Frontiers in Education: Digital Learning Innovations
21 hours 57 minutes ago
Lived experience is a powerful medium for learning, yet most educational and workforce systems lack tools to recognize complex competencies developed in homes, communities, and workplaces. This paper introduces LivedX, a socio-technical platform designed to make lived learning visible, valid, and portable across education and employment. LivedX integrates three core components: an integrative taxonomy of human capacities aligned with NACE; a human–AI annotation pipeline for coding narratives of lived experience; and a digital platform that translates narrative evidence into interpretable profiles and credential-ready artifacts. Drawing on iterative conceptual work and large-scale human coding of learning narratives, we describe how the LivedX taxonomy was developed by mapping existing frameworks, building a hierarchical codebook, and refining it through consensus-oriented annotation practice. The system was empirically evaluated on a corpus of approximately 3,000 learner narratives, achieving strong inter-rater reliability (ICC = 0.83) and improved automated competency prediction performance, including a 12% increase in F1 score and a 5% increase in AUROC relative to baseline models. We outline a workflow in which learners submit narratives, a natural language processing engine proposes competency mappings with embedded explanations, and human reviewers retain final judgment. LivedX offers a scalable model for equity-oriented recognition of non-formal learning.
Julia Mahfouz
1 day 6 hours ago
Inclusive classrooms demand learning systems that accommodate diverse student cognitive profiles while supporting the development of high-level mathematical reasoning. However, empirical evidence on the integration of artificial intelligence (AI), educational robotics, and adaptive scaffolding in inclusive mathematics learning remains limited. This study aims to analyse the effects of an AI-based robotic learning environment with adaptive scaffolding on improving students’ mathematical reasoning abilities in inclusive classrooms. The study used a quasi-experimental design with a mixed-methods approach involving 47 slow learners in an inclusive junior high school. The intervention consisted of a hybrid physical-digital learning environment that combined a line-follower robot, contextual dioramas, an interactive tablet application, and an AI system for real-time assessment and tiered assistance, including problem interpretation, procedural guidance, and symbolic support. The results showed a significant increase in students’ mathematical reasoning abilities, with the average score increasing from 47.96 to 60.66 (p < 0.001) and a very large effect size (Cohen's d = 1.58). Improvements were observed in most slow-learning students, demonstrating the system's potential to support adaptive differentiation in inclusive learning. These findings suggest that integrating tangible robotics with AI-based adaptive scaffolding can serve as an effective cognitive support system to improve students’ mathematical reasoning. This research contributes to the development of an AI-based robotic learning environment model that supports deep learning and pedagogical differentiation in inclusive mathematics education.
Riawan Yudi Purwoko
6 days 5 hours ago
With the advancement of technologies, the use of smart learning applications has been prevalent in school education, specifically in science inquiry learning. Many studies have reported the importance, significance, and learning techniques using Inquiry-based learning, but very few studies have evaluated the multi-year analyses of smart learning applications on students' evaluations along with their impact at institutional levels. Inquiry-based learning, a student-centered pedagogical approach in which students engage in questions, problems, or scenarios with minimal direct instruction, has become recognized for its significance and effectiveness in developing critical thinking, problem-solving, and independent learning skills. This paper presents a three-year multi-cohort quasi-experimental evaluation of a smart inquiry-based mobile application named MDISTIL (Mobile-Based Digital Storytelling for Inquiry Learning) implemented with different Grade-8 student cohorts between 2023 and 2025. The aim of the study is to design and evaluate the repeated implementation of a smart science inquiry-based learning application with 240 Grade-8 students across three consecutive years. Mobile Science Inquiry (MSI) framework is used to evaluate the quantitative and qualitative data using paired-sample t-test, one-way analysis of variance ANOVA, and an evaluation of a questionnaire measuring the usability of the application. Results show that the average pre-test scores were 10.75 for girls and 9.51 for boys, while the corresponding post-test scores were 12.35 and 11.74, respectively. Across the three annual cohorts, participating students demonstrated consistent improvements in post-test scores following MDISTIL-supported inquiry activities. The descriptive results indicate that both boys and girls demonstrated improvements in post-test scores following participation in the MDISTIL-supported inquiry activities. While differences in mean scores were observed between genders, the study design and available analyses do not permit firm conclusions regarding gender effects or gender-by-intervention interactions. Teacher observations suggested that students displayed varying levels of familiarity and confidence when initially handling mobile devices, while many students demonstrated strong engagement during the final presentation activities. Further, the qualitative evaluation indicates that students were satisfied with the usability of the application. Overall, this study highlights positive learning and usability patterns associated with the repeated implementation of a smart inquiry-based learning application and provides practical insights for educators and learning system designers.
