Frontiers in Education: Digital Learning Innovations
5 days 17 hours ago
This study examines how interactive digital mnemonics reshape the temporal dynamics of associative retrieval during foreign-language vocabulary acquisition among adolescent learners. A quasi-experimental design compared an adaptive digital learning environment with conventional paper-based methods in a sample of 102 middle-school students (aged 12–14), assigned at the intact-class level to an experimental group (n = 51) and a control group (n = 51). Participants studied 45 health- and sport-related English lexical items. A computerized lexical decision task programmed in PsychoPy measured reaction time (RT) in milliseconds and response accuracy across three phases: baseline pre-test (T0), immediate post-test (T1), and four-week delayed post-test (T2). The experimental group exhibited a statistically significant reduction in RT latencies at T1 [M = 942 ms vs. 1,520 ms; F(2, 200) = 84.30, p < 0.001, η²p = 0.46] and maintained faster retrieval at T2 (1,055 ms vs. 1,710 ms). Accuracy at T2 remained substantially higher in the experimental cohort (81.5% vs. 54.1%), indicating greater retention resilience. These findings provide behavioral evidence that the integrated digital mnemonic package was associated with faster associative retrieval and more durable L2 vocabulary retention. Because cognitive load was not measured directly, the results are interpreted as evidence of improved retrieval efficiency rather than as direct confirmation of reduced extraneous load.
Zhanar Ibrayeva
6 days 7 hours ago
Teaching students to solve geometric problems is one of the most pressing issues in mathematics education. Problem-solving is a primary means of applying students’ theoretical knowledge and constitutes an essential part of their work in geometry lessons. However, classroom practice indicates that many students experience serious difficulties in solving mathematical problems and often fail to develop adequate problem-solving skills. These difficulties are particularly evident in geometry. Therefore, further investigation is needed into instructional approaches that may improve students’ geometric problem-solving abilities. This study aimed to develop a GeoGebra-supported methodology for building students’ foundational knowledge and to test its effectiveness in teaching geometric problem-solving. A total of 370 students from four schools in Almaty—Schools Nos. 17, 33, 148, and 172—participated in the study. The experimental and control groups each consisted of 185 students from classes with similar levels of prior geometry achievement, based on their results from the previous academic year. The findings showed that students in the experimental group demonstrated higher levels of foundational knowledge and better problem-solving performance than those in the control group. The GeoGebra-supported methodology also contributed to students’ understanding of geometric concepts and improved their ability to solve geometric problems.
Almagul Ardabayeva
6 days 17 hours ago
Sergio Ruiz-Viruel
1 week ago
This study investigates the effectiveness of integrating Artificial Intelligence (AI) within a Project-Based Inquiry (PBI) framework to enhance elementary students’ mathematical connections in geometry learning. AI functions as a cognitive scaffolding tool by providing adaptive prompts that support students’ reasoning, representation, and reflection, while PBI engages learners in authentic problem-solving tasks such as designing classroom layouts and park environments.The indicators of mathematical connections assessed in this study include: (1) connections between mathematical concepts (e.g., area and multiplication), (2) connections across multiple representations (visual, symbolic, and verbal), and (3) connections between mathematics and real-world contexts. This study employed a Design-Based Research (DBR) approach involving iterative cycles of design, implementation, evaluation, and refinement. Data were collected at the elementary school level through classroom observations, students’ project artifacts, short interviews, and AI–student interaction logs. The findings reveal substantial improvements in students’ mathematical communication and reasoning skills. Quantitative results indicate increases across all assessed dimensions, including precision of vocabulary usage (from 38% to 76%), logical structuring of arguments (from 42% to 71%), interactive discourse (from 36% to 68%), and accuracy in problem-solving (from 44% to 79).Qualitative findings further show that students transitioned from procedural to conceptual reasoning, demonstrating the ability to connect geometric concepts with real-world design contexts. AI-supported scaffolding facilitated this shift by guiding students from informal reasoning toward formal mathematical modeling. Interview data confirmed increased student engagement, metacognitive awareness, and confidence in problem-solving.These results suggest that AI-supported PBI effectively promotes interconnected mathematical understanding and meaningful learning experiences in elementary education.
