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Artificial Intelligence in Educational Decision Sciences
Published by PT. Academic Bright Collaboration
ISSN : -     EISSN : 31238823     DOI : https://doi.org/10.66053/aieds
Core Subject : Science, Education, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences
Artificial Intelligence in Educational Decision Sciences (AIEDS) focuses on high-quality empirical, theoretical, and methodological research that examines the role of artificial intelligence in shaping, supporting, and optimizing decision-making processes within educational systems. The journal is explicitly positioned at the intersection of artificial intelligence, educational sciences, and decision sciences, emphasizing analytical rigor, theoretical grounding, and real-world relevance. The journal publishes original research articles, systematic reviews, and conceptual papers within the following scopes: AI-Based Educational Decision Systems Design and evaluation of decision support systems, predictive models, and optimization tools for instructional planning, assessment, curriculum design, and institutional decision-making. Learning Analytics and Educational Data Science Applications of learning analytics, educational data mining, big data, and explainable AI (XAI) to inform academic, managerial, and policy decisions in education. Intelligent and Adaptive Learning Technologies Intelligent tutoring systems, adaptive and personalized learning environments, recommender systems, and human–AI collaboration in learning and teaching processes. Educational Management, Leadership, and Policy Analytics AI-driven analysis for educational leadership, governance, quality assurance, resource allocation, and evidence-based policy formulation and evaluation. Ethics, Governance, and Trust in Educational AI Studies on algorithmic fairness, bias, transparency, accountability, ethical decision-making frameworks, and regulatory implications of AI use in education. Lifelong Learning and Workforce-Oriented Decisions AI applications supporting lifelong and life-course education, vocational and higher education pathways, career guidance, employability analytics, and workforce development planning. AIEDS welcomes interdisciplinary contributions that combine artificial intelligence techniques with decision science frameworks and educational perspectives, offering robust theoretical contributions and practical implications for research, practice, and policy.
Arjuna Subject : Ilmu Pengambilan Keputusan (semua kategori) - Ilmu Pengambilan Keputusan (Lain-Lain)
Articles 17 Documents
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Enhancing ESL Vocabulary Acquisition through AI-Based Learning Systems Tanvir Mostafa
Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i1.38

Abstract

Purpose – Vocabulary is one of the key aspects of second-language development, as it facilitates reading, listening, speaking, and writing. Nevertheless, because lexical development needs to be practiced repeatedly, with long-term motivation and long-term memory encouragement, many ESL learners have problems with remembering and applying new vocabulary. Recent advancements in artificial intelligence (AI) have provided new opportunities for vocabulary teaching in the form of adaptive practice, automated feedback, multimodal support, simulated dialogue, and long-term progress monitoring.Methods – This narrative review explores how AI-based learning systems can be used to support vocabulary acquisition in ESL learners based on existing research on vocabulary learning, technology-assisted language learning, and recent research on AI and generative tools.Findings – According to the literature reviewed, AI-assisted learning may be helpful if systems deliver tasks of suitable difficulty, the ability to repeat and retrieve information, contextualized input and feedback. Nonetheless, the success of AI relies on good pedagogy, prudent design of instructions, validation of results and teacher mediation.Research implications – Critical issues include misinformation, privacy issues, and learners’ overreliance on technology.Originality – AI should be seen as an auxiliary resource and not a substitute for principled vocabulary instruction.
Algorithmic Governance in Education: A Framework for AI-Driven Decision Systems, Inequality and Policy Accountability Kamal Singh Kunwar
Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i1.40

Abstract

Purpose – This article develops a mechanism-based conceptual framework to explain how artificial intelligence (AI)-enabled decision systems are reshaping governance processes and distributive outcomes in contemporary education systems. It addresses a key gap in the existing scholarship: the absence of an integrated analytical lens linking algorithmic decision-making with institutional accountability and inequality in education. Methods – This study adopts a theory-building approach grounded in cross-disciplinary synthesis, drawing on insights from Artificial Intelligence, Decision Science, and Public Policy. Through a structured analytical method, it advances a multilevel framework that explains how AI-driven decisions are produced, interpreted, and implemented within institutional contexts. The analysis focuses on causal mechanisms rather than technical system design, positioning the contributions within governance and policy analysis.Findings – Four interrelated mechanisms are identified: (1) algorithmic bias transmission rooted in data and model construction; (2) institutional mediation shaping the interpretation and use of algorithmic outputs; (3) policy distortion arising from uneven or selective implementation; and (4) the reproduction or amplification of inequality across educational settings. Together, these mechanisms illustrate how AI systems interact with institutional structures to influence their outcomes. Research implications – This study provides a structured basis for analyzing accountability and inequality in AI-enabled education, while offering indicative directions for improving transparency and governance in policy contexts.Originality – This study introduces the Unified Algorithmic Governance Framework (UAGF), which integrates data processes, algorithmic decision-making, institutional dynamics, and socio-educational outcomes into a single analytical model. Unlike existing work on AI ethics and educational data governance, the framework emphasizes the interaction between technical systems and institutional processes in producing distributive effects.
Artificial Intelligence Adoption in Academic Research: The Roles of Awareness, Attitudes, and Perceived Usefulness Panan Danladi Gwaison
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.53

