Frontiers in Education: Digital Learning Innovations
15 hours 39 minutes ago
IntroductionThis study evaluates interactive digital practice modules for grammar acquisition in German as a foreign language, focusing on verb-complement patterns involving verb meaning, complement selection, case marking, and prepositional choice. Drawing on construction-based pedagogy, it examines digitally supported retrieval practice, self-paced learning, response checking, and structured comparison.MethodsThe study analyzed an archived dataset of 61 Arabic-speaking university learners who participated in a six-week quasi-experimental pretest-posttest design. The experimental group used interactive digital modules, while the control group received conventional instruction. Because the recovered test forms differed in length, scores were converted into percentages, although this adjustment could not ensure measurement equivalence.ResultsBoth groups demonstrated statistically significant improvements in percentage scores from pretest to posttest. However, the difference in learning gains between the experimental and control groups was not statistically significant.DiscussionThe findings suggest that grammar learning improved under both instructional conditions without establishing a significant advantage for the digital modules. Because digital delivery and pedagogical organization varied simultaneously, their independent contributions could not be determined. Furthermore, percentage-based score conversion did not address potential differences in test difficulty, content coverage, or measurement precision. The study highlights the importance of comparable assessment instruments, transparent outcome measures, and designs capable of distinguishing technological effects from pedagogical effects in digitally mediated second language grammar instruction.
Nadia Naqrash
15 hours 39 minutes ago
IntroductionArtificial intelligence (AI) is transforming quality assurance in higher education by enabling adaptive, data-driven assessments of teaching performance and student learning. This integrative review synthesizes the purposes, methodologies, and outcomes of AI-enabled assessments, examines emerging technological trends, and identifies opportunities for improving teaching quality and learner support.MethodsAn integrative review was conducted using the Whittemore and Knafl framework, with study identification and selection reported in accordance with PRISMA guidelines. Of 367 records identified, 21 studies were included. Methodological quality was appraised using the Mixed Methods Appraisal Tool (MMAT). Descriptive and thematic analyses were supplemented by keyword network mapping using VOSviewer version 1.6.20.ResultsMost studies were multi-authored (71.43%) and published in interdisciplinary education and technology journals (57.14%), with publication activity concentrated in 2024 (28.57%) and 2025 (33.33%). The Western Pacific Region accounted for the largest proportion of studies (38.10%), while no studies originated from the African Region. Teacher performance was the predominant assessment focus (n = 11; 52.38%), followed by learner behavior (n = 4; 19.05%) and learner performance (n = 3; 14.29%). Teacher-focused assessments primarily analyzed learner-generated data using machine learning and natural language processing, whereas some learner-focused assessments employed video-based data and computer vision. Machine learning and natural language processing were the most frequently identified technological approaches (n = 6), followed by computer vision and multimodal or speech recognition approaches (n = 3). Most implementations followed a data-input, AI-processing, and output-generation structure, with varying levels of complexity. Thematic analysis identified four domains, while keyword network analysis revealed eight clusters.DiscussionThe findings highlight a fragmented assessment landscape in which teacher performance and learner behavior are predominantly examined independently. Although learner behavior may provide complementary evidence of teaching effectiveness, reciprocal relationships between teaching and learning remain insufficiently explored. Geographic disparities and methodological heterogeneity further constrain the generalizability and institutional applicability of existing evidence.ConclusionAI offers promising opportunities to strengthen teaching evaluation and learner support in higher education. However, broader geographic representation, methodological rigor, validated assessment frameworks, and equitable, human-supervised implementation are essential to ensure that institutional adoption is guided by robust evidence.Systematic Review Registrationhttps://osf.io/ugt4w.