Fery Muhamad Firdaus
1 week ago
Against the backdrop of the ongoing technological revolution and industrial transformation, artificial intelligence (AI) has emerged as a pivotal technological driver for advancing the high-quality development of higher education. As one of China's leading provinces in both higher education and manufacturing, Shandong faces a pressing need to integrate AI into its higher education system to facilitate educational modernization and support the broader transition from old to new growth drivers. This study examines the theoretical underpinnings and operational mechanisms through which AI contributes to the high-quality development of higher education and explores its practical implications within the specific context of Shandong Province. Focusing on key dimensions—including talent cultivation, teaching and learning innovation, university governance, and the optimization of educational resource allocation—the study demonstrates that AI enhances higher education development primarily through data-driven decision-making, intelligent technologies, and human–machine collaboration. However, the empirical application of AI within Shandong's higher education sector remains constrained by fragmented technological implementation, insufficient AI literacy among faculty members, and underdeveloped institutional and ethical governance frameworks. To address these challenges, this paper proposes a series of policy recommendations, including strengthening top-level institutional design, promoting the intelligent transformation of teaching and learning processes, enhancing faculty capacity building, and establishing robust systems for AI governance and ethical regulation. The findings offer both theoretical insights and policy implications for advancing AI-enabled high-quality development of higher education at the regional level.
Cuihua An
1 week ago
This study investigates the application of generative artificial intelligence (generative AI) in mathematics education from 2022 to 2026. Drawing on a systematic literature review of 268 articles retrieved from OpenAlex, and EBSCO databases, we identify and analyze 66 core research articles through a rigorous screening process following PRISMA guidelines. The findings reveal three key patterns. First, generative AI applications in mathematics education span four domains: student learning support (64%), teacher instruction assistance (38%), assessment and feedback innovation (27%), and special education (2%), with student learning support as the dominant application area. Second, while generative AI shows positive effects in enhancing learning motivation and providing personalized support, it exhibits notable limitations in mathematical reasoning accuracy and critical thinking development—with 70% of studies documenting AI limitations. Third, challenges associated with these applications are multifaceted, encompassing technical (30%), pedagogical (52%), ethical (20%), and implementation (42%) dimensions. We propose an integrated analytical framework combining the Technological Pedagogical Content Knowledge (TPACK) framework with hybrid intelligence theory, and identify a novel pedagogical paradigm in which AI errors serve as instructional resources for cultivating critical thinking. Future research directions include broadening application domains, deepening theoretical frameworks, and strengthening empirical investigation. This review is limited to English-language publications from two databases, with the search closing in June 2026. Despite these limitations, the review contributes systematic evidence and practical guidance for the effective integration of generative AI in mathematics education.
Zengfu Chao
1 week ago
This study examines lecturer-guided artificial intelligence (AI)-supported flipped learning in undergraduate Information Technology programming education. The study focuses on how multiple AI tools can be pedagogically orchestrated across pre-class preparation, in-class programming practice, and post-class reflection while maintaining lecturer guidance, student reasoning, and responsible AI use. A classroom-based non-equivalent groups quasi-experimental design was implemented with 120 undergraduate Information Technology students in a Programming Techniques course. One intact class participated in a lecturer-guided AI-supported flipped learning condition (n = 60), while another intact class participated in a traditional flipped classroom condition without planned AI-supported learning tasks (n = 60). Quantitative data included pretest and posttest programming scores and post-intervention self-reported measures of learning autonomy, reflective learning, and digital competence. Qualitative interview data from students and lecturers were used to contextualize participants' perceptions of AI-supported learning activities. The results showed that the AI-supported flipped learning group achieved higher posttest programming performance, showed a larger observed programming-performance gain, and reported higher post-intervention learning autonomy, reflective learning, and digital competence than the traditional flipped classroom group. Because the self-reported items referred explicitly to AI-supported activities, these differences are interpreted as condition-linked perceptions rather than as comparisons of general competence. Qualitative findings indicated that students and lecturers perceived AI as useful for preparation, debugging-related feedback, solution comparison, reflection, and lecturer-guided verification. The findings should be interpreted as classroom-based evidence of observed group differences rather than as definitive causal proof of AI tool effects. The main contribution of the study is to illustrate how multiple AI tools can be organized as a lecturer-guided pedagogical orchestration within the flipped programming learning cycle.