Abstract

Purpose – This study examines artificial intelligence (AI) adoption in academic research by assessing academicians’ awareness of AI tools, attitudes toward AI, perceived usefulness, and the associations of attitudes and perceived usefulness with reported AI adoption.Methods – A quantitative cross-sectional survey was conducted among academicians from universities, colleges of education, polytechnics, and monotechnics in Plateau State, Nigeria. From a target population of 4,570 academicians, a sample of 368 was determined, and 307 usable responses were retained for analysis. Data were collected using the Awareness, Attitudes and Perceptions of Academicians Towards Artificial Intelligence in Research Questionnaire (AAPATAIRQ), comprising 20 items across four constructs. Descriptive statistics and multiple regression were conducted using SPSS version 27.Findings – Descriptive results indicated generally low levels across the four constructs. Perceived usefulness recorded the highest mean score (M = 2.58, SD = 1.376), followed by attitude toward AI (M = 2.42, SD = 1.403), awareness of AI tools (M = 2.36, SD = 1.219), and AI adoption in research (M = 2.35, SD = 1.331). The reported regression results indicated that attitudes toward AI and perceived usefulness were positively and statistically significantly associated with AI adoption, with perceived usefulness showing the stronger relative association.Research implications – The findings indicate that institutional strategies for responsible AI integration should extend beyond general awareness initiatives by strengthening practical AI competencies, demonstrating the research value of AI tools, and providing guidance on academic integrity, transparency, reliability, and responsible use.Originality – The study contributes context-specific evidence from a heterogeneous tertiary-education setting and analytically distinguishes AI awareness from the evaluative factors associated with reported adoption. Because the study is cross-sectional and based on self-reported data, the findings should be interpreted as associations rather than causal effects
A Conceptual Framework for an AI-Based Educational Decision Support System in Nigerian Colleges of Education Suleiman Ebaiya Abubakar; Mohammed Umar; Joshua Joseph Yakubu
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.91

Abstract

Purpose – This study develops a conceptual framework for an Artificial-Intelligence-based Educational Decision Support System (AI-EDSS) to support academic and administrative decision-making in Nigerian Colleges of Education. It addresses the limited integration of institutional data readiness, AI-enabled analytics, decision-support usability, technology acceptance, human oversight, and governance within existing educational decision-support approaches, particularly in resource-constrained African higher-education contexts.Method – A structured conceptual-synthesis methodology was employed. Literature was searched between January and March 2026 across Scopus, Web of Science, ERIC, IEEE Xplore, ScienceDirect, and Google Scholar. Forty-two sources passed initial screening, of which 21 were retained for inductive-deductive thematic coding, theory mapping, framework construction, and proposition development.Findings – The synthesis produced a five-layer AI-EDSS comprising an Institutional Data Layer, AI/Analytics Processing Layer, Decision-Support and Recommendation Layer, Human-in-the-Loop Interaction Layer, and a cross-cutting Governance, Ethics, and Feedback Layer. Six theoretical propositions were derived linking data integration, predictive utility, user uptake, facilitating conditions, institutional trust, model feedback, and human decision authority.Limitations – The framework is conceptual and has not been empirically validated, prototyped, externally validated, or tested with institutional data. Its propositions require subsequent empirical operationalisation and validation.Originality – The study integrates DSR, educational data mining, UTAUT, human oversight, and responsible governance within a context-sensitive architecture tailored to Nigerian Colleges of Education.
Governing Artificial Intelligence in Indian Education: A Doctrinal Analysis of NEP 2020, Constitutional Rights, and Data Protection Law Anurag Yadav; Rais Ahmad
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.94