Michael Joseph Dino
16 hours 59 minutes ago
IntroductionArtificial intelligence (AI) is becoming increasingly relevant to forensic practice and has potential applications in forensic education, including evidence interpretation, simulation-based learning, and decision-making. Then, evidence regarding its educational implementation remains fragmented, with limited consensus on pedagogical frameworks and competency development.MethodologyA systematic review was conducted following PRISMA guidelines. Studies published between 2020 and 2025 were identified through Scopus, PubMed, and IEEE Xplore. Eligible studies for the primary synthesis were required to include an identifiable forensic educational or training context, an AI-supported educational intervention, or a measured educational/training outcome. Technical studies of AI applications in forensic practice were analyzed separately to identify emerging competency and curriculum requirements.ResultsOnly a limited subset of the retrieved literature directly examined AI within an educational or training context. The available educational evidence addressed areas such as learner skill development, professional training needs, and AI-supported learning activities, but remained heterogeneous and methodologically limited. In contrast, a much larger body of technical forensic AI research demonstrated rapid advances in image analysis, classification, digital forensics, and decision support. These developments identify emerging educational requirements in algorithmic literacy, validation, explainability, bias assessment, data governance, and human oversight.DiscussionCurrent evidence indicates that AI should complement rather than replace human expertise. Effective implementation requires competency-based curricula integrating computational literacy, ethical reasoning, interdisciplinary collaboration, and experiential learning supported by explainable AI.ConclusionDirect empirical evidence supporting AI-enhanced forensic education remains limited. This scarcity represents an important evidence gap. Rapid technical advances nevertheless provide a strong rationale for developing and prospectively evaluating competency-based AI education in forensic curricula.
María Belén Morales-Cevallos
5 days 10 hours ago
Ahmed Lachheb
1 week ago
AI-mediated tools have expanded from specialised systems to generative and multimodal platforms, but research in primary English language education remains fragmented by technology, educational stage, and evidence type. This scoping review followed JBI methodology and PRISMA-ScR. Scopus, Web of Science Core Collection, and ERIC were searched with a cut-off of 21 September 2026 without publication-year limits. The synthesis included 119 reports, comprising 72 core empirical reports on primary English education, 31 adjacent empirical reports from related settings and 16 contextual sources. The synthesis organised the evidence into five recurring pedagogical functions. These covered oral interaction and speaking rehearsal, pronunciation and oral-reading feedback, and writing, vocabulary, and feedback scaffolding. They also included reading and multimodal meaning-making and teacher-facing assessment and materials design. AI-mediated tools supported structured language practice and feedback-guided activity. Reported performance differences varied across tasks, comparison conditions and outcome measures. Delayed outcomes varied, and transfer findings were tied to the tested material or task. Cross-study analysis developed an evidence-informed conceptual framework linking pre-task design and in-task regulation with post-task interpretation and subsequent instructional adjustment. The educational value of AI appears to vary according to task design, support during use, and professional interpretation. The review offers a function-based framework for selecting tools and an orchestration framework for connecting AI-mediated activity with classroom teaching.
Mengying Li
1 week ago
Generative AI has complicated how higher education distinguishes assistance from replacement in academic writing, and neither blanket prohibition nor permission resolves it, because the same GenAI-assisted practice may scaffold a student's reasoning or perform the work for students. This scoping review maps how empirical and systematically documented practice-based literature categorises responsible student GenAI use in academic writing, how these boundaries are translated into educational approaches, and what evidence exists for their evaluation and implementation. Following PRISMA-ScR, five databases were searched for English- and Spanish-language higher-education studies published between 1 January 2022 and 6 June 2026, supplemented by citation searching. Included studies reported original empirical data or first-hand documentation of an enacted practice, and contributed either to responsible-use boundaries or to a specified pedagogical assessment or guidance approach. In total, 31 studies were included. Charting yielded 192 practice-level records from 29 studies and 122 approach-level records from 29 studies. 27 studies contributed to both datasets. An interpretive synthesis of practice-level conditions, restrictions and explanatory notes identified six recurring principles: targeting learning objectives, preserving human agency, verifying GenAI output, transparency and accountability, authorship and human voice, and privacy, consent and fairness. These judgements are made at the micro level of the writing task but are sustained or eroded at the meso level of course design and the macro level of institutional guidance. Educational approaches were mainly pedagogical, but evaluation typically focused on short-term outcomes an relied on self-reporting. The included evidence suggests that responsible use depends on task purpose, expected intellectual contribution and the conditions surrounding AI assistance. The six principles offer a descriptive basis for developing and testing decision support. Further research could connect explicit boundaries with interventions and assess writing processes, textual outcomes and longer-term transfer.