Ngoc Duong Bach
1 week 1 day ago
Despite the growing interest in studying metaverse in educational and media contexts, the experimental studies that measure its impact on understanding and recalling cultural content are still limited. Therefore, this study aims to examine changes in media students’ understanding and recalling of cultural content following participation in a metaverse-based learning experience, in light of the rapid digital transformations that have imposed new types of immersive and interactive learning environments. The study adopted a quasi-experimental design based on two non-random groups, with the application of a pre-test and a post-test on a sample consisting of one hundred and forty-two students from the media students. The same cultural content was presented to the participants through the Metaverse environment, while the means of accessing it differed between a group that used virtual reality (VR) headset and a group that used a mobile phone. The differences were also studied in light of the gender variable. The achievement test consisted of nine content-related items; five items were allocated to measure recalling, and four items to measure understanding. To analyze the data, the Wilcoxon signed-rank test was used to compare the pre- and post-tests, and the Mann–Whitney test to compare independent groups, along with gain score analysis and covariance analysis to adjust for pre-test differences, with effect sizes being calculated. The results showed a statistically significant improvement between the pre- and post-tests in both recalling and understanding, with large effect sizes that warrant careful interpretation. Furthermore, the post-test scores and achievement scores were higher for the virtual reality group compared to the mobile phone group, and this difference remained significant even after adjusting for pre-test performance. The gender variable had no effect on recalling, while a difference in understanding was observed, favoring the female students. The study recommends employing metaverse environments in teaching cultural and media content and conducting follow-up studies on larger samples and other learning variables.
Asmaa Mosad
1 week 5 days ago
IntroductionAlthough artificial intelligence (AI), particularly generative AI, poses challenges related to potential misuse in education, it also offers an opportunity to improve students' writing and revision skills when thoughtfully integrated as a pedagogical resource. To investigate AI use in undergraduate education and understand students' perceptions of its impact on their writing, we designed and implemented two consecutive writing assignments that incorporated AI-assisted revisions in a large-enrollment general education (GE) course.MethodsFor each assignment, students were required to compose an initial draft without AI assistance to promote critical thinking and writing proficiency. They then used AI tools of their choice to revise the drafts with the goal of improving grammar, clarity, and coherence. Assignment grading was based mainly on the initial drafts, and feedback was provided by the instructor and the teaching assistant to support students' learning. In addition, students completed reflection questions about their experiences with AI, particularly its effects on their writing process and outcomes.ResultsThese reflections provided insights into how students selected and engaged with AI tools during the revision process as well as how they perceived the role of AI in assisting their writing development.DiscussionBy preserving traditional writing practices and strategically integrating AI-assisted revisions, this study can potentially inform pedagogical strategies that enhance student writing experience while maintaining academic integrity.
Alexis R. Heather
2 weeks 1 day ago
As generative artificial intelligence (GAI) tools become popular in education, questions about when and whether students disclose their AI use have emerged as a critical concern for academic integrity and self-regulated learning (SRL). Using SRL as an interpretive lens, we examine undergraduate students’ AI disclosure practices, investigating how their worries about negative consequences, AI usage behaviours, and demographic factors relate to their willingness to disclose AI use in academic settings. Drawing on survey data from 78 undergraduate students enrolled in an online education course, we developed study-specific survey items addressing worry, disclosure, and AI use. Given the developmental status of the instrument, we retained two preliminary composite indicators, worry and disclosure to teachers, and analyzed less internally consistent peer-disclosure and AI-use items individually. Our findings reveal a pattern of co-occurring worries: fear of teacher judgment and fear of social stigmatization are strongly correlated, and both are associated with students’ active concealment of AI use. Interpretively, we describe two contrasting response patterns among students: a transparent-disclosure pattern, in which disclosing AI use to instructors appears to reflect part of SRL processes, and an anxious-concealment pattern, in which heavier AI users report less transparency with teachers. A further finding is that anxiety is not associated with reduced disclosure to teachers directly, but is associated with increased peer-only sharing and complete secrecy. Exploratory analyses also suggest demographic patterns worth further study, with academic discipline showing the largest association with disclosure and a marginal indication that monolingual students may disclose less than multilingual students. Given the sample size, we reserve our interpretations and treat these as hypotheses for future work rather than established effects. These findings suggest that institutional AI disclosure frameworks may benefit from addressing both policy compliance and the affective and disciplinary dimensions of students’ SRL decision-making.