Sameena Javaid
1 week ago
This study investigated the use of eye-tracking technology in higher education, as well as its implications for teaching and feedback, through a systematic review guided by the PRISMA 2020 guidelines (databases and registers only). Using keywords relating to eye tracking, feedback and the educational context, 2,544 records were retrieved from Web of Science-indexed journals published by Elsevier, Springer, SAGE, Taylor and Francis, Wiley and Frontiers. Following screening, 2,490 records were excluded, leaving 54 full-text reports to be assessed for eligibility. A total of 27 empirical studies conducted in higher education contexts involving undergraduate and graduate students, adult learners and university instructors met the inclusion criteria and were synthesized. Analysis of the learning environment showed that most studies were conducted in controlled laboratory settings (88.8%, n = 24), with a smaller proportion conducted in university classrooms (11.2%, n = 3). Interactive digital materials and standard/static digital materials were equally represented, each accounting for 44.4% of the reviewed studies. The findings were organized into four outcome domains: (1) cognitive processing and learning performance, (2) attention allocation and engagement, (3) affective states and (4) instructional skills and teaching quality. Overall, this review provides evidence-based insights into how eye tracking can inform the design and evaluation of feedback in face-to-face, blended and online higher education environments.
Oktay Cem Adıgüzel
1 week ago
IntroductionSelecting an undergraduate major is a crucial academic decision that significantly influences students' long-term learning trajectories and career outcomes. Despite policies promoting flexible curriculum design and informed major–minor combinations, students often make suboptimal choices due to limited guidance and external influences.MethodsTo address this challenge, this study proposes a LightGBM Leaf-Embedding Neural Blender (LENB) framework that integrates gradient-boosted tree representations with neural embedding layers to predict major-course selection.ResultsUsing a comprehensive dataset of 19,921 undergraduate students across multiple academic streams— Arts, Commerce, Science, Language, and Other—the proposed model achieves high predictive performance with accuracies up to 87.50% and strong cross-validation stability. The framework incorporates class balancing (SMOTE with class weighting), AdamW optimization with early stopping, and dropout-based regularization to ensure robustness and generalization. Furthermore, SHAP-based interpretability analysis provides clear insights into the factors influencing major selection, revealing that MINOR, CGPA, and Programme are the most dominant predictors, reflecting the importance of academic background and curricular structure. In contrast, socio-demographic attributes such as Gender and Religion exhibit relatively lower influence compared to academic features, indicating reduced reliance on sensitive attributes rather than complete bias neutrality.DiscussionComparative evaluation against RandomForest, XGBoost, CatBoost, LightGBM, and TabNet demonstrates that the proposed LENB framework achieves competitive performance across multiple streams and outperforms baseline models in several cases, particularly in the Arts and Commerce streams. However, in the Language stream, Random Forest achieves slightly higher accuracy, indicating that the effectiveness of hybrid models may vary depending on the underlying data structure. By integrating explainable artificial intelligence with a hybrid learning architecture, the proposed framework delivers transparent, data- driven insights that can support educators, policy-makers, and career counselors in guiding informed academic decision-making.