Jiayu Tie
1 week ago
IntroductionTeachers' continuous professional development (CPD) is critical for effective digital technology integration in teaching and learning. This study explored how professional development initiatives support Grade 12 Business Studies teachers in integrating digital technologies in resource-constrained rural schools in South Africa.MethodsThe study adopted a qualitative case study design within the interpretivist paradigm. Data were generated through semi-structured interviews with six purposively sampled Grade 12 Business Studies teachers and analysed thematically.ResultsThe study findings revealed that teachers participated in formal continuous professional development initiatives, including departmental workshops and cluster meetings, as well as informal opportunities such as peer collaboration and self-directed online learning. However, most formal initiatives focused predominantly on technical skills rather than the pedagogical application of digital technologies. Consequently, teachers relied heavily on peer collaboration, professional learning communities and informal digital networks to address gaps in formal training.DiscussionThe study further highlights the need for sustained and contextually responsive professional development that aligns digital technologies with pedagogical practice. The study recommends evidence-based district support systems that integrate technical assistance with pedagogical mentorship to strengthen teachers' capacity to integrate digital technologies effectively in resource-constrained rural schools.
Siyabonga Andrias Magoso
1 week ago
The arrival of generative AI in every classroom has produced a crisis that is at once technological, epistemological, and pedagogical. We argue that the appropriate response is neither prohibition nor naive adoption, but a principled reconstruction of constructionism for a computational environment that is generative, context-sensitive, and dialogically accessible. Drawing on Peircean semiotics, Papertian constructionism, Polanyi's epistemology, and contemporary neural-network research, we develop a programmatic framework we call the New Constructionism. We make four connected contributions. First, we argue that generative AI introduces an indexical mode of computation: its relation to the empirical world may be direct, mediated, or incomplete, but in a situated human–AI configuration meaning is operationally conditioned by conversational history, artifacts, tools, observations, and action. Second, we introduce a three-dimensional analytical space — semiotic, psycho-epistemological, and pedagogical — in which learning paradigms can be located and compared. Third, we reinterpret the history of programming education as a movement from a predominantly symbolic environment toward a hybrid ecology in which symbolic execution, distributional generation, and situated reference coexist. This hybridity qualifies but does not cancel the indexical turn at the level of interaction and educational practice. Fourth, we articulate three transformations: from “object to think with” to “agent to think with”; from rule-first instruction to abductively initiated inquiry coordinating abduction, induction, and deduction; and from bricolage to artistic navigation. Navigational thinking has logical priority while the relevant problem space remains unstabilized, although it interleaves recursively with computational thinking as inquiry develops. The paper is conceptual rather than empirical: it advances a theoretical account intended to orient subsequent developmental research and pedagogical realization.