Daniel H. Chang
2 weeks 1 day ago
IntroductionFundamental movement skills (FMS) are an important foundation for children's confident participation in physical activity and sport, but many children spend substantial time engaged in sedentary screen-based activities. Phygital play, defined here as digital experiences that require real physical activity with gross motor skills, offers one possible way to create more active forms of screen time. However, before such experiences can be evaluated for broader developmental or movement-related effects, it is necessary to establish whether young children can access them successfully and whether they deliver the intended active gameplay experience.MethodsThis implementation-focused pilot evaluated the mobile movement-tracking game Joy of moving in 182 children aged 4–7 years from Irish primary schools. The study examined whether children could independently complete a child-led setup process to reach gameplay and, once gameplay began, whether sessions delivered the intended dose of movement under adequate tracking conditions. An iterative three-phase design was used. Feasibility was defined as successful independent setup and access to gameplay. Fidelity was defined as “dose with quality” during gameplay.ResultsAcross three iterations of the setup flow, independent setup success increased from 42.6% to 77.4% (χ²(2, N = 182) = 16.41, p < .001). Among children who successfully completed setup, gameplay completion remained consistently high (92.3%–94.9%), while implementation fidelity (“dose with quality”) improved from 66.7% to 91.1% as refinements to the setup guidance resulted in more reliable body positioning and tracking during gameplay.DiscussionThese findings suggest that many children aged 4–7 years were able to access gameplay independently and that, following optimisation of the setup process, the intended movement experience could be delivered with high implementation fidelity.
Jamie McGann
2 weeks 2 days ago
The present research explored whether AI-powered contextual flashcards facilitated through Anki had a positive impact on learners’ sentence-level fluency in EFL settings. This research employed a quasi-experimental pre-test–post-test control-group research design. Sixty EFL learners from the University of Zawia participated in the study and were allocated into either an experimental or control group where they received six weeks of AI-generated contextual flashcards or traditional instruction, respectively. The experimental group exhibited significant improvements from pretest to posttest (M = 10.45, SD = 2.11 to M = 17.82, SD = 1.95; t(29) = 14.52, p < .001), while the control group showed only marginal improvements (t(29) = 4.21, p < .01). Additionally, a significant difference was found between groups on the posttest (t(58) = 9.63, p < .001), d = 2.48. These results were discussed in relation to theories of second language acquisition and cognitive theory of learning. Findings suggest contextualized input combined with retrieval practice and spaced repetition can help learners develop fluency. Moreover, this study adds to the growing body of work related to AI-assisted language learning by showing its efficacy in developing fluency for communicative competence.
Romila Karrar Farh Karrar
2 weeks 6 days ago
This study investigates how Chinese English interpreting teachers in China position themselves in relation to the growing presence of artificial intelligence (AI) in interpreter training. While existing research has largely focused on student attitudes, tools, and curriculum-level discussions, the perspectives of interpreting educators remain less visible, despite their role in shaping pedagogical practice and professional norms. Drawing on a survey of 156 interpreting teachers in Master of Translation and Interpreting (MTI) programs, the study examines multiple dimensions of AI orientation, including readiness (enabling conditions and AI evaluative literacy), reported use of AI-enabled tools, critical evaluation practice, perceived usefulness and future intention, profession-related concerns (threat to professional autonomy and perceived labor devaluation), and restrictive responses (restriction behavior frequency, human judgment/skill protection, and restrictive orientation toward AI integration). Descriptive results indicate above-midpoint levels of readiness and perceived usefulness/future intention, alongside moderate reported use with substantial variability. Correlational analyses show that enabling conditions are positively associated with tool use and with perceived usefulness/future intention, whereas AI evaluative literacy shows a small negative association with tool use and perceived usefulness, and no association with future intention. Reported use is strongly associated with perceived usefulness and positively associated with future intention. Profession-related concerns are also evident: restrictive orientation is strongly associated with autonomy threat and positively associated with perceived labor devaluation, and autonomy threat and labor devaluation are positively related. In a regression model including role and teaching experience, autonomy threat and labor devaluation do not show unique effects on restrictive orientation, while teaching experience is strongly associated with restrictiveness. The findings suggest that interpreting teachers’ engagement with AI is not captured by adoption-related constructs alone. Alongside perceived pedagogical value, profession-related concerns and experience-linked boundary-setting shape how AI is positioned in interpreter training. The study extends technology acceptance approaches by incorporating constructs related to autonomy and labor value, and it highlights the need for AI integration strategies that address pedagogical use as well as professional implications.
