian government schools. Using a quasi-experimental pre-test–post-test design, 83 first-grade students were assigned to one control and three experimental groups. While the control group received conventional English instruction, the experimental groups participated in music-based lessons emphasizing instrumental play, singing, or dance. Low-cost and contextually adapted resources supported the intervention, ensuring feasibility in resource-constrained classrooms. An adopted and validated English Reading Skills Questionnaire measured gains in vocabulary, grammar, and reading comprehension. Results showed significant improvements across all domains for the experimental groups, with the largest gains in comprehension, particularly in the singing and dance conditions. Vocabulary improvements were consistent across modalities, whereas grammar gains were most pronounced in the singing group. Effects were consistent across genders, highlighting the inclusivity of music-based approaches. These findings support the integration of music into early language instruction, offering a scalable, culturally resonant strategy to strengthen foundational literacy in multilingual educational settings.

This systematic review explores the integration and impact of Artificial Intelligence in English as a Foreign Language teaching in schools, evaluating the effectiveness, challenges, and pedagogical implications of AI-driven tools. After screening 189 studies from seven databases, 22 relevant empirical studies focusing on experiential learning outcomes with AI use were selected, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The findings highlight AI's transformative impact on school-based EFL education, offering tailored, interactive experiences. Students using AI tools showed significant improvements in reading, writing, listening, speaking, vocabulary, and overall language comprehension compared to traditional methods. Improvements in language proficiency align with all three domains of Bloom's Taxonomy. Tools like Natural Language Processing and Intelligent Tutoring Systems enhance instruction but struggle with language nuances and cultural contexts. Challenges like the digital divide, literacy gaps, teacher readiness and role confusion, cognitive load, and context-specific adaptation persist. Addressing these requires robust infrastructure, teacher training, and institutional support. The review offers valuable insights for teachers, policymakers, and researchers dedicated to advancing school-based EFL education with innovative AI solutions.

AbstractIn the 21st century, Information and Communication Technology (ICT) is a critical component of innovative teaching and learning. This study looked at the impact of teachers' autonomy on their perceived ease of using ICT and the intricate relationship between the two, involving their self‐efficacy, job satisfaction, and perceived incentives to change. Using a descriptive survey approach, 311 (134 female) Indian elementary school teachers participated in the study and provided self‐reported data. A structural equation model was employed to test the mediating roles of self‐efficacy and job satisfaction, and the moderation of perceived incentives. The results affirmed that teacher autonomy had a direct and moderately positive effect on their perceived ease of ICT use. Self‐efficacy and job satisfaction significantly and partially mediated the indirect relationships between autonomy and ease of ICT use. In the three relationship paths the perceived incentive to change had a significantly positive moderation to catalyze the relationships. Considering Indian elementary school teachers' glaring lack of autonomy, this paper suggests a policy shift involving greater teacher autonomy and the use of incentives for improved efficacy, job satisfaction, and ICT use. The relationship matrix will serve as a reference for researchers and practitioners to gain a deeper understanding of the role of teacher autonomy in addressing the global issue of limited adoption and integration of ICT by school teachers.

This study aimed to investigate the complex psychological mechanism involved in the relationship between school teachers' mental health and teaching efficacy with the mediating role of emotional intelligence and the moderation of teacher autonomy. It used a descriptive survey method inside an Ex Post Facto study design randomly selecting 500 (female=229) Indian elementary school teachers teaching in grades one to eight. A structural equation model was used to examine the covert relationships among the constructs. The results indicated that mental health was positively associated with teaching efficacy, discretely and via emotional intelligence. Teachers’ autonomy partially mediated the indirect effect such that participants with high teacher autonomy demonstrated a stronger indirect link than those experiencing low autonomy. The findings contribute to a deeper understanding of the synergy between mental health and teaching efficacy with the policy implication for better mental health management for school teachers by paying specific attention to these vital factors like teacher autonomy and emotional intelligence at a time when 15 per cent of Indian school teachers are found suffering from mental health issues and state of teacher autonomy among these schools is lamentably low

This systematic scoping review aimed to collate evidence assessing associations between AI use and psychological outcomes (including cognitive, emotional, and behavioural responses to these intelligent systems) for school students from preschool (age <5 years), primary school (age 5-11 years), middle school (age 12-14 years), to high school (age 15-18 years). Original empirical studies were identified in seven reliable databases (Scopus, Web of Science, PubMed, PsycINFO, ScienceDirect, IEEE, and ERIC), resulting in 189 eligible studies. From these, we have identified 24 relevant studies reporting students' hands-on experiential learning outcomes on AI use. Findings revealed that the use of AI in schools can have both positive and negative impacts on the psychological well-being of students. Increased engagement, cognitive achievement, self-efficacy, learning autonomy, and decreased frustration are among the benefits of this strategy; nevertheless, over reliance, anxiety, stress, social isolation, unstable mental health, and moral dilemmas including privacy, bias, and justice are among its drawbacks. Overall, the psychological impacts of AI use among school students are multifaceted, contextdependent and across grades. By carefully considering the design, implementation, and ethical decorum of AI in school education, teachers and policymakers can maximise its benefits by mitigating potential risks in practicing technology enhanced learning.