Ilya Levin
1 week 2 days ago
The urgency for sustainable consumption underscores the need to disseminate sustainability knowledge and promote environmentally responsible habits across societies. Due to its broad reach, social media has the potential to promote sustainable consumption by influencing habits and increasing environmental awareness. In this study, we investigate the role of social media through the design and strategic use of an Instagram account, WeSustainQtr, which aimed to disseminate sustainability knowledge and encourage sustainable consumption practices in Qatar and similar settings, through culturally relevant and solution-oriented content. Emphasizing a multi-theoretical framework through systematic and empirical qualitative research approach, we triangulated between secondary data from a scoping review and primary data of 420 initial survey and 415 post-course interaction survey responses. Baseline sustainability awareness was assessed through an initial survey, while the subsequent survey explored participants’ perceptions of sustainability and self-reported behaviors following their engagement with the account contents. Because responses could not be matched at the individual level, observed differences are interpreted as group-level patterns rather than evidence of individual behavioral change. Participants reported adopting a range of sustainable practices— from reducing single-use plastics and household energy consumption, to recycling — into the tapestry of their daily lives, with some reported influencing sustainability practices within their social circles. Findings suggest that social media can be used as a potential platform for sustainability education and user engagement by supporting knowledge sharing and collective participation. Reported engagement suggest that tailored implementations that introduced options for private participation and recognized culturally valuable themes may support followers’ engagement while also aligning with local norms towards privacy in the culturally sensitive context of Qatar and similar contexts. The findings also provide practical insights for integrating similar approaches into higher education curricula through structured learning activities that can encourage meaningful student engagement and behavioral change.
Maryam Al-Hail
1 week 2 days ago
The advent of generative artificial intelligence (AI) is transforming academic production and confronting the social sciences with the challenge of preserving critical thinking in the face of mechanically generated content. This research examines the perceptions and practices of social science lecturers and students at Ecuadorian universities regarding the use of AI, together with their motivations, constraints and the institutional policies currently in force. A mixed-methods design was adopted, combining qualitative and quantitative approaches. The instruments comprised semi-structured interviews with lecturers and students at national and Ibero-American universities; a non-probabilistic survey of 200 students; and a content analysis of seven sets of regulations from universities in Ecuador, all aligned with the framework of competencies identified by UNESCO-IESALC. The results show a moderate-to-high level of AI use (mean = 3.68), explained by perceived usefulness and extrinsic motivation rather than ease of use, and a gap between the demand for regulation (4.00) and perceived regulatory clarity (3.21); only one of the seven universities has a policy formally designated as such, and ethics predominates over professional and disciplinary development. The study concludes that the governance of AI in Ecuadorian higher education requires an evaluative redesign and discipline-differentiated competencies.
Abel Suing
1 week 2 days ago
BackgroundEffective communication is fundamental to high-quality healthcare. Communication skills training is a core component of medical curricula, yet opportunities to practice with standardized patients (SPs) are limited, costly, and can elicit anxiety in novice learners. Generative AI (GenAI) virtual patients may offer a scalable and accessible supplement. This quasi-experimental study examined the effect of practicing with a GenAI-patient on medical students’ self-reported nervousness, preparedness, self-efficacy, and self-perceived competence during their first SP encounter.Materials and methodsFirst-year medical students (n = 383) at the University of Amsterdam were allocated on the basis of the class schedule to a control group receiving standard e-learning preparation (n = 182) or an intervention group offered voluntary additional access to a GenAI-patient built on a Retrieval Augmented Generation (RAG) framework using GPT-4o (n = 201). Students completed a 31-item questionnaire after their first SP class. Primary outcomes were nervousness, preparedness, self-efficacy, and self-perceived competence. Group differences were assessed using Mann–Whitney U tests with Benjamini-Hochberg false discovery rate (FDR) correction. The primary analysis was by intention-to-treat, comparing all students allocated to the intervention with all control students; the comparison restricted to actual users of the tool was secondary.ResultsOf 180 students that completed the questionnaire, 84 were controls and 96 students were allocated to the intervention, of whom 49 had used the GenAI-patient. No statistically significant differences were found between groups on the outcomes after FDR correction. In the intention-to-treat analysis; all composite effect sizes were near zero (Cliff's delta −0.05 to 0.04, all 95% confidence intervals including zero), as were those in the per-protocol comparison (−0.12 to 0.07). Among tool users, experience was generally positive: 63%–71% gave positive ratings. The majority considered the GenAI-patient a valuable contribution to the standard class preparation (71%).ConclusionsVoluntary, single-session text-based practice with a GenAI-patient did not significantly reduce nervousness or improve self-assessed communication competence before a first SP encounter. Positive user experience suggests potential for the format, provided future iterations incorporate mandatory minimum engagement, multiple patient cases, automated feedback, and higher-fidelity multimodal interaction.
