International Journal of Emerging Issues in Social Science, Arts and Humanities ( IJEISSAH)

Transforming Education in the Age of Artificial Intelligence: From Adoption to Responsible, Context-Sensitive Implementation

Gulzira Kurbanbaevna Kdirbaeva — 2026-10-28

How Generative AI Reshapes Classroom Dialogue in Chinese Higher Education: A Mixed-Method Study of AI-Enhanced Dialogic Teaching

Cuiyun Wang — 2026-10-26

This study examines how generative artificial intelligence (GenAI) changes classroom dialogue in Chinese higher education, using a mixed-method design informed by dialogic teaching theory. Whereas earlier research has concentrated on AI adoption and user perceptions, this study treats GenAI as a structural mediator of classroom dialogue, one that shapes instructional design, interaction processes, and student engagement. Data were gathered at two Chinese universities and include 12 undergraduate courses, 10 teacher interviews, 24 classroom observations, and 221 student questionnaires. The quantitative results show significant increases in classroom participation (+22.2%), contribution frequency (p < 0.001), and higher-order cognitive responses, although overall learning engagement stays relatively stable. The qualitative findings point to four interrelated mechanisms by which GenAI supports dialogic teaching: AI-assisted lesson design, AI-supported questioning sequences, AI-driven personalized learning support, and AI-facilitated collaboration. These mechanisms correspond to, and extend, Alexander’s principles of dialogic teaching, enhancing purposefulness, reciprocity, supportiveness, cumulativeness, and collectivity. The study proposes a GenAI-Enhanced Classroom Dialogue Framework, which shows that GenAI does not affect learning outcomes directly but reshapes them by restructuring classroom dialogue. The findings suggest that GenAI mainly strengthens teachers’ pedagogical design capacity and allows classroom interaction to become more structured, inclusive, and cognitively demanding.

Integration of Artificial Intelligence Technologies into the Educational Process: Uzbekistan's Experience and Future Prospects

Uteulieva Aynura Audanbaevna — 2026-10-26

This article reports a mixed-methods field study on how AI-based educational technologies affect learning in Uzbekistan. It was carried out in 15 schools and 3 higher education institutions across the Tashkent, Samarkand, and Fergana regions during the 2023–2024 academic year. An intervention group was compared with a comparison group over 16 weeks (920 students in total), and interviews were held with teachers and students. We looked at knowledge acquisition, learning motivation (ARCS), task-completion time, teachers’ administrative workload, and knowledge retention 12 weeks later. On the descriptive means, the intervention group showed about 27.5% higher knowledge acquisition than the comparison group, along with stronger motivation, better retention, and faster task completion. Teachers’ administrative workload was 34.6% lower, which reflects reduced workload rather than a general gain in teaching effectiveness. These differences still need to be confirmed with inferential analyses that account for clustering and baseline values. The article also considers the policy setting for AI in Uzbek education, keeping policy goals separate from the empirical findings. The results should be read as associations observed under the study conditions, not as proof of a universal causal effect.

Democratizing the Digital Frontier: Strategies for AI Literacy and Equity in Resource -Constrained Educational Settings

V. Vanitha — 2026-10-26

Artificial Intelligence (AI) is changing education systems worldwide at a rapid pace; however, unequal access to digital infrastructure, computational resources, and AI-enabled learning environments has widened what is now called the “AI Divide.” In resource-constrained educational settings, students and institutions with limited access to advanced AI technologies risk being shut out of future academic and economic opportunities. This study investigates scalable and inclusive strategies for implementing AI literacy in low-resource environments, where high-cost infrastructure and high-speed internet connectivity remain out of reach. Drawing on a qualitative analysis of case studies on frugal innovation, policy documents, and open-source Small Language Models (SLMs), the research proposes a “Low-Bandwidth AI Literacy Framework” intended for equitable AI education. The framework gives weight to mobile-first pedagogy, hardware-agnostic tools, offline-capable AI systems, and critical AI literacy skills such as algorithmic auditing, ethical awareness, and prompt-engineering logic. The findings reveal that developing meaningful AI competency does not necessarily depend on premium generative AI platforms or advanced computing ecosystems. A foundational “Critical AI Agency” can instead be cultivated through pedagogical models that are decentralized, accessible, and ethically grounded. The study argues that democratizing AI education requires moving away from resource-intensive implementation and toward educational strategies that are sustainable, inclusive, and sensitive to context. Through the adoption of open-source and low-bandwidth AI ecosystems, educational institutions can promote digital equity, social mobility, and global educational inclusion in the age of Artificial Intelligence.

The AI Detector Paradox: A Corpus-Based Analysis of Lexical Features and ZeroGPT Scores in Chinese L2 Undergraduate Writing

Huyue Liao — 2026-10-26

This study examines the "AI Detector Paradox": the association between L2 English lexical features and AI-detection scores. Applying standardized multiple regression to a baseline corpus of 200 verified human-authored undergraduate theses, we analyzed how specific lexical features are associated with ZeroGPT AI-likelihood scores. The results point to a counterintuitive "Penalty of Proficiency." Reliance on formulaic language is not associated with higher scores. Rather, higher lexical diversity (TTR) is significantly associated with higher AI scores (β = 0.188, p < 0.01), while dense academic vocabulary (AWL) paradoxically acts as a protective shield (β = -0.284, p < 0.001). These associations suggest that ZeroGPT scores may penalize signs of L2 lexical development, which could pressure L2 students to fall back on rigid, template-based writing to avoid false accusations.

