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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 9 Documents
 1 
Search results for , issue "Vol 2, No 2 (2027): January 2027" : 9 Documents clear
Prevalence and Perceived Academic Impacts of Artificial Intelligence Tool Use among Medical Students Ali, Hazhmat; Alhakim, Maryam; Mohammed, Heleen; Yousif, Aram; Sabri, Laween; Ahmed, Jehat
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.389

Abstract

Artificial intelligence (AI) is increasingly transforming medical education by supporting personalized and efficient learning, yet its growing use among medical students raises concerns about academic integrity, data privacy, ethical practice, and potential overdependence, making it necessary to examine its prevalence, usage patterns, perceived benefits and limitations, and association with self-reported academic outcomes. A cross-sectional single-institutional study employing convenience sampling was conducted at the University of Duhok, Kurdistan Region of Iraq. Participants completed a closed-ended questionnaire that gathered data on demographics, prevalence, and patterns of AI use, and perceived advantages and limitations. Data analysis was performed using SPSS version 26.A majority of students (61%) reported regular use of AI tools. ChatGPT was the most frequently utilized tool (66.3%), primarily for summarizing materials and completing writing tasks. Most participants perceived AI as beneficial for writing, academic performance, and time management; however, only one-third considered AI-generated content reliable. Reported disadvantages included diminished critical thinking, increased passive learning, decreased reliance on traditional resources, ethical concerns, and ambiguous institutional policies. AI use was primarily associated with self-reported improvements in writing, time efficiency, and academic performance, but also with reduced critical thinking. Regular users were more likely to report improvements in writing, time management, and academic achievement, as well as a greater decline in critical thinking. No significant association was found between AI use and increased study motivation. Although this study was conducted at a single institution, the findings indicate that while AI is perceived to enhance learning and academic performance, its use should be balanced with strategies that foster critical thinking and independent learning. Institutions are encouraged to develop clear guidelines and provide training to support the ethical and effective integration of AI in educational contexts.
A Conceptual Model for Integrating Machine Learning Competencies into Technical Higher Education: Development and Pilot Validation in a Central Asian Context Kylychbekovich, Arkabaev Nurkasym
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.429

Abstract

The integration of machine learning (ML) competencies into undergraduate technical education remains methodologically underdeveloped, particularly in higher education systems of developing economies undergoing digital transformation. This study aimed to develop and empirically validate a conceptual model for integrating ML competencies into technical higher education programmes. The model was constructed through the synthesis of systemic, competency-based, and activity-based pedagogical approaches and comprises four interrelated components: target, content, procedural, and evaluative-outcome. A quasi-experimental pilot study was conducted at Osh State University (Kyrgyz Republic) involving 54 second-year students of specialty 710100 "Computer Science and Engineering," assigned to an experimental group (n=28) and a control group (n=26). Group differences were assessed using an independent-samples t-test. Students in the experimental group achieved significantly higher scores in theoretical knowledge (82.4% vs. 71.3%; p<0.05), practical skills (85.7% vs. 68.9%), and reported substantially higher learning motivation (89% vs. 54%). These findings suggest that embedding ML components within a structured, modular instructional model improves both subject-matter competency and student engagement, indicating the model's potential for broader adoption in technically oriented higher education systems pursuing digital transformation
Applying Random Forest Machine Learning to Predict Opportunity-to-Learn Classifications in Instrumental Music Education: Toward Data-Driven Equity and Inclusion in Arts Programs Wesolowski, Brian
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.437

Abstract

This study applied Random Forest machine learning to predict opportunity-to-learn (OTL) classifications for secondary instrumental music programs using person–item interaction measures derived from a multifaceted Rasch partial credit model. Drawing on a national sample of 374 music educators in the United States, the Random Forest model demonstrated strong predictive performance, achieving an out-of-bag error rate of 13.3% and an overall accuracy of 87%, with cross-validated accuracy estimates showing comparable results. A multinomial logistic regression baseline achieved substantially lower accuracy (62%), confirming that the Random Forest captured a nonlinear structure not recoverable through linear classification. Variable-importance analyses identified curricular, staffing, scheduling, and resource indicators that most reliably distinguished among the three empirically derived OTL classes. The consistency of these results across multiple validation procedures demonstrates that the model effectively captures the underlying structure of OTL conditions and highlights a concentrated set of structural indicators that exert the strongest influence on classification outcomes. By translating complex survey data into a predictive framework, this study offers a foundation for future work aimed at developing scalable, data-informed tools for understanding structural OTL conditions in music education, specifically, and the related arts and sciences, more broadly.
From Content to Instruction: Comparing Zero-Shot and Theory-Guided Prompting in Large Language Model-Assisted Curriculum Development DaCosta, Boaventura; Kinsell, Carolyn
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.493