Remco Jongkind
1 week 5 days ago
IntroductionLarge language models (LLMs) are increasingly used to support evaluative tasks, raising questions about whether their judgements remain independent when users provide prior evaluative information. This study examined the context sensitivity of GPT-5.5 Instant-generated academic peer-review evaluations under positive and negative user-provided evaluative contexts.MethodSixteen academics evaluated two published manuscripts, classified for the study as relatively higher and lower quality, across four structured conditions. Each participant completed all conditions in separate ChatGPT sessions, yielding 64 peer-review outputs. Descriptive summaries of overall scores and publishability judgements complemented qualitative content analysis.ResultsThree themes emerged: alignment of evaluative emphasis with user-provided evaluative context, differences in the relative prominence of evaluation criteria, and explicit alignment with the user-provided evaluation. Mean ChatGPT scores were 8.74 and 8.84 under positive evaluative context, compared with 3.37 and 3.64 under negative evaluative context for the lower- and higher-quality manuscripts, respectively.DiscussionBecause all evaluative inputs included both a verbal evaluation and a numerical rating, the findings reflect sensitivity to the broader evaluative context and do not distinguish the contribution of verbal framing from numerical anchoring. The results highlight a potential limitation in treating ChatGPT-generated evaluations under comparable interaction conditions as independent second opinions and underscore the importance of interaction design in human–AI evaluation workflows.
Gülşah Subaşı
2 weeks ago
Generative AI is increasingly reshaping how learners engage in informal English learning beyond the classroom, making AI-mediated informal digital learning of English (AI-IDLE) an important emerging learning context. Yet research has predominantly focused on intra-personal psychological factors, the social dimensions of learning, particularly how learners construct their social identities in relation to AI, remain significantly underexplored. This qualitative study examines the social identity categories and their construction processes among 12 Chinese university EFL learners engaged in AI-IDLE. Drawing on Social Identity Theory (SIT), data from semi-structured interviews, reflective journals, and AI chat logs were analyzed using thematic analysis. The findings reveal two distinct identity pathways: learning-oriented academic identities, characterized by deep-learning commitment, humble mindset toward AI, and sustained immersion in the student role; and an instrumental identity, organized around a user category defined by transactional engagement, service satisfaction and surface learning approaches. Within this instrumental pathway, a more intensified fast-food consumer orientation emerged, which was explicitly self-identified by one participant and analytically inferred from behavioral patterns across several others. The study demonstrates that AI-IDLE does not simply erase or replace existing social identities but selectively reinforces and reconfigures them through group norms, prototypical markers, and evaluative comparison. These findings extend SIT by revealing how technological affordances and social configuration shape identity pathways that support or undermine meaningful learning engagement. Practical implications are proposed for AI developers, digital learning designers, educators, and curriculum designers to foster learning-oriented identity pathways in AI-mediated informal learning environments
Wu Fei
2 weeks ago
Generative AI is now a routine presence in higher-education classrooms, shifting the operative question from whether to permit its use toward when and how access should be structured within a learning activity. This paper reports a design-based research programme examining phase-based AI constraints, deliberately restricting AI access to specific phases of a collaborative teaching method rather than permitting or prohibiting it wholesale, as a design principle for structuring human and AI contributions within collaborative learning activities. The wider DBR programme examined AI-augmented designs across six collaborative teaching methods, administered repeatedly to five student groups over two academic years; the phase-based AI-constraint principle reported here as DP1 was developed through, and its evidence anchored in, Think-Pair-Share, with the other five methods contributing broader DBR context rather than independent confirmatory evidence. Its strongest evidence comes from an AI-free Think phase within Think-Pair-Share, replicated across three independent student groups spanning three degree programs, where learners consistently endorsed the AI-free phase as appropriate; this endorsement pattern was typically the highest-scoring item on its questionnaire. This design itself resulted from an earlier design cycle within the same programme: an initial administration permitting AI access during this phase prompted redesign after the instructor's classroom observation indicated that independent reflection was being displaced by AI-mediated responses. We treat this endorsement as an empirical finding, not as a direct assessment of the quality or originality of what learners produced, and not as evidence that the phase in fact protects independent cognitive engagement; that stronger