Abstract

Purpose – This study examines the governance of artificial intelligence (AI) in Indian education by assessing whether existing constitutional, statutory, judicial, and policy frameworks adequately regulate AI-assisted educational decision-making under the National Education Policy (NEP) 2020 and by identifying appropriate directions for rights-based and risk-sensitive reform.Methods – The study employs doctrinal legal research supplemented by comparative legal analysis. It examines the Constitution of India, the Information Technology Act 2000, the Digital Personal Data Protection Act 2023, relevant copyright provisions, judicial principles, NEP 2020, NITI Aayog’s AI strategy, and selected international frameworks, including UNESCO, OECD, and the European risk-based approach. The analysis focuses on privacy, transparency, fairness, accountability, and human oversight.Findings – India’s Educational AI governance problem is characterized by regulatory fragmentation rather than a complete absence of law. Existing protections remain partial and do not provide an integrated framework for algorithmic educational decision-making. Data-protection compliance alone does not ensure fairness, explainability, contestability, or accountability. The study also identifies gaps in institutional coordination and legal accountability. Its proposed Governance Matrix treats admissions and automated grading as high risk, learning analytics as medium risk, and supplementary AI chatbots and tutors as lower risk.Research implications – India should combine existing constitutional and statutory protections with sector-specific, risk-sensitive obligations, including meaningful human review, explanation, contestation, audits, clear institutional responsibility, grievance mechanisms, AI governance capacity, and attention to digital inclusion.Originality – The study integrates constitutional rights, data protection, educational policy, institutional accountability, and international AI governance into a context-specific framework for Indian education. It also distinguishes data governance from algorithmic accountability and proposes a normative, non-empirical Governance Matrix based on the consequences of AI use rather than fixed technology categories
The Role of Artificial Intelligence in Strategic Decision-Making of Private Universities: A Systematic Review Yundong Wu; Weijian Kong
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.100

Abstract

Purpose – This systematic review examines how artificial intelligence (AI) can support strategic decision-making in private universities, the organizational conditions shaping its value, and the governance and implementation risks that constrain responsible use.Methodology – Searches were conducted in the Web of Science Core Collection, Scopus, and Google Scholar between March and May 2026, with the final update on May 31, 2026. After duplicate removal, screening, full-text assessment, and evidence appraisal, 46 substantive sources published between 1955 and 2025 were included in this review. Four additional methodological references supported the review reporting and synthesis. Because the evidence base was heterogeneous, narrative thematic synthesis was applied while distinguishing direct private university evidence from evidence transferred from general higher education, organizational decision research, and AI governance.Findings – The synthesis identifies four interconnected roles of AI: environmental intelligence, decision augmentation, strategic execution, and governance infrastructure. AI can strengthen institutional sensing, the comparison of strategic alternatives, implementation coordination, and decision traceability. However, direct empirical evidence specific to private universities is limited. Strategic value depends on data quality, organizational learning, analytical capability, decision ownership, auditability, governance capacity, strategic fit, and alignment with the institutional mission. Therefore, AI is best understood as a human-led decision-support capability rather than a substitute for institutional judgment.Research limitations – The heterogeneous corpus prevents statistical estimation of a common institutional effect, while the review is restricted to English-language sources from three search platforms. Therefore, the four-part architecture should be treated as an evidence-organizing framework rather than a validated causal model.Originality – This review integrates higher education, organizational decision-making, strategic management, and AI governance evidence into an institution-level capability architecture for responsible AI-supported strategic decision-making.
Toward Institutional Priorities: Fuzzy Clustering of Aggregate Digital Learning Indicators in Malaysian Private Higher Education Muhammad Dhiyauddin Saharudin; Zulkifflee Mohamed; Khairul Affendy Md Nor; Valliappan Raju
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.103

Abstract

Purpose – This study develops an exploratory institution-level approach for prioritizing digital-learning improvements when only aggregate survey distributions are available.Methodology – A new secondary computational analysis used 35 five-point Likert indicators from a doctoral survey of 167 postgraduate respondents in selected Malaysian private-university contexts. Each indicator was represented by its five-category frequency distribution, transformed using square-root/Hellinger coordinates, and clustered with Fuzzy C-Means (FCM). Candidate solutions were compared using internal validity indices; the selected four-profile solution was benchmarked against favorable-direction means and favorable-response percentages; and 1,000 multinomial perturbations assessed aggregate-count stability.Findings – The four-cluster solution had MPC = .500, normalized PE = .487, Xie–Beni = .315, and silhouette = .345. Internet disruption was the clearest diagnostic concern, with 53.9% agreeing or strongly agreeing that access problems hindered online-learning completion. FCM ordering aligned with conventional ranking (Spearman ρ = .923 for mean rank; ρ = .888 for favorable-response rank). Perturbations produced mean ARI = .322, mean NMI = .475, mean retention = 62.8% (median 59.3%), and IA4 retention = 50.6%.Research limitations – The analysis clusters 35 indicators, not students. Aggregate data cannot recover respondent-level relationships, and perturbation tests aggregate-count robustness rather than respondent-level or population stability. IA4 is treated as a diagnostic outlier signal, not a robust singleton class.Originality – The study provides an exploratory, auditable soft-prioritization approach that preserves five-category response profiles and graded membership while avoiding unsupported claims of superiority over simpler ranking.

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