Emi Kalita
1 week 1 day ago
This paper examines whether OpenClaw, an open-source AI agent framework, can serve as the foundation for adaptive educational AI systems. We analyse OpenClaw's architecture against established requirements for adaptive learning: student modelling, curriculum delivery, difficulty adjustment, and assessment feedback. A proposed framework maps OpenClaw's core features (persistent memory, modular skills, and retrieval-augmented generation) onto educational functions including adaptive tutoring, self-regulated learning support, and data sovereignty compliance. We extend the analysis with (a) a three-year total-cost-of-ownership comparison between local graphics processing unit (GPU) deployment and frontier cloud application programming interfaces (APIs), (b) a concrete integration path with open-source Item Response Theory libraries (py-irt, mirt-Python, EduCDM) to give difficulty adjustment a psychometric foundation, (c) a multimodal accessibility layer combining open-source automatic speech recognition (ASR; Whisper) and text-to-speech (TTS; Coqui) for learners with visual or motor impairments, and (d) a Consolidated Standards of Reporting Trials (CONSORT)-style design for a single-course feasibility pilot. Resultssuggest that OpenClaw offers significant advantages in cost, customisation, and data control, while challenges remain in technical deployment complexity, psychometric maturity, and the absence of empirical validation. The paper concludes with a concrete pilot protocol and specific research directions for classroom implementation.
Simon Baradziej
1 week 4 days ago
Difficulties in connecting visual representations with formal deductive reasoning continue to hinder students’ success in geometry proof construction. This study examined the association between generative artificial intelligence (AI)-supported instruction and Senior High School students’ geometric reasoning and proof construction. A quasi-experimental pre-test-post-test non-equivalent control group design was employed involving 86 students from two intact classes in a public senior high school in Ghana. The experimental group received generative AI-supported instruction, while the control group received conventional instruction. Data were collected using a Geometry Reasoning and Proof Test and a perception and attitude questionnaire. Independent-samples t-tests, paired-samples t-tests, ANCOVA, and descriptive statistics were used for data analysis. The findings revealed that students exposed to generative AI-supported instruction achieved significantly higher geometric reasoning and proof construction scores than students receiving conventional instruction, even after controlling for pre-test performance. Students in the experimental group also reported favorable perceptions and attitudes toward the instructional approach. The study contributes to the growing literature on AI-supported mathematics learning by demonstrating the potential of generative AI to function as a cognitive scaffold that supports the transition from visual intuition to deductive reasoning in geometry. The findings suggest that generative AI-supported instruction may provide a useful instructional approach for enhancing students’ geometric reasoning and proof construction.
Isaac Davor
1 week 4 days ago
Generative Artificial Intelligence (AI) is increasingly embedded in student support systems across primary and secondary education (K-12) and higher education, offering new possibilities for personalized learning, adaptive feedback, and scalable academic assistance. At the same time, its integration raises complex ethical challenges that extend beyond technical issues to core educational values. This conceptual paper examines ethical dilemmas associated with AI-powered student support, focusing on how they appear differently across educational contexts. While concerns such as data privacy, equity and algorithmic bias, transparency, accountability, and the preservation of human relationships are shared across sectors, their implications are context sensitive. In K–12 education, ethical priorities center on developmental vulnerability, child protection, parental consent, and teacher mediation, while in higher education, attention shifts toward student autonomy, academic integrity, surveillance, and institutional accountability. Ethical frameworks often generalize across educational levels, overlooking main developmental and institutional differences. This study provides/suggests a structured, context-sensitive ethical framework to guide the responsible implementation of AI-powered student support across educational settings.
Kleopatra Nikolopoulou
1 week 5 days ago
IntroductionOnline learning effectiveness depends on pedagogical design rather than platform capabilities. This study evaluates the BOOOM model (Boosting Cognition, Online Collaboration, Organizing Instruction, Overseeing Metacognition, Motivating) in teacher education.MethodsA quasi-experimental pretest-posttest control group design was employed with 90 students (Group B: experimental, n = 45; Group A: control, n = 45) over 12 weeks in Google Classroom. Data were collected through achievement tests, engagement scales, self-efficacy scales, satisfaction questionnaires, and interviews.ResultsThe BOOOM model exerted a statistically significant positive effect on academic achievement, cognitive and behavioral engagement, and pedagogical self-efficacy: F(1, 87) = 28.54, p < .001, partial η² = .247. No significant differences were found for emotional engagement or platform usability.DiscussionThe effectiveness of online learning depends more on the quality of pedagogical design than on the platform itself. The BOOOM model is proposed as a practical structured approach for active learning in teacher education.
