Artificial Intelligence in Educational Decision 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.
Articles
13 Documents
<![CDATA[Assessment of Artificial Intelligence on Academic Achievement Among Nigerian University Students: A Meta-Analysis ]]>
<![CDATA[Dada, Kayode Sunday John]]>
<![CDATA[ Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences ]]>
Publisher : <![CDATA[PT. Academic Bright Collaboration ]]>
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DOI: 10.66053/aieds.v1i1.27
<![CDATA[Purpose – This meta-analysis systematically examines the association between artificial intelligence (AI) interventions and academic achievement among Nigerian university students, synthesizing empirical evidence from 2022 to 2025.Methods – Following PRISMA 2020 guidelines, 47 primary studies meeting rigorous eligibility criteria were identified, yielding a combined meta-analytic sample of 8,234 undergraduate and postgraduate students from federal and state universities in Nigeria. A random-effects model with Hedges' g as the primary effect size metric produced an overall pooled estimate of g = 0.68 (95% CI [0.54, 0.82]), indicating a moderate-to-large positive association between AI integration and academic performance.Findings – However, substantial heterogeneity (I² = 86.5%) indicates that this overall estimate masks considerable variation across implementation contexts and should be interpreted with caution rather than as a stable, universally applicable effect. Moderator analysis identified significant variations across learning strategy, subject area, AI role and type, intervention duration, and sample size. Intelligent tutoring systems delivering individualized instruction in STEM disciplines over sustained periods yielded the largest effects. Infrastructural deficits, limited financial resources, and insufficient faculty AI competency were the most prevalent implementation barriers.Research implications – This study provides an empirically grounded synthesis of AI's educational associations within a resource-constrained developing country context while acknowledging the methodological limitations that constrain the certainty of the conclusions.Originality – The Implications for evidence-based policy and institutional practice in Nigerian higher education are discussed.]]>
<![CDATA[Enhancing ESL Vocabulary Acquisition through AI-Based Learning Systems ]]>
<![CDATA[Mostafa, Tanvir]]>
<![CDATA[ Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences ]]>
Publisher : <![CDATA[PT. Academic Bright Collaboration ]]>
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DOI: 10.66053/aieds.v1i1.38
<![CDATA[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.]]>
<![CDATA[Algorithmic Governance in Education: A Framework for AI-Driven Decision Systems, Inequality and Policy Accountability ]]>
<![CDATA[KUNWAR, KAMAL]]>
<![CDATA[ Artificial Intelligence in Educational Decision Sciences Vol 1 No 1 (2026): Artificial Intelligence in Educational Decision Sciences ]]>
Publisher : <![CDATA[PT. Academic Bright Collaboration ]]>
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DOI: 10.66053/aieds.v1i1.40
<![CDATA[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.]]>
