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M. Miftach Fakhri
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Journal of Applied Artificial Intelligence in Education
Published by PT. Academic Bright Collaboration
ISSN : -     EISSN : 31097081     DOI : https://doi.org/10.66053/jaaie
Core Subject : Science, Education,
Computer Science & IT Education
Applied AI in Classroom Practice, exploring practical classroom implementations such as smart content delivery, AI-powered virtual assistants, and automated learning support tools. Intelligent Tutoring Systems, focusing on adaptive AI-driven systems that personalize instruction based on individual learner characteristics and performance. AI-Based Assessment and Feedback, examining automated grading, formative assessment mechanisms, and intelligent feedback systems. Learning Analytics and Educational Data Mining, investigating AI-driven analysis of student behaviors, prediction of learning outcomes, and optimization of pedagogical strategies. Adaptive and Personalized Learning Environments, designing systems that dynamically adjust learning pathways based on real-time interaction and learner progress. Natural Language Processing in Education, including automated writing evaluation, language learning applications, and conversational agents for instructional support. AI for Inclusive and Accessible Education, leveraging AI technologies to assist diverse learners, including individuals with disabilities and those in underserved communities. Ethics and Governance of AI in Education, addressing fairness, transparency, accountability, data security, and responsible AI deployment within educational settings.
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 10 Documents
 1 
Search results for , issue "Vol 2, No 1 (2026): July 2026" : 10 Documents clear
Auditable Automated Essay Scoring and Formative Feedback: A Rubric-Grounded Pipeline for Secondary and Higher Education Qi Xin
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.348

Abstract

Automated essay scoring in education is increasingly expected to do more than reproduce human holistic scores; classroom use also demands rubric-aligned feedback, transparent evidence, and a way to route uncertain cases to teachers. In this study, “LLM-ready” refers to a system that outputs structured score evidence, weak-trait signals, and document-level anchors that can later be verbalized by a language model without changing the underlying decision trace. This study aimed to evaluate whether a rubric-grounded, LLM-ready pipeline can achieve competitive scoring accuracy while also generating auditable formative feedback and a teacher-controllable review signal. The evaluation used the public ASAP corpus of 12,976 essays across eight prompts and prompt-wise five-fold cross-validation. Four holistic scorers were compared: length-only, rubric forest, prompt-adaptive centroid regressor (PACR), and the final RG-Score ensemble with trait grounding, isotonic calibration, and audit control. Auxiliary analytic scoring was examined on Prompts 2 and 7–8, and feedback experiments were conducted on all 2,292 essays from Prompts 7 and 8. PACR obtained the highest macro QWK of 0.739, while RG-Score reached 0.738 and provided a calibrated, auditable path to feedback. The prompt-level QWK for RG-Score ranged from 0.66 to 0.82, with particularly strong gains on Prompts 6 and 7. Auxiliary analytic scoring yielded QWK values of 0.623 for Prompt 2 domain2, 0.604 on average for Prompt 7 traits, and 0.506 on average for Prompt 8 traits. The rubric-grounded evidence feedback template achieved a Trait Recall@2 of 0.829, a valid evidence rate of 0.912, and an auditability index of 0.893 on Prompts 7 and 8. These findings support rubric-grounded AES as a practical assessment-support approach for secondary-school writing and as a structured foundation for higher-education formative feedback workflows, while also indicating that weaker trait models should be treated as advisory rather than fully autonomous
Effects of Artificial Intelligence on Academic Achievement Among Nigerian University Students: A Meta-Analysis (2022–2025) Kayode Sunday John Dada
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.359

Abstract

Nigeria’s higher education sector faces persistent challenges, even as artificial intelligence shows growing potential to improve learning outcomes, while prior findings in the Nigerian university context remain fragmented and methodologically inconsistent. This study aimed to quantitatively synthesize empirical evidence on AI’s impact on academic achievement among Nigerian university students, identify moderating variables explaining effect heterogeneity, and document implementation challenges constraining AI adoption in the educational sector. Following PRISMA 2020 guidelines, a systematic search of eight bibliographic databases identified 47 eligible studies published between 2022 and 2025, covering a combined sample of 8,234 undergraduate and postgraduate students from federal and state universities in Nigeria. Random-effects models with restricted maximum likelihood estimation were conducted in R using the meta for package, with Hedges’ g as the primary effect size. Moderator analyses applied mixed-effects models and meta-regression across seven variables, while publication bias was examined using Egger’s regression test and trim-and-fill analysis. The pooled effect was moderate to large (g = 0.68, 95% CI [0.54, 0.82], p < .001), with substantial heterogeneity (I² = 86.5%) indicating important moderator effects. The strongest outcomes were associated with intelligent tutoring systems (g = 0.91), individualized learning strategies (g = 0.79), STEM disciplines (g = 0.84), and interventions lasting more than eight weeks (g = 0.81). Key implementation barriers included poor internet connectivity (91.5%), unreliable electricity supply (87.2%), limited faculty AI competence (89.4%), and financial constraints (85.1%). These findings support evidence-based AI integration policies in Nigerian higher education, particularly in infrastructure development, faculty training, and equitable implementation strategies.
Artificial Intelligence in Senior High School: Assessing Student Engagement and Academic Performance An Action Research Study Getigan, April Joy
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.361