Nina Y. Y. Cheung
2 weeks 6 days ago
This perspective article is grounded in professional experience and classroom observation, and its aim is to raise an issue and open it for discussion rather than to settle it. AI is often presented to teachers as a way to make educational work easier, faster, and more flexible, yet they are simultaneously confronted with a growing array of AI platforms, agents, automation systems, and technical courses. This creates a practical tension: if AI is meant to reduce teachers’ workload, it is not obvious why using it well should require them to keep learning new technical systems. One source of this tension, we argue, is the weak distinction between AI literacy and prompt engineering. We take AI literacy in language education to be a broad competence concerned with how teachers, students, and institutions learn to live, work, study, and teach responsibly in a world shaped by AI, whereas prompt engineering concerns more specifically how users design, refine, evaluate, and revise prompts to guide AI tools toward useful and responsible outputs. For English and English medium instruction (EMI) teachers, we propose that this difference matters because these teachers do not need to master AI technically; they need to learn how to make it serve teaching, learning, language support, content understanding, and student participation. We refer to this teacher-facing capacity pedagogical prompting and distinguish it from both broad AI literacy and technical prompt engineering. To make the idea concrete, we offer the Pedagogical Prompting Feedback Cycle as a teacher-oriented way of translating existing instructional expertise into AI-supported practice. We present it as a tentative conceptual model rather than a validated framework: it is meant to provoke inquiry and design, and it requires empirical validation across diverse languages, disciplines, and educational settings.
Ali Khodi
3 weeks ago
The present study aimed to examine the relationships between critical thinking and education for sustainable development (ESD) practices, as well as to determine the levels of critical thinking, and education for sustainable development ESD practices among teachers. The study sample consisted of (145) teachers from the elementary, preparatory, and secondary educational stages in the United Arab Emirates. A descriptive correlational research design was employed. Two researcher-developed scales were administered: The Teachers’ Critical Thinking Scale and the Education for Sustainable Development Practices Scale for Teachers, along with their psychometric properties. The results revealed that there was no statistically significant relationship between critical thinking and ESD practices. In addition, the findings indicated that the levels of both critical thinking and ESD practices were high.
Elaf Almansour
3 weeks ago
Reading texts on a screen has become a routine part of everyday life. While teaching reading in primary school, this calls for practical didactic approaches combining differentiation, digitalization and cross-curricular content. This study examines how digital differentiated cross-curricular reading materials (EFADIL) are implemented in Grade 3 classrooms in Austria. A mixed- methods design integrates teacher interviews (N = 34), classroom observations (N = 33), and student questionnaires (N = 605). The findings reveal tensions between perceived advantages, disadvantages, and enacted classroom practices. The materials provided a motivating learning environment and enabled differentiation and independent work. Simultaneously, they might lead to superficial and rapid task processing, along with variations in the use of the interactive digital features (e.g., glossary word function). Tasks requiring language production were often perceived to be more cognitively demanding. The study reveals instructional guidance, the teachers' role, and dimensions of instructional quality to be key factors in ensuring that digital differentiated materials support effective and inclusive reading instruction.
Fabian Feyertag
3 weeks ago
IntroductionThis paper aims to identify effective strategies for integrating Artificial Intelligence (AI) in higher education (HE), specifically addressing the challenges related to AI literacy, ethical concerns, and institutional variations. It provides a framework for incorporating an “embracing” approach to AI adoption, characterised by strategic incorporation of AI tools and underpinned by AI literacy and constructive AI skepticism.MethodsThe paper employs three main approaches. First, we explore current challenges faced by instructors in taking an embracing approach, particularly in relation to AI literacy. Second, we introduce instructor AI skepticism from a multi-layered perspective. Third, we present a series of case studies from universities in Northern Ireland reflecting on challenges of the embracing approach across STEM, Humanities, and Social Sciences, and how these challenges have been addressed.ResultsAn exploration of AI adoption across different subject areas in four case studies demonstrates substantial variability in AI literacy, AI skepticism and acceptance across disciplines. Nonetheless, broader common themes emerge across the case studies. These themes generally involve critical thinking and application of subject area-specific theories to new contexts. Crucially, structured instructional approaches significantly reduce misconceptions and enhance ethical AI usage.DiscussionThis study uniquely integrates discipline-specific case studies and institutional context analyses. It contributes to understanding AI literacy as a multidimensional skillset. It further positions AI literacy as a critical means of addressing the underrepresented instructors' multilayered AI skepticism, with an emphasis on ethical considerations and critical student engagement. Our recommendations highlight the necessity for targeted faculty training, clear institutional policies, and structured curriculum integration strategies to foster AI literacy, address instructor skepticism, and enhance ethical awareness effectively.