Artificial Intelligence-Supported Project-Based Learning for Enhancing Students’ Digital Engagement in Remote Schools

Asdar — 2026-10-26

Artificial intelligence (AI) is opening new ways to support interactive, student-centered learning in schools. In remote island schools, however, AI-supported learning is still difficult to implement because technological infrastructure is limited, access to digital resources is restricted, and learning opportunities are unequal. This study examines the implementation of Artificial Intelligence-Supported Project-Based Learning (AI-Supported PjBL) and explores its role in enhancing students’ digital engagement in a remote island school context. A mixed-method approach was used with 30 junior high school students attending a small school on Lae-Lae Island, Indonesia. Students worked on contextual projects in which AI-assisted digital tools were used for information exploration, project planning, and digital content creation. Quantitative data came from a digital engagement questionnaire covering the cognitive, emotional, and behavioral dimensions of engagement; qualitative data were gathered through classroom observations, interviews, and project documentation. Students showed relatively high levels of digital engagement during the AI-supported project activities. Cognitive engagement recorded the highest percentage (76.23%), followed by emotional engagement (76.02%) and behavioral engagement (75.55%). The qualitative data further indicated that, despite limitations in technological infrastructure, the project activities encouraged active participation, collaborative inquiry, digital communication, and contextual problem-solving. The study suggests that AI-Supported PjBL can offer meaningful and engaging digital learning experiences to students in geographically isolated schools and may serve as a contextual approach to strengthening digital participation in resource-limited educational environments.

Integrating Artificial Intelligence into Ethno-STEM Project-Based Learning to Support Students’ Science Inquiry Skills in Island Schools

Sundari Hamid — 2026-10-26

Artificial intelligence (AI) gives STEM education new ways to support inquiry-based, student-centered learning. Using AI-supported learning in remote island schools is still difficult, however, because of limited infrastructure, restricted digital access, and contextual barriers to education. This study examines how AI, when integrated into Ethno-STEM Project-Based Learning (Ethno-STEM PjBL), supports students’ science inquiry skills in remote island schools. A descriptive mixed-method design was used with 63 junior high school students on Lae-Lae Island, Indonesia. Students worked on four contextual Ethno-STEM projects dealing with local food carbon footprints, renewable energy, waste management, and traditional water filtration. AI tools helped them explore information, generate ideas, organize their projects, and communicate scientific results. Quantitative data came from a science inquiry skills test covering six dimensions: observing, classifying, hypothesizing, experimenting, using tools and materials, and communicating. Qualitative data came from classroom observations, project documentation, and reflective discussions. Inquiry skill performance was relatively high in every dimension. Observing scored highest (87%), followed by communicating (79%) and using tools and materials (78%). The qualitative findings show that AI-supported inquiry encouraged students to investigate their own context, explore together, and link scientific concepts more closely with local cultural practices. Internet connectivity and digital literacy remained problems. Even so, the study suggests that AI-integrated Ethno-STEM PjBL has strong potential to improve science inquiry skills in geographically isolated schools, and it adds to the discussion on culturally responsive, technology-supported STEM education.

Mapping the Research Landscape of Artificial Intelligence in Design Education: A Systematic Literature Review (2016–2025)

Hsi-Hsun Yang — 2026-10-26

This study reviews the research landscape of artificial intelligence (AI) in design education from 2016 to 2025. AI and generative AI tools have recently been taken up widely in design learning, design thinking, creativity training, and visual communication education. Research on AI in design education is still developing, however, and a clear overview of its main trends and research structure is still needed. To address this gap, the study combines a literature review with bibliometric analysis. Publications were retrieved from IEEE Xplore, Web of Science, and Scopus. Following keyword searching, database comparison, duplicate removal, and manual screening of titles, abstracts, and keywords, 137 journal articles were retained for analysis. Co-authorship and keyword co-occurrence analyses were carried out in VOSviewer. The results show that publications on AI in design education rose rapidly after 2022, with especially strong growth in 2024 and 2025. The co-authorship network is still fragmented: there are several small research groups but little collaboration between them. Keyword analysis points to “artificial intelligence,” “design education,” “generative artificial intelligence,” “students,” “teaching,” “creativity,” and “design process” as important themes. The findings suggest that AI in design education has become an emerging research area, although more studies are needed on how AI supports students’ design thinking, creative processes, and visual communication practice in real learning contexts.

Implementing AI Policy in Indonesian Education: An Edwards III Analysis of Communication, Resources, Disposition, and Bureaucratic Structure

Dhita Safitri — 2026-10-26

Artificial intelligence (AI) has become increasingly embedded in Indonesian education policy, some promote AI while others set safeguards for children, academic integrity, and responsible use. Drawing on George C. Edwards III’s policy implementation framework, this study analyses how AI policy is implemented in Indonesian education and identifies the factors that shape implementation. It adopts qualitative policy analysis within an interpretive tradition. The core document corpus consists of nine Indonesian policy instruments issued between 2014 and 2026, supplemented by national statistical data and published studies on digital access, teacher readiness, and AI governance. The author coded the documents deductively against four variables: communication, resources, disposition, and bureaucratic structure, and then compared the documentary evidence across instruments. The analysis finds that Indonesia has moved from a broad national AI strategy toward more sector-specific educational regulation, yet implementation remains institutionally fragmented. The 2025 curriculum regulation makes provision for Coding and Artificial Intelligence as an elective subject, while the 2026 Joint Decree sets age-appropriate, task-specific, and risk-based conditions for digital and AI use. Resource constraints appear in uneven digital access and teacher capacity, and the evidence on implementer disposition is mixed rather than neatly split into pro- and anti-AI camps. Bureaucratic structure is marked by responsibilities distributed across central and regional authorities. The study recommends a cross-ministerial coordination mechanism, explicit implementation guidance, sequenced development of teacher capacity, and measurable indicators for monitoring implementation. Its limitations are a document-based evidence base and the absence of direct interviews with policy implementers.