Abstract

The rapid proliferation of Artificial Intelligence (AI) technologies across society has prompted a fundamental reconsideration of learning and teaching in the 21st century. AI tools such as ChatGPT, Google Gemini, Grammarly, Photomath, and adaptive learning platforms are becoming increasingly accessible to students at all educational levels. Despite the growing global evidence of AI’s educational impact, empirical research in the Philippine Senior High School (SHS) context remains limited. Existing policy frameworks and curricular guidelines from the Department of Education (DepEd) remain largely silent on the productive and potentially harmful use of AI by learners, creating an urgent need for locally grounded evidence on this issue. This study examined the influence of AI tool integration on student engagement and academic performance among Grade 12 students at Pedro N. Roa Sr. High School and generated evidence-based recommendations for institutional AI policy in the Philippine SHS setting. This action research used an explanatory sequential mixed-methods design conducted during the 2025–2026 School Year. The respondents included all 208 Grade 12 students enrolled across five SHS tracks, selected through total population sampling. Data were collected using the Student AI Engagement Scale (SAES), a researcher-adapted instrument validated for local context (Cronbach’s α = 0.87), official quarterly academic records, and teacher-observation protocols. Quantitative data were analyzed using descriptive and inferential statistics, while qualitative findings from focus group discussions were examined thematically. Results showed a significant positive relationship between AI tool usage and academic performance (r = 0.61, p < 0.001), improved mean academic performance from Q1 to Q3, higher engagement among female students, and themes of increased confidence, enhanced critical thinking, and occasional over-reliance on AI-generated content.
Ethical Concerns and Pedagogical Responses to Generative Artificial Intelligence in Social Science General Education: Perspectives of Higher Education Teachers in the Philippines Saldua, Lester; Rabago, Jasper Kim Mirasol
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.386

Abstract

The increasing integration of generative artificial intelligence (AI) in higher education has introduced ethical and pedagogical challenges, particularly in social science general education courses, where critical thinking and contextual analysis are essential. Despite its growing presence, the implications of artificial intelligence in non-STEM disciplines remain underexplored, especially in Philippine higher education where institutional policies and practices are still evolving. This study aimed to examine the ethical concerns raised by social science educators regarding the use of generative artificial intelligence and determine how these concerns shape their teaching practices. A qualitative research design was employed involving twenty-four social science educators from higher education institutions in Northern Luzon, Philippines. Participants were selected through purposive sampling based on their direct experience with artificial intelligence use in teaching and student work. Data were collected through semi-structured interviews conducted between August and December 2025, with each session lasting 45–60 minutes. The data were analyzed using Braun and Clarke’s thematic analysis to identify recurring patterns and themes. The findings identified five major ethical concerns: algorithmic misinformation in knowledge construction, artificial intelligence (AI)-enabled academic dishonesty, cognitive dependency and the decline of critical thinking, inequality in AI-assisted learning, and algorithmic bias and distorted social narratives. In response, educators adopted five corresponding pedagogical strategies: modification of assessment strategies, increased monitoring and verification of student work, integration of artificial intelligence awareness in instruction, reinforcement of academic integrity policies, and selective and guided use of artificial intelligence. These findings show that ethical concerns directly shape pedagogical decision-making in social science education and contribute to context-responsive pedagogical and policy frameworks for artificial intelligence integration in Philippine higher education
Guided Integration of Generative Artificial Intelligence in Mathematics Education: Teacher Role Transformation, Adaptive Learning, and Metacognitive Development Rafiepour, Abolfazl; Karimi , Pouya
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.335

Abstract

The rapid emergence of generative artificial intelligence (AI) has shifted education from content digitization toward intelligent learning processes. In mathematics education, however, AI integration remains pedagogically complex, particularly in relation to teachers’ professional roles, adaptive instruction, and teacher-reported support for students’ metacognitive regulation. This study explored how guided integration of generative AI influences instructional practices and professional role transformation among lower secondary mathematics teachers. A qualitative exploratory design was employed. Fourteen mathematics teachers participated in a structured 12-hour professional development workshop focused on pedagogically guided AI integration. Data were collected through semi-structured interviews, workshop field observations, and teacher-designed instructional artifacts. Thematic analysis was conducted to identify patterns in teachers’ post-intervention experiences. Findings revealed four interconnected themes: (1) AI as an enabler of personalized and adaptive learning pathways; (2) conversational AI as a dialogic cognitive partner supporting structured mathematical reasoning; (3) AI-mediated feedback as a teacher-perceived catalyst for metacognitive monitoring and self-regulated learning; and (4) ethical, pedagogical, and infrastructural constraints shaping sustainable implementation. Teachers perceived AI not as a replacement for professional judgment but as a tool requiring deliberate pedagogical mediation within a human-in-the-loop framework. The study contributes a context-sensitive model of teacher-mediated AI integration in mathematics classrooms and highlights the necessity of structured professional development to ensure ethically grounded, cognitively meaningful, and pedagogically aligned use of generative AI in school mathematics
Curriculum Design and Pedagogical Innovation in the Time of Artificial Intelligence: Conceptual and Empirical Investigation Moin, Md Alaul
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.336