claim is a theoretically motivated proposition this study does not test, and we frame DP1 accordingly across three explicit levels: an empirical endorsement finding, a transferable design principle of differentiating AI access by activity phase rather than deciding it once for a whole activity, and an untested theoretical proposition about the cognitive mechanism this differentiation may support. A complementary, independent stakeholder strand surveyed higher-education professionals at a practitioner festival session, offering a modest, exploratory social-validity signal: the phase-based procedure itself was almost universally endorsed, while personal adoption intention was somewhat more reserved. An exploratory qualitative coding of practitioners' open responses showed only slight inter-coder reliability (κ=0.17), so we do not use these responses to derive additional design principles, retaining only one response on function-based AI restriction as an illustrative example. The study is exploratory and formative, without a no-AI control condition or random assignment, and we frame its claims as design-principle evidence within a design-based research programme rather than as causal effect estimates. Anchored in independently replicated student endorsement data from Think-Pair-Share and supplemented by a practitioner social-validity signal, phase-based AI constraints offer higher-education instructors an actionable, low-friction design principle, one we expect to be conceptually transferable to other collaborative teaching methods, though this transfer remains to be empirically demonstrated.
Felix Höpfl
2 weeks 1 day ago
In an era of expanding digital finance, fintech innovation and AI-supported decision-making, financial literacy has become a critical educational priority for informed, inclusive and responsible participation in contemporary financial systems. This study examines financial literacy in digital and AI-supported educational contexts through bibliometric mapping and a PRISMA-based systematic review. A bibliometric analysis of 3,105 Web of Science and Scopus records identified the broader conceptual structure of the field, followed by thematic synthesis of 29 studies with educational relevance. Methodological quality was assessed using design-specific JBI Critical Appraisal Tools. The findings show that financial literacy and financial inclusion form the main conceptual hubs, while fintech and digital finance act as bridging themes. AI-related research is visible, but is mainly linked to prediction, decision-making, fintech adoption and digital services rather than direct pedagogy. The review further shows that digital tools are used across diverse educational and assessment contexts, with the more consistent evidence emphasizing structured instructional design and pedagogical integration rather than technology exposure alone. AI-related evidence remains limited and is predominantly analytical, diagnostic, advisory or supportive rather than directly pedagogical. The main research gap lies in the limited empirical evidence on pedagogically grounded AI-supported approaches to financial literacy education. Overall, the study provides an integrated framework for identifying conceptual structures, emerging digital and AI-supported trends, and critical pedagogical gaps relevant to evidence-informed and inclusive financial literacy education.
Nika Drnovšek
2 weeks 2 days ago
Despite scientific argumentation being a fundamental competency of the PISA assessment framework, prospective biology teachers often demonstrate insufficient development in evidence-based reasoning and rebuttal construction skills. Considering the limitations of large-scale implementation of individualized approaches in higher education, this study evaluates the effectiveness of Generative Artificial Intelligence (ChatGPT-4) configured as a “Socratic tutor” in developing argumentation skills among future educators. An 8-week quasi-experimental study involving 124 students from a pedagogical university employed traditional peer discussion methods in the control group (n = 62), while the experimental group (n = 62) utilized an AI agent that induced cognitive dissonance regarding socio-scientific issues. Analysis of Covariance (ANCOVA) of data obtained through Toulmin's Argument Pattern (TAP) and PISA rubrics confirmed that the experimental group achieved statistically significantly higher overall argumentation quality compared to the control group (F (1,121) = 14.23, p < .001, η2 = .105). Particularly, participants who received AI-assisted instruction showed marked improvement in forming rebuttals and evaluating counterevidence, which are considered among the most cognitively demanding components within the PISA scientific literacy framework (OECD). These findings suggest that a Generative AI-mediated Socratic instructional package can be an effective and scalable approach for developing epistemic cognition, and they propose a pedagogical model for integrating structured AI-based tutoring into teacher preparation systems aligned with international assessment standards. These findings should be interpreted as evidence for the effectiveness of the structured, AI-mediated Socratic instructional package as a whole, rather than as evidence of AI's inherent superiority over other methods; the relative contribution of the AI medium versus the structured pedagogical protocol it delivers was not directly tested in this design.
