Artificial Intelligence in Lifelong and Life-Course Education
Artificial Intelligence in Lifelong and Life-Course Education (AILLCE) focuses on advancing scholarly understanding of how artificial intelligence (AI) is designed, implemented, and evaluated within educational contexts across the entire lifespan. The journal emphasizes lifelong and life-course perspectives, addressing learning as a continuous process that spans early childhood, formal schooling, higher education, vocational education and training, adult learning, professional development, and later-life education. Its primary focus lies in examining the pedagogical, psychological, technological, and ethical dimensions of AI-supported education in formal, non-formal, and informal learning environments. The journal publishes original research articles, theoretical analyses, methodological studies, and systematic reviews that address, but are not limited to, the following areas: Artificial Intelligence Across the Life-Course AI applications in early childhood education, school education, higher education, vocational and professional education, adult education, and education for ageing populations; life-course transitions and longitudinal perspectives in AI-supported learning. AI-Enhanced Lifelong Learning Systems Adaptive and personalized learning systems, intelligent tutoring systems, learning recommender systems, AI-driven assessment, learning analytics, educational data mining, and lifelong learning pathways supported by AI technologies. Pedagogical, Psychological, and Developmental Perspectives The impact of AI on learning outcomes, motivation, self-regulated learning, academic emotions, cognitive processes, well-being, and learner agency across different developmental stages and educational contexts. AI Literacy, Ethics, and Governance in Education AI literacy and digital competence across the lifespan; ethical, transparent, and trustworthy AI in education; issues of algorithmic bias, fairness, explainability, data privacy, and governance frameworks for AI-enabled educational systems. Emerging Technologies and Innovative Learning Environments Integration of AI with immersive and interactive technologies, including virtual and augmented reality, game-based learning, workplace learning systems, open and community-based education, and informal learning environments. Methodological and Design-Oriented Research Design-based research, design and development research, mixed-methods approaches, longitudinal studies, learning analytics methodologies, and the validation of AI-supported educational models, frameworks, and instruments.
Articles
17 Documents
<![CDATA[AI-Driven Competency-Based Education: Shaping Lifelong Learning and Skill Acquisition in Dynamic Educational Environments ]]>
<![CDATA[Muhammad Rafiq-uz-Zaman]]>
<![CDATA[ Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 1 (2026): Artificial Intelligence in Lifelong and Life-Course Education ]]>
Publisher : <![CDATA[PT. Academic Bright Collaboration ]]>
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DOI: 10.66053/aillce.v1i1.29
<![CDATA[Purpose – This study analyzes how Artificial Intelligence (AI) can strengthen Competency-Based Education (CBE), an approach that prioritizes demonstrated mastery over time-based progression. Since traditional models do not ensure competency attainment, this review evaluates AI’s potential to enhance personalized learning pathways, adaptive assessment mechanisms, and continuous feedback systems that support lifelong competency development. Design/methods/approach – A systematic literature review was conducted following PRISMA guidelines, examining 29 peer-reviewed Q1–Q3 journal articles focusing on AI applications in CBE, personalized learning systems, and lifelong learning models. The synthesis covers technologies such as intelligent tutoring systems, learning analytics, natural language processing, and adaptive algorithms, interpreted through the lenses of the Technology Acceptance Model and mastery learning theory. Findings – The evidence indicates that AI contributes to competency development by enabling individualized instruction, real-time formative assessment, and early detection of learning gaps. AI-supported environments promote adaptive self-regulated learning skills that are central to lifelong learning. However, empirical evidence demonstrating long-term, quantifiable learning outcomes remains limited, and many studies rely on short-term or exploratory designs. Implementation challenges continue, especially in resource-constrained contexts where infrastructure, institutional readiness, and educator expertise are insufficient. Research implications/limitations – The generalizability of findings is restricted by the methodological limitations of existing studies, including limited longitudinal evaluation and contextual validation. Further research is needed to measure sustained mastery outcomes and test AI-enhanced CBE models across diverse educational settings. Originality/value – This study proposes an AI-Driven CBE Framework that integrates competency mapping, personalized learning pathways, dynamic assessment systems, and structured lifelong learning support. It highlights the importance of AI literacy, pedagogically grounded implementation, and ethical safeguards particularly data privacy, algorithmic fairness, and equitable access to ensure responsible and sustainable AI integration in education.]]>