Juliana Gerard
3 weeks ago
Social networks have transformed the way young people relate to knowledge. The intended pedagogical use in higher education is still barely theorized. Our article proposes a theoretical design of a private Facebook group as a connectivist learning space for university students in the first levels of the Education career. The objective is to theoretically argue how a private Facebook group, structured under connectivist principles, can constitute an intentional learning node that favors the collective construction of knowledge about the role of the teacher in the digital age. Methodologically, we adopt a non-empirical approach, inscribed in the conceptual and theoretical tradition, which articulates the principles of connectivism of Siemens and Downes with the pedagogical possibilities of existing social networks. We include conceptual instructional design without reaching its empirical implementation. Our paper posits a closed, moderated space on a well-known social platform where students can reduce technological friction, activate authentic cognitive and relational connections. We conclude that the instructional design of the group is the determining condition for the emergence of connectivist learning and not the platform itself.
Cristián Londoño-Proaño
3 weeks 2 days ago
This paper conceptualises the SAGE Defend step, the sixth stage of the Structured AI-Guided Education framework, as a format-agnostic assurance checkpoint for AI-integrated higher education assessment. The study responds to a verification gap identified in earlier SAGE research, in which process documentation and AI interaction logs were found to support transparency but not, by themselves, to verify individual ownership of reasoning in group-based AI-integrated submissions. Adopting a design-informed conceptual approach grounded in design-based research principles, the paper integrates a multi-year programme of empirical SAGE studies, a structured synthesis of the assurance-task literature, and diagnostic observations from three Defend-proximate assessment implementations across undergraduate and postgraduate units at Central Queensland University. It distinguishes between assurance tasks that directly require students to demonstrate reasoning or performance, controlled assurance conditions that restrict the assessment environment, and corroborative assurance signals that provide corroborating but non-stand-alone evidence. On this basis the paper proposes a three-class assurance-task typology, an epistemic matching framework, and six design principles for embedding SAGE Defend within assessment sequences. It further argues that assurance should be distributed across the assessment sequence of a unit, so that each learning outcome is verified at a point and intensity proportionate to its stakes rather than concentrated in a single terminal examination. The paper frames this response as assurance by design, an approach that, echoing the established engineering principles of security by design and privacy by design, builds verification into the assessment sequence rather than appending it after the fact, and it names the compounding cost of the retrofitted alternative as assurance debt. Rather than presenting SAGE Defend as an oral examination model or claiming empirical validation of a single format, the paper positions Defend as a design principle through which educators can align verification tasks with the cognitive, professional, or technical competency being assessed. The contribution is therefore conceptual and practice-informed, offering a structured basis for the future empirical validation of specific Defend formats across disciplines, cohorts, and delivery modes.
Mahmoud Elkhodr
3 weeks 5 days ago
IntroductionThis scoping review examines the growing intersection between lifelong learning (LLL) and artificial intelligence (AI), focusing on whether the promises of assistance, personalization, and automation are aligned with the broader educational, social, and equity-oriented aims of LLL. The review addresses a central concern: although AI is increasingly promoted as a transformative resource for lifelong education, its deployment may not always correspond to the paradigms, geographical contexts, and vulnerability profiles emphasised in LLL literature.MethodsA scoping review was conducted using systematic search and reporting procedures informed by PRISMA 2020 and PRISMA-ScR. Two complementary corpora were analysed: 110 conceptual and empirical articles focused on LLL, and 79 articles addressing the application of AI within LLL contexts. The analysis combined categorical coding with exploratory statistical procedures, including chi-square tests and Cramér's V, to examine descriptive associations between LLL paradigms, geographical regions, vulnerability profiles, and the three dominant promises of AI.ResultsThe findings show that LLL paradigms display descriptive variation across regions and are associated with different vulnerability configurations, including socio-economic, territorial, demographic, and human-diversity-related dimensions. In contrast, AI-related articles show weak descriptive associations with LLL paradigms, geographical contexts, and vulnerability profiles. Although personalization appears as a prominent AI promise, assistance and automation are also present across the corpus, usually without strong conceptual alignment with the equity-oriented and socially grounded concerns of LLL.DiscussionThe review identifies a misalignment between the theoretical and policy-oriented framing of LLL and the practical deployment of AI in educational contexts. This suggests the need to move beyond technology-centred approaches and to design AI-supported lifelong learning initiatives that are explicitly connected to regional needs, learner vulnerability, social justice, and human development. The review contributes an analytical framework for examining how AI promises can be critically aligned with inclusive and transformative visions of lifelong learning.
Gerardo Alfredo Rodríguez