Gulrukhsor Surkhonidinovna Ergasheva
2 weeks 5 days ago
As generative artificial intelligence (GenAI) becomes increasingly integrated into education, understanding whether students possess the foundational conditions needed to benefit from AI-mediated learning has become an important educational challenge. This study conceptualizes dispositional foundations for AI-mediated learning as the configuration of ICT self-efficacy, attitudes toward technology and expectations of future ICT use that may support meaningful engagement with AI-mediated learning environments, and examines how these configurations relate to objectively assessed digital competence and to student characteristics. ICILS 2023 contains no measure of AI literacy or AI use, so these are candidate foundations rather than a direct measure of preparedness for AI. Using data from 118,764 Grade-8 students across 35 education systems participating in the International Computer and Information Literacy Study (ICILS 2023), we conducted sampling-weighted latent profile analyses on four internationally comparable indicators. Computer and information literacy (CIL) was examined as an external criterion using the BCH approach, and profile membership was predicted by socioeconomic background, gender, educational aspirations and ICT access. Four profiles emerged: average (55.9%), attitude-oriented (25.2%), low-disposition (11.0%) and efficacy-dominant (7.9%). Differences in CIL across profiles were modest, and students in the efficacy-dominant profile, who reported ICT self-efficacy more than two standard deviations above the mean, did not outperform the average profile in objectively assessed competence, a pattern we interpret as a confidence-competence mismatch rather than as high preparedness. On a common metric of average marginal effects, gender showed larger and more consistent contrasts than socioeconomic background. These findings suggest that the dispositional foundations for AI-mediated learning are distinct from computer and information literacy and should be understood as a multidimensional configuration of learner characteristics whose relevance to actual AI-mediated learning remains to be tested directly.
Vanessa Izquierdo-Álvarez
2 weeks 5 days ago
IntroductionThis study examined the relationships between the application of software technologies and the delivery of Business Education content in federal universities in South South Nigeria. Specifically, it determined the individual and joint relationships of Cognitive Artificial Intelligence (AI), Voice on the PowerPoint (VOPPT) lectures, Skillshare and Google Forms with Business Education content delivery.MethodsA correlational research design was adopted. The population comprised 306 respondents, and the entire population was studied. Data were collected using a structured questionnaire and analysed using simple and multiple linear regression at the .05 level of significance. Five research questions were answered and five null hypotheses were tested.ResultsCognitive AI had a significant positive relationship with Business Education content delivery (R = .574, R² = .330, β = .574, t = 12.235, p < .001). VOPPT also had a significant positive relationship (R = .626, R² = .392, β = .626, t = 14.011, p < .001), while Skillshare demonstrated a significant positive relationship (R = .578, R² = .335, β = .578, t = 12.363, p < .001). Google Forms recorded the strongest individual relationship (R = .804, R² = .646, β = .804, t = 23.572, p < .001). Collectively, the four technologies explained 65.6% of the variance in Business Education content delivery (R = .810, R² = .656, adjusted R² = .652), with the overall regression model being significant, F(4, 301) = 143.634, p < .001. In the joint model, Skillshare (β = .185, t = 2.921, p = .004) and Google Forms (β = .808, t = 14.746, p < .001) made significant unique contributions, whereas Cognitive AI (β = −.107, t = −1.395, p = .164) and VOPPT (β = −.062, t = −.775, p = .439) did not.DiscussionThe results indicate that the four software technologies are individually associated with Business Education content delivery, but their unique contributions differ when considered simultaneously. The findings highlight the importance of strategic technology integration, lecturer capacity development, reliable digital infrastructure and institutional support for the effective delivery of Business Education content in federal universities.