<![CDATA[AI-Powered Pedagogy: Integrating Generative AI into Nigerian Tertiary Institutions Teaching and Learning ]]>
<![CDATA[Kayode Sunday John Dada]]>;
<![CDATA[Ibrahim Salihu Yusuf]]>
<![CDATA[ Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 1 (2026): Artificial Intelligence in Lifelong and Life-Course Education ]]>
Publisher : <![CDATA[PT. Academic Bright Collaboration ]]>
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DOI: 10.66053/aillce.v1i1.30
<![CDATA[Purpose – This study investigates the factors shaping Nigerian lecturers’ acceptance, adoption, and pedagogical integration of generative artificial intelligence (AI) in tertiary institutions. It integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and Activity Theory to explain both individual adoption dynamics and systemic institutional constraints. Design/methods/approach – A cross-sectional quantitative survey was conducted with 236 lecturers across Nigerian tertiary institutions. Structural Equation Modeling (SEM) was employed to test hypothesized relationships among UTAUT constructs (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and actual use). Additionally, a systematic meta-analysis of 47 empirical studies (N = 12,483; 2022–2025) contextualized findings within global higher education research. Descriptive and correlational analyses examined integration patterns and implementation challenges. Findings – Performance expectancy emerged as the strongest predictor of behavioral intention (β = .742, p < .001), indicating that lecturers adopt generative AI primarily for perceived pedagogical value rather than efficiency gains. Facilitating conditions demonstrated the strongest influence on actual use (β = .734, p < .001), revealing a structural gap between high adoption motivation and weak institutional support. A significant intention–behavior gap was observed, attributable primarily to infrastructural inadequacies, insufficient training, and policy ambiguity. Integration patterns showed that generative AI is predominantly used for preparatory tasks (e.g., literature synthesis, instructional material development) rather than student-facing applications. Activity Theory analysis identified four systemic contradictions Subject–Artifact, Artifact–Rules, Artifact–Community, and Object–Division of Labor that constrain transformative integration. Research implications/limitations – While the study confirms UTAUT applicability in sub-Saharan African higher education, the convenience sampling approach and overrepresentation of university lecturers limit generalizability. Future research should employ stratified probability sampling and longitudinal designs to examine evolving adoption patterns. Originality/value – This study provides one of the first large-scale empirical examinations of generative AI adoption in Nigerian tertiary education. By combining UTAUT’s behavioral precision with Activity Theory’s systemic diagnostic framework, it offers a theoretically integrated and policy-relevant explanation of why adoption intentions do not consistently translate into sustained pedagogical practice in resource-constrained contexts]]>
<![CDATA[Algorithmic Governance in Lifelong Learning Systems: Artificial Intelligence, Educational Policy Transformation, and Human Development ]]>
<![CDATA[Kamal Kunwar]]>
<![CDATA[ Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 2 (2026): Artificial Intelligence in Lifelong and Life-Course Education ]]>
Publisher : <![CDATA[PT. Academic Bright Collaboration ]]>
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DOI: 10.66053/aillce.v1i2.31
<![CDATA[Purpose – This study aims to address the theoretical gap between artificial intelligence in education and digital governance by explaining how algorithmic decision systems reshape governance structures in lifelong learning. While existing studies often examine AI in education or governance independently, limited attention has been given to how algorithmic governance influences education policy, institutional decision-making, and human development across the life course. Therefore, this study proposes a conceptual framework to explain the role of AI-driven governance mechanisms in mediating policy processes and optimizing learning systems.Design/methods/approach – This research employs a conceptual and critical analytical approach by synthesizing interdisciplinary literature related to artificial intelligence governance, education policy, and human development. Through systematic conceptual analysis, the study develops the Algorithmic Lifelong Learning Governance Model (ALLGM) as a theoretical framework to explain the interaction between algorithmic systems, educational governance, and lifelong learning policy implementation.Findings – The analysis identifies three central governance mechanisms within the proposed model: algorithmic policy mediation, predictive learning governance, and data-driven human development optimization. These mechanisms demonstrate how AI-driven systems can