Dana Shrymbay
1 week 5 days ago
The rapid integration of Artificial Intelligence (AI) into higher education underscores the urgent need to assess and foster robust AI literacy among future teachers. This cross-disciplinary competence is a critical, ethical, and equity-oriented foundation that empowers educators to personalize instruction and promote inclusive learning. However, current literature exhibits a methodological gap due to the scarcity of validated Spanish psychometric instruments. Furthermore, previous studies often evaluate pre-service teachers homogeneously, overlooking specialization-specific asymmetries and hindering the development of tailored, realistic, and effective curricula. To address this limitation, this study validates the Spanish adaptation of the Meta AI Literacy Scale (MAILS) to multidimensionally assess AI literacy. The 34-item instrument—measuring application, understanding, detection, ethics, creation, self-efficacy, and self-competence—was administered to a sample of 471 students in the Faculty of Education at the University of Salamanca across Sport and Exercise Sciences (48.6%), Primary Education (34.4%), Early Childhood Education (11.9%), and minor academic tracks (5.1%), providing high ecological validity. Psychometric validation demonstrated excellent internal consistency (overall Cronbach's alpha = .903). Overall, students exhibited strengths in practical application and self-efficacy, but only moderate levels of theoretical understanding and detection. ANOVA tests revealed significant competency disparities across seven of the nine assessed dimensions. Sport Sciences students excelled in creation (M = 7.62) and application (M = 17.22) due to their frequent use of performance-monitoring technologies. Conversely, Early Childhood Education students exhibited high digital vulnerability, scoring lowest in creation (M = 5.41), application (M = 14.38), and problem-solving self-efficacy (M = 6.34). Primary Education students demonstrated the highest resistance to algorithmic persuasion (M = 9.65). Transversely, the ethics dimension showed no significant differences (F = 0.053, p = .948), reflecting a shared, highly developed deontological awareness. In conclusion, these findings highlight the need to abandon one-size-fits-all digital training in higher education. Designing tailored curricular pathways that address the specific epistemological, technical, and affective demands of each degree is essential. Only through targeted preparation can AI literacy drive equity and social justice, empowering future educators to implement AI, open educational resources, and Universal Designfor Learning frameworks safely, ethically, and inclusively.
Olga Arranz-García
1 week 5 days ago
Background/objectivesArtificial intelligence (AI) Chatbots have benefited students in many ways but several issues have arisen with regards to ethical aspects and students’ critical thinking skills. This study aims to provide insights to the prevalence and frequency of AI Chatbot usage among Faculty of Medicine, Universiti Kebangsaan Malaysia (FPER UKM) undergraduate students, and explore the behavioral and psychological factors associated with AI Chatbot use and dependency.MethodsThis study employed a cross-sectional design. An online survey involving 300 undergraduate students from FPER UKM consisting of medical, nursing and emergency medicine courses was conducted using questionnaires that were adapted and screened from the Bergen Facebook Addiction Scale (BFAS) and other literature. All items were rated on a five-point Likert scale with an inclusion of four open-ended questions at the end of the questionnaire for participants to give their opinion on AI chatbot usage. Two main factors contributing to AI dependency, which are academic stress and academic self-efficacy, were measured using the same scale. Data analysis of the collected responses was then done via bivariable correlation and multivariable regression using SPSS version 29.ResultsOur study included 300 students in FPER UKM which mainly consisted of Malaysian students with only 4 of them being non-Malaysian. It was found that the prevalence of AI Chatbot usage among all undergraduate courses to be 100%. Most of the respondents used AI tools for one to three hours per week with the main purpose of studying or revision. The factors most strongly associated with AI dependency score was weekly usage of more than four hours (p < 0.001, β = 0.301), followed by academic stress (p = 0.001, β = 0.188). The final multiple regression model was significantly associated with AI Chatbot dependency (p < 0.001) with R2 of 0.182.ConclusionAll undergraduate students have used AI Chatbot previously with its main benefit on studying and revision. AI Chatbot dependency is significantly associated with duration of AI Chatbot usage and level of academic stress. Further studies are warranted to classify dependency on AI Chatbot and explore other potential correlates related to over-reliance on AI Chatbot.