Abstract

Artificial intelligence (AI) has shifted from peripheral automation to a constructive force in curriculum and pedagogy, requiring education systems to reconsider not only instructional tools but also educational purposes. This study positions curriculum design as the central arena where AI’s transformative potential and ethical risks are negotiated. Grounded in constructivism, connectivism, and the TPACK framework, it proposes a pedagogy-first and values-led model for AI integration that connects skill development, governance, and equity. The study aims to conceptualise AI-aware curriculum design by integrating pedagogical theory, equity considerations, and governance frameworks, while also examining the empirical applicability of the proposed framework among curriculum stakeholders. A descriptive quantitative cross-sectional survey was conducted with 100 school teachers and curriculum stakeholders from ten reputed government schools in Bihar, India. Data were collected using a structured five-point Likert-scale questionnaire covering eight interrelated constructs, including AI curriculum readiness, teacher competency, assessment redesign, ethical governance awareness, and institutional support. The data were analysed using SPSS 26. The findings indicate moderate but uneven readiness for AI integration. Assessment Redesign Readiness showed the strongest correlation with AI Curriculum Readiness (r = .71), suggesting that evaluative reform is a key mechanism for meaningful integration. Teachers’ AI Competency was strongly associated with Ethical Governance Awareness (r = .65), highlighting the connection between technical fluency and ethical judgment. Limited access to professional development (M = 2.97) revealed systemic instability. The study identifies five operational principles: purposeful competency framing, pedagogy-first integration, teacher-centred professional learning, ethical transparency, and equity-oriented governance. It concludes that sustainable AI integration depends less on technological advancement than on coherent, human-centred curricular architecture.
Beyond UNESCO: A Comparative Framework Analysis and Growth Ladder Model of AI Competency for Higher Education Teacher Professional Development Tandon, Bharti; Singh, Aakriti; Gupta, Adit
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.394

Abstract

Artificial intelligence (AI) is rapidly reshaping professional practice across higher education; however, most faculty lack structured, theoretically grounded frameworks to support responsible and pedagogically effective AI integration. This study addresses this gap through a four-phase comparative analysis of six international AI competency frameworks: UNESCO (2024), ISTE (2023), DigCompEdu with AI Extensions (2023), OECD Learning Compass (2019), Asia Society–OECD Global Competence (2018), and China’s National AI Teacher Competency Framework (2024), using systematic thematic coding with substantial inter-rater reliability (κ = 0.82). The analysis identified six critical gaps: cultural contextualization, economic adaptability, infrastructure flexibility, multilingual provision, indigenous knowledge integration, and disability accessibility. An adapted framework was developed by synthesizing three theoretical traditions–extended Technological Pedagogical Content Knowledge (TPACK), Adult Learning Theory, and Social Constructivism–selected for their collective capacity to address these gaps. Central to the model is the Growth Ladder, a four-level progression comprising Acquire, Adapt, Act, and Create, grounded in established models of teacher expertise and organized across five competency domains. A further theoretical contribution is the collective professional trajectory, which reframes AI competency development as a bidirectional process through which individual educator growth shapes and is shaped by institutional and community contexts. Unlike existing frameworks that treat cultural diversity or developmental progression in isolation, the adapted framework combines culturally responsive competency descriptors with explicit economic scalability principles, embedding Indigenous knowledge integration and multilingual provision as required dimensions absent from current international models. The framework is presented as a theoretically derived model intended for empirical validation using the Delphi method.
AI-Powered Tools to Enhance Critical Thinking and Clinical Reasoning in Nursing Education: A Scoping Review with Implications for Low- and Middle-Income Countries YEM, Sokha; YIM, Sovannra; KEM, Sokunthea; TUN, Sreypeov; Lida, Vann
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.396