Abstract

Generative Artificial Intelligence (AI) tools are increasingly used in instructional design (ID) contexts; however, little empirical work has examined whether frontier language models produce theory-grounded instruction without explicit pedagogical guidance. This study compared zero-shot and theory-guided prompting across three large language models to evaluate whether principled instructional structure emerges on its own or requires explicit theoretical direction. Using a primarily qualitative instrumental case study design, each model generated a training curriculum under both conditions. Outputs were evaluated using a five-dimensional instrument grounded in Merrill's First Principles of Instruction. Theory-guided prompting produced higher overall instructional integrity than zero-shot prompting for two of the three models, with the largest gains in application and in integration and transfer. Zero-shot outputs were generally well-organized but more often reflected topic-based information presentation than principled ID. These findings suggest that, in the cases examined, even frontier-level models do not reliably produce theory-grounded instructional structure without explicit guidance, particularly in ways consistent with cognitive principles, and that generative AI cannot substitute for sound ID practice or human oversight. For instructional designers and educators, the results underscore the importance of instructional theory in AI-assisted curriculum development.
Personalized Course Promotion in MOOCs: A Hybrid Recommender with LLM-Structured Persuasive Explanation for Educational Advertising Maoxi Li; Hanqi Zhang
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.505

Abstract

This study investigates whether course recommendation and persuasive course promotion can be unified in a fully reproducible pipeline for learning-oriented course promotion. The problem is timely because large language models (LLMs) have rapidly entered education, recommendation, and personalized content generation, yet most educational recommendation studies still optimize ranking accuracy alone, while many promotion-oriented LLM studies emphasize text generation without a rigorous educational recommendation benchmark. We therefore propose an offline end-to-end framework named LGPRec that couples a hybrid recommender with a deterministic template-based explanation planner with an LLM-structured slot design. The recommender integrates collaborative, latent-factor, content-based, and completion-sensitive signals, while the explanation planner uses the same learner profile to produce short persuasive course promotions that remain faithful to the recommended course. Full experimental evaluations were conducted on two public datasets: an IBM course recommendation benchmark and the XuetangX MOOC sequence benchmark. On the IBM dataset, the final model achieved Recall@10, NDCG@10, and MRR@10 of 0.7264, 0.4750, and 0.3962. On XuetangX, the proposed HoTrans sequence model reached 0.4848, 0.3333, and 0.2869. Explanation metrics were evaluated on the IBM benchmark, which provides course text and topic labels; the personalized explanation layer preserved item faithfulness at 1.0000 while raising explicit history mention from 0.0012 to 1.0000 and increasing the unique ratio from 0.0027 to 0.1912. These results show that learning-oriented course promotion can be framed as a recommendation-plus-explanation problem and evaluated rigorously under reproducible offline conditions using public data.
Cognitive Misinterpretation Dynamics in Artificial Intelligence in Education: A Narrative Review of Anthropomorphism, Bias, and AI Literacy Naidoo, Vynolyn
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.565

Abstract

The integration of Artificial Intelligence (AI) into educational environments has intensified scholarly interest in its cognitive and behavioral implications for learners. At the same time, the term “AI psychosis” has appeared in non-academic discourse to describe perceived psychological effects of AI use. However, this terminology is not recognized in established clinical classification systems, including the DSM-5-TR and ICD-11, and its use in education more accurately reflects misinterpretation in human–AI interaction rather than clinical pathology. This study presents a narrative review at the intersection of artificial intelligence in education, cognitive psychology, and human–AI interaction. Drawing on literature related to AI literacy, anthropomorphism, cognitive bias, and learner behavior, the review synthesizes key mechanisms shaping how learners perceive and engage with AI systems. A structured thematic approach was used to organize evidence from prior empirical and conceptual studies in educational technology and cognitive science. The synthesis identifies recurring mechanisms, including anthropomorphism in AI perception, authority bias toward algorithmic outputs, confirmation bias in AI-assisted inquiry, and cognitive offloading that may reduce independent critical evaluation. These mechanisms are especially pronounced when AI literacy is limited, influencing learner trust, dependency, and evaluative judgment. Based on the synthesis, the AI Misinterpretation Model (AIMM) is proposed as a conceptual framework organizing these mechanisms into three layers: perception, interaction, and integration. The review emphasizes strengthening AI literacy to promote critical engagement with AI-generated outputs and reframes “AI psychosis” as a misinterpretation phenomenon rather than a clinical construct.
A MobileViT-Based Offline Low-Resource AI Hardware Simulation Framework: An Exploratory Pilot Study in STEM Education for Rural High Schools in China Yang, Xia
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.579