Jonathan Wei De Tan
2 weeks 1 day ago
IntroductionIndonesia's extraordinary cultural diversity, which encompasses more than 1,340 ethnic groups and 718 regional languages, presents both opportunities and challenges for early childhood education. Although multicultural education is recognized as essential during the formative years, the integration of artificial intelligence (AI) models with culturally responsive pedagogy remains largely unexplored, particularly in early childhood education. This study investigates the structural relationships among teachers’ multicultural competencies (TMC), AI technology readiness (ATR), institutional support systems (ISS), the implementation of AI-based multicultural learning media (AMLMI), and the effectiveness of multicultural education management (MEME).MethodA cross-sectional survey was conducted among 500 kindergarten teachers in Central Java Province, Indonesia, using stratified random sampling. Data were collected using a structured questionnaire comprising 48 items, rated on a five-point Likert scale. The proposed structural model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0, including bootstrapping with 5,000 subsamples for hypothesis testing.ResultsThe measurement model demonstrated adequate convergent validity (all external loadings >.70; AVEs > .50) and strong construct reliability (Cronbach's Alpha > 0.88; CR > 0.91). The structural model showed that ISS was the strongest predictor of AMLMI (β = .571, p < .001), followed by TMC (β = .219, p < .001) and ATR (β = .163, p = .001), which collectively explain 77.1% of the variance. AMLMI, in turn, is strongly associated with MEME (β = .832, p < .001; R² = .692). Full mediation was confirmed for all indirect paths (VAF=100%), with ISS providing the largest indirect effect on MEME through AMLMI (β = .475, p < .001).DiscussionThe findings indicate that institutional support is the strongest predictor of AI-based multicultural education outcomes, while teachers’ multicultural competence and AI technology readiness serve as significant complementary predictors.The pattern of full mediation suggests that these factors must be implemented through the effective application of AI-based media to enhance multicultural education management. This study contributes an integrated empirical model that bridges the fragmented literature on multicultural pedagogy, AI readiness, and institutional support, offering actionable insights for policymakers and practitioners in culturally diverse early childhood education.
Luluk Elyana
2 weeks 4 days ago
Despite the rapid digital transformation in the UAE, research often relies on quantitative metrics that fail to capture the lived experiences of students. A significant gap exists in understanding how specific cultural and academic pressures in the UAE intersect with social media usage to influence mental well-being beyond surface-level usage statistics. This study explores how social media usage influences the mental well-being and educational engagement of university students in the UAE, highlighting the tension between social support and psychological stress. Using a qualitative, interpretivist framework, we conducted semi-structured interviews with 15 single university students between the ages of 18 and 24 who resided in the UAE, attended a UAE educational institution, and had used social media for at least six months. We selected 15 participants for this study because it allowed us to develop themes that the data strongly supported across their lived human experiences. We limited participation to single individuals to isolate the impact of social media from variables related to marital or domestic responsibilities. We selected this age range because some social media platforms only permit users over 18 to create accounts, and individuals older than 24 are generally no longer university students because they have completed their degrees. We analyzed the data using Braun and Clarke's thematic analysis approach. The analysis moved beyond descriptive listing to reveal a digital paradox categorized into four themes. Findings revealed that social media functions as a vital tool for educational support and professional networking (e.g., LinkedIn and WhatsApp groups) while simultaneously acting as a psychological stress through curated perfectionism and social comparison. Notably, findings also suggest that students are not passive victims of algorithms but active agents who use content curation as coping mechanisms to overcome digital distractions and maintain emotional regulation. This study contributes to digital well-being literature by providing a contextualized concept of intentional engagement within the UAE's unique cultural landscape. Practically, it offers evidence for the need of designing targeted interventions that promote digital literacy and healthier social media boundaries. While limited by its English-speaking sample, yet this study clearly highlights the strategies to promote healthier social media engagement in diverse linguistic and cultural context.