transform policy implementation processes, enable personalized learning pathways, and influence institutional decision-making within lifelong learning ecosystems. Research implications/limitations – As a conceptual study, the framework has not yet been empirically validated through real-world educational governance data or institutional case studies. Therefore, the generalizability of the model remains limited. Future research is required to empirically test the ALLGM framework across different educational systems and governance contexts to assess its practical applicability.Originality/value – This study contributes to the emerging field of algorithmic governance by integrating digital governance theory with lifelong learning policy analysis. The proposed model offers a novel theoretical perspective on how AI-driven decision systems reshape educational governance and highlights the importance of democratic accountability, ethical oversight, and inclusive policy design in AI-enabled lifelong learning environments.]]>
<![CDATA[To Innovate or Not to Innovate: Faculty Willingness and Reluctance to Integrate Generative AI in Higher Education ]]>
<![CDATA[Evan W. Faidley]]>;
<![CDATA[Louis Nadelson]]>;
<![CDATA[Brandy Walthall]]>;
<![CDATA[Janet Filer]]>
<![CDATA[ Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 2 (2026): Artificial Intelligence in Lifelong and Life-Course Education ]]>
Publisher : <![CDATA[PT. Academic Bright Collaboration ]]>
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DOI: 10.66053/aillce.v1i2.50
<![CDATA[Purpose – To investigate how U.S. tertiary faculty perceive and integrate generative artificial intelligence (GenAI) in teaching, ethical communication, and professional practice, addressing gaps in cross-institutional U.S.-based, theory-driven, and rural-inclusive faculty research.Design/methods/approach – Guided by Rogers’ (2003) diffusion of innovations theory, an exploratory cross-sectional mixed-methods online survey (N = 131; Cronbach’s α = .90) combined 20 Likert items with 5 open-ended prompts, analyzed through descriptive and inferential statistics and descriptive content analysis.Findings – Faculty engagement was bifurcated. A committed minority adopted GenAI for planning, differentiation, and assessment, while a larger group used it minimally. Participants endorsed ethical dialogue and disclosure expectations (Mdns = 4.0 and 5.0) yet voiced concern that GenAI diminishes critical thinking (Mdn = 4.0). Significant differences emerged by gender, discipline, and institutional locale, rural faculty expressed greater concern and lower preparation than suburban peers, but not by age or tenure.Research implications/limitations – The cross-sectional design, a self-selected, predominantly female and Caucasian sample, and small sample size constrain generalizability; AI literacy was not directly measured; however, findings support co-construction of GenAI policies with faculty and students, targeted professional development for rural faculty, and integration of AI literacy into curricula.Originality/value – This study offers the first U.S. cross-institutional, theory-driven account of tertiary faculty GenAI engagement and a debut to comparing rural and suburban institutions, providing a baseline for longitudinal, equity-based research and recommendations for policy, faculty development, and pedagogy.]]>
<![CDATA[Teachers' Attitudes Toward the Use of Artificial Intelligence in English Language Teaching: A Scoping Review With Implications for Bangladesh ]]>
<![CDATA[Tanvir Mostafa]]>
<![CDATA[ Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 2 (2026): Artificial Intelligence in Lifelong and Life-Course Education ]]>
Publisher : <![CDATA[PT. Academic Bright Collaboration ]]>
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DOI: 10.66053/aillce.v1i2.35
<![CDATA[Purpose – This scoping review maps recent literature on English teachers’ attitudes toward artificial intelligence (AI) in English language teaching (ELT) and discusses its implications for Bangladesh. It addresses the limited synthesis of evidence across empirical studies, systematic reviews, and policy reports on teachers’ perceptions of generative AI, automated writing support, speech recognition technologies, and adaptive learning systems.Design – Guided by established scoping review frameworks and PRISMA-ScR reporting guidance, searches were reconstructed and updated in May 2026 through Google Scholar, ERIC, SpringerLink, ScienceDirect, Frontiers, MDPI, journal websites, and relevant institutional sources. Search terms covered artificial intelligence, generative AI, ChatGPT, ELT, EFL/ESL, teacher attitudes, teacher perceptions, AI literacy, and Bangladesh. After duplicate removal and screening, 18 sources were included. Data were manually coded thematically by context, design, evidence base, AI applications, attitudinal direction, benefits, concerns, and implications.Findings – Five themes emerged: perceived usefulness, perceived ease of use and AI literacy, ethical and pedagogical issues, contextual and institutional challenges, and teacher identity and agency. Teachers generally showed conditional positivity toward AI, valuing its support for material preparation, feedback, assessment, differentiated instruction, and time efficiency, while remaining concerned about cheating, overreliance, hallucinations, bias, privacy, unequal access, and reduced pedagogical control.Research implications – Responsible AI use in Bangladeshi ELT requires teacher-centered professional development, redesigned assessment, clear institutional policy, and locally relevant classroom models. This review does not infer causality.Originality – The study offers a focused thematic map for future research, professional development, policy design, and responsible AI integration in ELT]]>