Abstract

Artificial Intelligence (AI) is increasingly integrated into educational systems worldwide, offering innovative approaches to improve learning outcomes in health professions education. In nursing, AI-powered tools such as ChatGPT, intelligent tutoring systems (ITS), virtual patient simulation platforms, and automated assessment systems have shown potential to strengthen critical thinking (CT) and clinical reasoning (CR), which are essential competencies for safe and evidence-based practice. However, their scope, effectiveness, and applicability remain underexplored, particularly in low- and middle-income countries (LMICs), where limited digital infrastructure, faculty capacity gaps, and resource constraints hinder implementation. This scoping review aimed to map existing evidence on AI-powered tools used in nursing and health professions education to enhance CT and CR, identify implementation gaps and barriers, and derive context-specific implications for LMICs, with particular attention to Cambodia. Following the Arksey and O’Malley framework, refined by Levac et al. and Peters et al., and guided by PRISMA-ScR, a systematic search was conducted in PubMed, Scopus, and CINAHL for peer-reviewed publications from January 2015 to October 2024. Forty-two studies from 15 countries were included. Four categories of AI tools were identified: conversational agents (n = 14), intelligent tutoring systems (n = 11), virtual patient simulations (n = 10), and automated assessment systems (n = 7). Most studies reported positive outcomes, with seven of eight RCTs showing significant CT improvement and virtual simulations consistently enhancing CR. Nevertheless, infrastructure limitations, faculty unpreparedness, ethical concerns, and licensing costs remain major barriers. Sustainable AI integration in LMIC nursing education requires context-sensitive infrastructure, capacity-building, governance, and stronger longitudinal research.
Rural High School Zero-Cost Offline Generative AI Maker Education Framework: Design, Implementation, and Research on AI Literacy Evolution in Rural China Yang, Xia
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.481

Abstract

The rapid development of generative artificial intelligence GenAI brings opportunities to K-12 education while simultaneously exacerbating the digital divide in low-resource regions. This study addresses the realities of backward infrastructure and unstable networks in Chinese rural high schools by designing and validating a fully localized, zero-cost offline GenAI maker education framework. It utilizes the Ollama offline engine, Open WebUI interface, and text adventure game templates to achieve multimodal interaction in network-free environments. Centered on Seymour Papert’s constructionism theory, it proposes the “Offline GenAI Maker Education Peak Progressive Framework.” Through an 8-week progressive curriculum basic narrative → cultural multimodal → emotional companionship → ethical guardianship → comprehensive originality → meta-reflection, a quasi-experimental intervention was conducted in a Grade 10 class N=50 at a rural high school in Hebei Province. The results show that students’ AI literacy exhibited significant multi-stage peak evolution: rapid early increases in textual depth and cultural integration, successive mid-term peaks in emotional engagement and ethical critique, later expansion of originality capabilities, and, by Week 8, the achievement of closed-loop internalization of cognition–emotion–responsibility through meta-reflection. All dimensions showed pre- and post-intervention effect sizes of Cohen’s d > 3.2 p < 0.001, using a researcher-developed 5-point standardized scale; due to the extremely low baseline among rural students and the narrow scoring range, a certain ceiling effect exists. All 50 students produced complete original interactive games that met the preset evaluation standards. The study validates the localized pathway of constructionism in low-resource contexts in the digital era, fills the empirical gap in fully offline GenAI education for rural high schools, and provides a replicable Chinese solution for educational equity in the Global South
System Based Artificial Intelligence Adoption in Nigerian Tertiary Education: A Conceptual Governance Model for Learning, Academic Integrity, and Equity Adelakun, Najeem Olawale; Lasisi, Mariam Adenike; Kolawole, Tolani Damilola
Journal of Applied Artificial Intelligence in Education Vol 2, No 1 (2026): July 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i1.713

Abstract

This study explores the adoption of Artificial Intelligence in Nigerian tertiary education institutions from a conceptual governance perspective, specifically on learning outcomes, academic integrity and equity in HEIs. AI is no longer a tool for learning support but is a governance issue that requires policies and guidelines, ethical supervision, and pedagogical incorporation as it becomes more central to academic life. The study employs the narrative review method to integrate the empirical and conceptual evidence and to gain insight into the role of different types of Artificial Intelligence systems and the shifts in student learning practices and responses to the changes in institutional policy (2019-2025). The analysis presents Artificial Intelligence as an ecosystem of technologies and not as a single technology, and outlines the different governance considerations for each system type. The analysis reveals that Artificial Intelligence plays a role in the areas of personalised learning, academic productivity and access to educational resources in tertiary institutions in Nigeria. The benefits have many drawbacks, including academic dishonesty in generative systems, privacy and transparency issues in adaptive systems, and equity and dependency issues in intelligent tutoring systems. The institutional governance has been a gradual process and the students have been slow to take up Artificial Intelligence tools. The paper concludes that the integration of Artificial Intelligence in tertiary education in Nigeria should be structured, supported by a framework for policy development and Artificial Intelligence literacy, curriculum reform, and investment in digital infrastructure should have implications for other similar higher education systems in Sub Saharan Africa.

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