Seemal Ali
2 weeks 6 days ago
BackgroundThe current study is concerned with the efficiency of AI virtual tutor ‘TEAMIGO’ in the development of emotional self-regulation in children and adolescents with neurodevelopmental disorders (NDDs).MethodAfter the single-arm longitudinal design without a control group, we managed to obtain a sample consisting of 110 subjects (including 55 participants with ASD and 55 with ADHD) within the age range of 6–18 years. The study employed relaxation exercises along with emotion registration, i.e., the process of identifying one's emotions with the help of audio and video cues to reduce disruptive behavior.ResultsThere was a statistically significant decrease in disruptive behavior: 35.5% (p < .001) for the ASD group and 23.6% (p < .01) for the ADHD group.DiscussionAccording to SEM analysis, there was no metric invariance, which indicates that the way in which AI functions therapeutically is different among patients with distinct neurological profiles, acting as a compensation measure for alexithymia in the case of ASD rather than a measure against executive impulsivity in the case of ADHD.
Celia Gallardo Herrerías
2 weeks 6 days ago
IntroductionThis study examines how a structured digital pedagogical design supports the development of research competence among primary school students in Kazakhstan. Research competence is understood as an age-appropriate ability to formulate questions, work with information, organize evidence, use digital tools purposefully, and present simple inquiry-based conclusions.MethodsThe study used a quasi-experimental non-equivalent control group pretest–post-test design with 118 Grade 3–4 students: 59 in the control group and 59 in the experimental group. The experimental group participated in a nine-week digitally supported inquiry intervention, while the control group followed regular instruction. Data were collected through a diagnostic test, a student questionnaire, an analytic rubric for digital research products, and teacher interviews.ResultsThe experimental group showed a substantial increase in research competence from pretest to post-test, from 2.39 to 3.27 on a four-point scale, t(58) = 7.18, p < .001, Cohen's dz = 0.94, 95% CI [0.63, 1.13], whereas the control group showed only a small non-significant gain. Motivational indicators also increased more strongly in the experimental group: interest in research activity rose from 38% to 71%, confidence in using digital resources from 42% to 76%, and perceived usefulness of digital technologies from 41% to 69%. Rubric assessment showed a higher proportion of advanced digital research products in the experimental group than in the control group, χ²(2, N = 118) = 17.19, p < .001, Cramer's V = .38.DiscussionThe findings suggest that digital technologies may support research competence when they are embedded in guided inquiry, teacher mediation, formative feedback, and aligned assessment, rather than used as separate technical tools. Urban and rural/small-rural subgroups both improved under the intervention, although contextual differences remained.
Balzhan Kurebay
3 weeks ago
As Generative Artificial Intelligence (GenAI) becomes ubiquitous in educational settings, concerns increase about how it may affect learners’ identities in relation to the learning process. This study investigated how students’ identities relate to their use of GenAI while learning within an AI-ready pilot high school. Drawing on focus groups with 39 students, we used grounded theory to develop the AI Relational Identity (AIRI) framework. AIRI is defined as the ways learners understand, position, and recognize themselves in relation to GenAI as they negotiate agency and authorship. Findings reveal four distinct AIRIs: Consumers, Technicians, Philosophers, and Innovators. Each group represents a different configuration of technical proficiency and sociopolitical critiques. This study also describes how these identities interact with broader identities—such as learner, maker, and digital identities—and identifies specific instructional strategies contributing to the development of each AIRI. Unlike traditional disciplinary identities, AIRI focuses on the co-constituted relationship between the human and the GenAI agent. This research contributes a new theoretical lens for understanding GenAI-mediated learning and offers promising practices for educators seeking to integrate GenAI in ways that deepen critical thinking and support empowered student citizenry.
Jennifer Darling-Aduana
1 month ago
IntroductionPrevious research on digital music teaching in primary education has largely focused on learning outcomes, while often neglecting the underlying learning processes that contribute to these gains. This study investigates the extent to which digital music-driven instructional activities enhance primary students' music learning engagement and support the development of music literacy skills in Jiangsu, China.MethodsGrounded in a mixed-methods design, the study employed a pre-post engagement questionnaire and semi-structured interviews to capture both quantitative and qualitative insights. Quantitative data were analyzed using descriptive statistics, paired-samples t-tests, and effect size calculations, while qualitative data were examined through thematic analysis.ResultsThe results revealed significant improvements across all four dimensions of engagement—behavioral, cognitive, emotional, and social—with large effect sizes, indicating a substantial impact of the intervention. The findings also showed improvement in students' music literacy skills, particularly in rhythm recognition, pitch discrimination, melody identification, and tempo and dynamics understanding. Qualitative findings further demonstrated that students perceived digital music activities as interactive, enjoyable, and effective in enhancing their understanding of musical elements such as rhythm, pitch, and melody.DiscussionThese findings suggest that digital music-driven instructional activities can support primary students' engagement and music literacy development when they are designed as structured, interactive, multimodal, and pedagogically meaningful learning experiences.
