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M. Miftach Fakhri
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jaaie@abcollab.id
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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 24 Documents
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Mapping the Evolution of AI and Academic Literacy Research in Undergraduate Humanities Education: A Two-Period Scoping Review Robert Stroud; Jinming Du; Conttia Lai; Khanh Duc Kuttig; Yuncheng Hua; Fiona Myers Kanemura
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.v2i1.873

Abstract

Generative artificial intelligence (GenAI) presents both opportunities and challenges for developing academic literacy in higher education. Unlike traditional academic literacy supports, which typically rely on human instruction, peer feedback, and independent writing processes, GenAI tools can generate, revise, and evaluate academic content in real time, fundamentally altering students’ engagement with writing and knowledge construction. However, the existing literature has not systematically examined how research on artificial intelligence (AI) and academic literacy has evolved over time or where key gaps remain. This scoping review investigates research continuity and gaps in studies of AI and academic literacy within undergraduate, humanities-related higher education contexts, comparing publications from 2022–2024 with those published in 2025. Guided by the PRISMA extension for scoping reviews, a total of 2,026 records were retrieved, of which 56 empirical studies published between Jan 1st 2022 and Oct 31st 2025 were included for analysis. The studies were examined across two time periods (2022–2024 and 2025) to analyze publication trends, geographical distribution, methodological approaches, and pedagogical themes. The findings indicate a rapid expansion of research following the emergence of large language model–based tools, such as ChatGPT. Early studies primarily focused on AI-supported writing processes, user adoption, and efficiency, whereas more recent research increasingly emphasizes critical AI literacy, responsible AI use, and human–AI collaboration in learning contexts. The review also revealed significant geographical and methodological imbalances in the literature. These findings suggest that future research should prioritize methodological diversity, stronger theoretical grounding and broader sociocultural representation. Higher education institutions are encouraged to integrate AI literacy into academic literacy instruction and develop clear policies that support the responsible and pedagogically effective use of AI technologies.
Generative Artificial Intelligence in Higher Education: A Decision-Support Framework for Practical Use, Governance, and Teaching Decision-Making Ronaldo Nunes Pinheiro
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.v2i1.954

Abstract

Generative artificial intelligence has entered higher education faster than institutions have governed it, leaving lecturers to decide case by case what may responsibly be handed over. This paper reframes the lecturer not as a user of AI tools but as a decision-maker who delegates parts of the teaching workflow to an analytical assistant under explicit human oversight. Drawing on an integrative review of literature from 2019 to 2025 in Scopus, Web of Science, IEEE Xplore and ERIC, the paper synthesises the documented uses, benefits and risks of AI in university teaching. A consistent finding is that the benefits are conditional, arising from the human-machine pairing rather than the tool alone, while the most structural risks cluster around academic integrity, honest disclosure and equity. One question organises them: what may be delegated, and how far? Adapting the classical model of types and levels of automation from human-factors engineering, the paper maps four teaching decision functions, namely acquiring, analysing, deciding and implementing, against a graded scale of AI assistance. The resulting pedagogical decision-support framework couples each function with governance guardrails covering transparency, academic integrity, data protection, equity, and accountability, and proposes a decision-rights rule that keeps evaluative and relational judgements with the teacher while allowing higher assistance for preparatory and generative work. The contribution is a per-task rule: policy frameworks organise institutions and AI-literacy frameworks build competence, while this one tells a lecturer how far to delegate a given task. The paper closes with limitations and an agenda for validation
Students’ Perspectives on the Ethical Use of Generative AI in Mathematics Learning: A Case Study Kelly Angelly hevardani; Redy Williantama Zulhafendi
Journal of Applied Artificial Intelligence in Education Vol 1, No 2 (2026): January 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i2.325

Abstract

The increasing integration of Generative AI tools into mathematics classrooms has transformed how students access explanations, solve problems, and receive feedback. While these technologies offer significant pedagogical benefits, their ethical implications from students’ perspectives remain underexplored. This study investigates students’ perceptions of the ethical use of Generative AI in secondary mathematics learning through a qualitative case study design. The research was conducted in a senior secondary school that has integrated AI-powered tools into classroom instruction and homework activities. Data were collected from 24 students through semi-structured interviews, focus group discussions, classroom observations, and analysis of AI-assisted learning artifacts. Thematic analysis revealed five major ethical dimensions perceived by students: (1) academic integrity and dependency risk, (2) fairness and unequal access, (3) transparency and trust in AI-generated solutions, (4) data privacy concerns, and (5) shifting responsibility in mathematical reasoning. Findings indicate that while students value AI for instant explanations and efficiency, they also express uncertainty about overreliance, authenticity of learning, and the credibility of AI outputs. Based on these findings, the study proposes student-informed ethical guidelines for responsible AI integration in mathematics learning. The results contribute to ongoing discussions on governance, digital literacy, and the pedagogical alignment of Generative AI in education.
Governance of AI Policy Implementation in Indonesian Public School Classrooms Chitra Imelda; Aris Munandar
Journal of Applied Artificial Intelligence in Education Vol 1, No 2 (2026): January 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/jaaie.v1i2.379

Abstract

The integration of artificial intelligence (AI) into classroom instruction has become an increasingly important issue in educational transformation because it is closely related not only to technological innovation, but also to public policy, equity, and governance in schooling. In Indonesia, the policy landscape for AI in education has begun to develop more formally through curriculum reform, ministerial implementation guidelines, digitalization programs, and broader legal discussions concerning data governance, AI ethics, and child protection. This development shows that AI in public schools is no longer merely a pedagogical trend, but an emerging object of public regulation and educational governance. This study aims to analyze how AI policy is interpreted and implemented in classroom learning in Indonesian public schools and to identify the governance challenges emerging from this implementation. This study uses a qualitative approach in the form of policy document analysis. The data consist of ministerial regulations, implementation guidelines, official government statements, policy reports, and relevant scholarly literature related to AI in education. At the stage reported in this article, the completed research component is the documentary phase; therefore, the findings are based on documentary evidence rather than on completed interviews or classroom observations. Data were analyzed using the interactive model of Miles, Huberman, and Saldaña through data condensation, data display, and conclusion drawing/verification. The findings show that AI policy in Indonesian public schools is officially interpreted as a human-centered, curriculum-based, and supportive pedagogical instrument intended to strengthen learning quality, digital literacy, and future readiness rather than replace teachers. The main policy instruments examined in this study include Keputusan Menteri Pendidikan Dasar dan Menengah Nomor 127/P/2025, Permendikdasmen No. 13 Tahun 2025, UU No. 27 Tahun 2022 on Personal Data Protection, PP No. 71 Tahun 2019 on Electronic Systems, and Surat Edaran Menkominfo No. 9 Tahun 2023 on AI Ethics, together with broader national policy discussions on the AI roadmap. The study identifies five specific governance challenges: (1) infrastructure inequality across schools and regions, (2) limited teacher readiness and professional capacity, (3) digital inequality in access to AI-supported learning, (4) fragmented regulation across education and digital-governance sectors, and (5) unresolved ethical issues involving data privacy, child protection, surveillance risks, and algorithmic bias. Overall, the study concludes that Indonesia already has an emerging framework for AI in education, but its effectiveness depends on stronger governance coherence, more equitable institutional readiness, and sustained policy support to ensure that AI contributes to educational justice and public accountability rather than reproducing existing disparities.

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