<![CDATA[AI Paradox: Investigating Behavioral Divergence and Cognitive Offloading in AI-Assisted English Language Teaching for SMTP Students ]]>
<![CDATA[Boonchat Mekkaeo]]>;
<![CDATA[Parkpoom Sungchuay]]>;
<![CDATA[Supawadee Thibai]]>
<![CDATA[ Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 2 (2026): Artificial Intelligence in Lifelong and Life-Course Education ]]>
Publisher : <![CDATA[PT. Academic Bright Collaboration ]]>
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DOI: 10.66053/aillce.v1i2.51
<![CDATA[Purpose – This study investigates the "AI Paradox" and the behavioral divergence between AI Augmentation and AI Replacement among 112 Grade 9 SMTP students. It addresses the critical problem of cognitive offloading, where high-quality AI-assisted assignments mask a decline in individual linguistic proficiency. The research explores how the BOT-AI Model can be used to distinguish between productive scaffolding and counter-productive dependency in English Language Teaching (ELT).Design – A 12-week quasi-experimental investigation was conducted at Petcharik Demonstration School. Participants were categorized into two groups based on a Digital Interaction Log (DIL) Matrix and the AI Literacy Engagement Scale (AI-LES). The methodology compared performance across 10 weeks of AI-assisted assignments against a strictly proctored, unassisted post-test to identify the "Paradox Gap."Findings – Results confirm a significant AI Paradox. The Replacement group (n=50) exhibited a sharp 10.70-point decline in proctored environments, demonstrating a "mirage of competence" through cognitive offloading. Conversely, the Augmentation group (n=62) maintained consistent proficiency with a large effect size (d = 1.62), utilizing AI as a constructivist scaffold with an average of 6.4 iterations per task compared to only 1.2 iterations in the Replacement group. Research implications/limitations – The findings necessitate assessment reform and a shift toward process-oriented AI pedagogy. Limitations include the specific high-achieving SMTP context within a Thai municipal school, which may vary in broader educational settings.Originality – This research provides novel empirical evidence of the AI Paradox using real-time log data. It offers a scalable framework for educators to monitor digital interaction patterns, ensuring that technology serves to amplify human intellect rather than replace it.]]>
<![CDATA[Artificial Intelligence and Refugee Education in Africa: A Scoping Review of Adaptive Learning, Digital Inclusion, and Educational Continuity ]]>
<![CDATA[Jeketule Soko]]>
<![CDATA[ Artificial Intelligence in Lifelong and Life-Course Education Vol 1 No 2 (2026): Artificial Intelligence in Lifelong and Life-Course Education ]]>
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DOI: 10.66053/aillce.v1i2.69
<![CDATA[Purpose – This study maps the application, discussion, and theorization of artificial intelligence (AI) in refugee education across Africa, with particular attention to adaptive learning, multilingual communication, digital inclusion, educational continuity, and ethical governance. It addresses the limited empirical evidence connecting AI-enabled education directly with African refugee contexts.Methodology – A scoping review was conducted using the PRISMA-ScR and Population–Concept–Context frameworks. The search covered eight academic databases and selected grey literature sources. Of the 3,847 records identified, 2,644 were screened, 353 underwent full-text assessment, and 46 sources were included in the final thematic synthesis.Findings – The evidence clustered around four themes: adaptive learning and educational continuity, multilingual AI and communication, digital inclusion and infrastructural inequality, and ethical governance. However, no included study provided direct empirical evidence evaluating AI-enabled systems specifically for refugee learners in Africa. Much of the evidence remains adjacent, and several initiatives described as AI-supported are more accurately characterized as digital learning platforms.Limitations – The review did not formally appraise source quality, and non-English and country-specific evidence may have been underrepresented. The limited number of refugee-led, Africa-centered, and longitudinal studies constrains claims about effectiveness.Originality – This study clarifies the boundary between AI-enabled and broader digital education and positions AI-supported refugee education as a rights-sensitive socio-technical intervention requiring infrastructure, multilingual design, teacher support, and accountable governance.]]>