Sheng Chen
1 month ago
The integration of Artificial Intelligence (AI) in education presents a transformative opportunity to enhance teaching and learning processes. This study aims to explore the multifaceted impacts of AI on Management education. The following are the purposes of this study:-
To assess the awareness regarding AI tools and their usage in management education among faculty members.To identify the repetitive and tedious works of academicians (MBA Faculty) which the faculty are desirous of offloading to AI.To study whether AI is considered as a threat to Management Faculties?To study how Management Faculties perceive and anticipate AI will change their Job Description.To forecast the anticipated future roles of AI in EducationTo analyze the apprehensions and expectations of faculty members as to how AI will affect Job opportunities in the education sector?. The study employs qualitative and exploratory research design. Qualitative data analysis was done using the software - ATLAS.ti to generate word clouds, perform sentiment analysis and also for generating themes through the codes generated by the software. The analysis of data collected through interviews of academicians and faculty members from Management institutes in India gives conclusive sentiments about each of the objectives mentioned (sentiment analysis) and the themes and codes generated as a result of content analysis give useful insights on all the 6 research objectives mentioned above. The interpretations based on the themes and codes generated by the software gave valuable and interesting insights and findings about the perceived threats and opportunities posed by Artificial Intelligence in Management Education in India and some very important blind spots and contradictions have emerged in this study.
Kumar Mohit Spring
1 month ago
IntroductionThis small-scale study examined how technology-savvy principals perceived modern changes in teaching, learning, and leading in K-12 schools resulting from the introduction of artificial intelligence (AI).MethodsTo explore these changes, we captured the perspectives and lived experiences of nine principals who most recently received the National Association of Secondary School Principals (NASSP) Digital Principals Award.ResultsFindings show that these digital principals perceive that AI can personalize the student learning experience, allows teachers to make their classrooms more responsive to students' needs, and affords leaders flexibility by delegating lower-level tasks to AI.DiscussionThis article fills the research gap by exploring modern AI-driven shifts in K-12 schools and offers recommendations for educators to embrace organizational change amid rapidly evolving AI technology.
William L. Sterrett
1 month ago
The trend of next-generation digital technologies being more widely used in higher education implies that it is time to deal with the factors that affect their adoption and usage more effectively. The current research normalizes the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model by the acceptance of digital literacy and privacy concerns and thereby examining the user's engagement, satisfaction, and performance in learning. Despite extensive use of the UTAUT2 framework in higher education, limited research integrates digital literacy and privacy concerns as core determinants of next-generation technology adoption. This study addresses the lack of a comprehensive model examining how these factors interact with traditional predictors to influence behavioral intention and learning outcomes. The approach of quantitative research design was applied for the collection of data done through an online survey directed at students in higher education during the shift to hybrid and online learning modes. Partial Least Squares Structural Equation Modeling (PLS-SEM) version 4.1.0.3 was used to analyze data of 268 valid responses received. The results of the study show that among the factors predicting the adoption of the technology, performance expectancy had the strongest impact, while behavioral intention, facilitating conditions, and social influence were the other factors that were significantly affected. Besides, social influence, in a positive manner, affects user engagement and satisfaction which highlights the need for peers and encouragement while using technology. The results are a validation of the extended UTAUT2 model's robustness and explanatory power. In conclusion, this research has made a significant contribution around technology acceptance by revealing the fact that the successful adoption of modern educational technologies is not only dependent on the perceived usefulness but also on the level of users’ digital skills, institutional support, and trust-related factors.
Muhammad Younas