Edet E. Okon
2 weeks 6 days ago
BackgroundEngagement and achievement gaps in higher education remain persistent, especially for students from lower socioeconomic and minority cultural backgrounds in digital learning environments.ObjectiveThis study examined whether participation in LMS-based courses implementing a Culturally Responsive Digital Differentiated Learning (CR-DDL) Framework was associated with greater improvements in student engagement and learning outcomes and with narrower observed engagement disparities compared with standard LMS-based instruction.MethodsAn explanatory sequential mixed-methods design was used with 252 undergraduates in public health and agrotechnology programmes at three Indonesian institutions, comparing CR-DDL courses (n = 129) with standard LMS-based courses (n = 123). Quantitative data included pre- and post-intervention Higher Education Student Engagement Scale scores, LMS-derived behavioural indicators, and final course scores. Outcomes were analysed using linear mixed-effects models with a random intercept for course to account for the clustering of students within the six participating course clusters. Eighteen students also participated in semi-structured interviews to provide contextual insight into their learning experiences.ResultsThe CR-DDL group demonstrated a larger increase in total engagement than the comparison group (mean change, +0.52 vs. + 0.13; group × time estimate = 0.31, SE = 0.07, approximate Wald 95% CI 0.17–0.45, p < 0.001). Greater increases were also observed in LMS-based participation. After adjustment for prior-semester GPA and course-level clustering, CR-DDL participation was associated with a 3.41-point higher final course score on the 0–100 reporting scale (SE = 1.12, approximate Wald 95% CI 1.21–5.61, p = 0.003). Lower-SES students in CR-DDL courses showed larger descriptive engagement gains than their counterparts in comparison courses. Because the formal group × time × SES interaction could not be verified from the currently reported summary results, the SES findings are interpreted as exploratory subgroup patterns rather than definitive evidence of effect modification. Documented interview excerpts provided limited contextual insight into students’ experiences but were not treated as independently reproducible qualitative evidence.ConclusionThe quantitative findings provide evidence that CR-DDL was associated with improved engagement, LMS participation, and course performance and with a narrowing of observed engagement disparities. The available qualitative material provides limited contextual insight into students’ experiences but should be interpreted cautiously because complete original qualitative records are currently unavailable.
Reno Renaldi
2 weeks 6 days ago
IntroductionGenerative artificial intelligence has diffused rapidly among learners, but a question remains whether that reach becomes useful for learning or stalls at access. This distinction matters most where learners already have connectivity, so the barrier is no longer reaching the technology but converting access into learning use.MethodsThis study draws on a survey of 14,653 computer science learners across 166 countries, a population for whom first-level access is broadly shared. A two-part model separates whether an everyday user converts to any learning use from how widely converters then use the tool, estimated across World Bank income tiers.ResultsEveryday adoption is high across income levels and is not lower in poorer settings. The quantity that varies is conversion. Among everyday users, those in low-income countries are markedly less likely to convert to any learning use, at an odds ratio of 0.49 (95% CI 0.27, 0.90), while breadth among converters does not differ by income tier. A latent typology shows that disengaged learners are found across every income tier rather than in one region, and the association between AI use and self-rated proficiency is strongest where other support is weakest.DiscussionThe gap sits at the threshold of first learning use, not in the breadth beyond it. This article names the pattern the access-conversion gap and develops it as a measurable construct at the second and third levels of the digital divide. The findings describe a connected population of engaged learners, so the divide to address is in use, not access.
Krishnashree Achuthan