Abstract

Rural high schools in low-resource environments face significant barriers to AI-enhanced hardware simulations, including limited network bandwidth, low-specification devices, and a lack of localized offline tools. This exploratory pilot study proposes and evaluates a low-resource open-source AI simulation framework integrating MobileViT for student behavior detection, Grad-CAM heatmaps, and open-source tools such as QEMU, Logisim-evolution, Tinkercad AR, MagicSchool.ai, and Blender. The framework was implemented through WeChat in an 8-week A/B testing intervention involving 25 students from a rural high school in central China. It was optimized for offline compatibility, rural agricultural contextualization, and privacy protection using anonymous IDs. An exploratory statistical analysis was performed to examine the potential mediating pathways. The results showed approximately 30% improvement in learning efficiency, 25% improvement in test accuracy, and 28% improvement in participation rate, with bootstrap-based 95% confidence intervals indicating positive effects, although these should be interpreted cautiously due to the small sample size. Large effect sizes were observed (Cohen’s d > 0.8, p < 0.001); however, their generalizability remains limited in this pilot context. MobileViT showed a preliminary mediating role in increasing participation and reducing cognitive load, consistent with Cognitive Load Theory and Self-Determination Theory. The framework supports UNESCO’s digital equity principles through equitable access, bias minimization, privacy protection, community participation, zero-cost deployment on standard teacher PCs, and a public GitHub repository with CI/CD pipelines. This study offers a practical and replicable preliminary solution for inclusive STEM education in resource-constrained K-12 classrooms globally.
OPLANNER: A GUI-Driven Metaheuristic Tool Using the Tiki Taka Algorithm for Balanced Course Planning in Engineering Programs Ab. Rashid, Mohd Fadzil Faisae
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.588

Abstract

In higher education, the design of academic curricula presents significant challenges, particularly in ensuring balanced workloads across semesters. Unbalanced course schedules can lead to student fatigue, decreased academic performance, and elevated dropout rates, especially in demanding fields such as mechanical engineering, where courses vary widely in difficulty and prerequisites. Traditional approaches to course planning, often reliant on manual allocation by academic advisors, are inefficient and error-prone, failing to systematically address constraints such as prerequisite dependencies, credit hour limits, and semester-specific availability. This study introduces the Open-Registration Planning Network (OPLANNER), an innovative graphical user interface (GUI)-driven optimization tool that leverages the Tiki Taka Algorithm (TTA), a sport-inspired metaheuristic, to optimize course planning. The tool offers both default and customized planning modes, enabling users to input student information and preferences for subjects such as mathematics or physics, which dynamically adjust course difficulty weights. In a case study on a 55-course mechanical engineering curriculum, OPLANNER achieved balance efficiencies of up to 95 %, demonstrating effective workload distribution compared to manual planning approaches. This study contributes to a practical, user-accessible system that enhances educational planning, fostering more equitable and effective learning experiences in engineering programmes.
Generative AI in Academic Writing: A UTAUT-Based Survey of Student Perceptions at a Rwandan Higher Education Institution Ndayizigiye, Jean Paul
Journal of Applied Artificial Intelligence in Education Vol 2, No 2 (2027): January 2027
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v2i2.633

Abstract

This study examines how higher education students in Rwanda, particularly at the Institut Catholique de Kabgayi (ICK), perceive and use artificial intelligence (AI) tools in academic writing within a Kinyarwanda-English multilingual learning environment. The study is guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), which is applied as an organizing descriptive framework rather than a predictive or statistically tested model. Academic writing remains a central but challenging skill for students who are required to produce structured, argument-based texts in English as a second language, making AI tools increasingly relevant in supporting writing development. Data were collected from 222 valid student responses using a structured questionnaire combining Likert-scale items and open-ended questions. Descriptive statistics, including frequencies, means, and standard deviations, were used to analyze quantitative data, while qualitative responses were used for illustrative interpretation. Findings show that students frequently use AI tools such as ChatGPT, Grammarly, and Google Translate to support idea generation, grammar correction, paraphrasing, translation, and overall writing improvement. ChatGPT was the most widely used tool (78.8%), reflecting its central role in students’ writing processes. Students generally reported that AI tools enhance writing quality, productivity, and confidence. However, concerns were also expressed regarding overreliance, contextual inaccuracy, ethical use, and reduced critical thinking. It is important to note that plagiarism detection is associated with specialized tools such as Grammarly Premium and Turnitin, not Google Translate, which primarily serves translation purposes. The findings further indicate that multilingual educational contexts shape how students interact with different AI tools for translation, language support, and content generation. The study concludes that while AI tools offer significant pedagogical benefits for academic writing, their use requires structured guidance, institutional policy clarity, and AI literacy training to ensure responsible and effective integration in higher education writing instruction.

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