Purpose This article proposes a methodology for issuing academic micro-credentials that remain verifiable after the issuing institution ceases to exist. Existing blockchain credentialing systems still require HTTP calls to the issuer's infrastructure during verification, which fails when the institution closes. We anchor issuer identity in did:blox on the Bloxberg consortium blockchain. Only the credential hash and the issuer's Decentralized Identifiers are stored on-chain, while the Verifiable Credential remains in the holder's wallet. Verification resolves directly from the blockchain, with no dependence on the issuer. The methodology is designed for institutions to adopt through application programming interface (API) integration with existing systems. Design/methodology/approach We define three design goals: issuer-independent verification, general data protection regulation (GDPR) compliance and open standards alignment. From these we derive three architectural decisions: a consortium blockchain (Bloxberg) suited to institutional deployment, a chain-anchored DID method (did:blox) that removes HTTP dependency from verification, and a hybrid on-chain/off-chain storage model that keeps personal data in the holder's wallet. Findings Verification is independent of the issuer. The verifier resolves the issuer's identity through did:blox on Bloxberg, without any HTTP call to the issuer's domain. GDPR compliance is achieved by keeping all personal data off-chain in the holder's wallet. The architecture complies with W3C Verifiable Credentials Data Model v2.0, making credentials portable across any compliant verifier. Research limitations/implications Revocation still depends on the issuer's ability to sign new transactions, which can fail if the institution loses its signing key. The architecture is bound to a single consortium blockchain. Consortium dissolution would require migration of the trusted issuers registry. Handling of DID deactivation is unresolved in current W3C specifications (Mazzocca et al., 2025). These limitations open three research directions: formal protocols for DID deactivation and historical key attestation, multi-signature or escrowed revocation models, and cross-chain interoperability mechanisms (Deng et al., 2025) to mitigate single-consortium risk. Practical implications For educational institutions, the adoption barrier is low. The institution does not operate a blockchain node, host the credential infrastructure, or learn blockchain-specific protocols. Adoption requires only key management and an API integration with the existing Student Information System. For employers and other verifiers, credential verification is reduced from postal correspondence with registrars or third-party transcript services to a quick response (QR) code scan and a cryptographic check in seconds. No intermediary is trusted. The trust comes from the consortium-governed registry on Bloxberg and from the cryptographic properties of the credential itself. Social implications Students gain ownership of their academic credentials. The Verifiable Credential lives in the holder's wallet under their control, and survives institutional changes, mergers or closures that would otherwise invalidate the verifiability of centrally hosted credentials. Cross-border recognition becomes feasible without bilateral institutional agreements, because any W3C VC-compatible verifier can validate the credential. The architecture reduces credential fraud, since each credential is cryptographically tied to an issuer DID on a public ledger. It also lowers the institutional and verifier costs of credentialing. Originality/value Issuer-independent verification (through did:blox chain resolution rather than HTTP), GDPR-compliant hybrid storage, full W3C Verifiable Credentials and DID compliance, and predictable institutional costs through the Bloxberg consortium model. Institutions integrate through an authenticated API to an existing issuance backend, without operating their own blockchain infrastructure.
PurposeThis study aims to explore how collaborative writing technologies, such as Padlet, Google Docs, and ChatGPT, within a Genre-Based Approach (GBA), can enhance university EFL students' multigenre writing development, with particular focus on their ability to apply and adapt genre conventions, structure, and language use across different writing tasks.Design/methodology/approachThis study employed a sequential mixed-methods design to examine the impact of collaborative writing technologies within GBA on the writing development of 27 university EFL students across four genres. Pre- and post-tests, along with genre-specific writing tasks administered throughout the six-session intervention, were used to assess improvement. Collaborative tools were integrated into each session to support writing processes. In addition, semi-structured interviews were conducted to explore students' learning experiences and perceptions of the technologies.FindingsResults indicate a significant overall improvement in pre-test (M = 10.29) and post-test (M = 15.35) scores, with the "narrative" and "descriptive" genres showing the most progress and "argumentative" the least. Qualitative analysis, focusing on students' experiences with the intervention, supports these findings. Notably, the "content" component demonstrated the most improvement, contrasting with modest gains in "lexical resources." The qualitative findings show that students appreciated collaborative writing technologies within a genre-based framework for enhancing their writing skills, collaboration, and autonomy, despite challenges in maintaining logical order and genre transferability.Originality/valueThis study advances existing research by treating the integration of collaborative writing technologies within GBA as a coordinated instructional system rather than isolated tool use. It foregrounds multigenre writing development, emphasizing learners' ability to adapt and transfer genre knowledge across tasks. The findings also refine current understanding by showing both the affordances and limits of such integration, particularly in supporting higher-order reasoning and cross-genre transfer.
Purpose Artificial intelligence (AI)-based learning tools are increasingly integrated into higher education, offering efficiency and instructional support while raising concerns regarding students' cognitive engagement and ethical risks. This study examines the relationships between students' dependence on AI-based learning tools and three key outcomes: decision-making autonomy loss, learning passivity, and perceived privacy and security concerns.Design/methodology/approach Using a quantitative research design, data were collected from 300 university students in Vietnam and analysed using structural equation modelling (SEM). The results indicate that greater dependence on AI-based learning tools is positively associated with all three outcomes. Specifically, students who rely more heavily on AI report reduced confidence in independent decision-making, lower levels of active learning engagement, and heightened concern regarding data privacy and system security. In addition, multi-group analysis reveals disciplinary differences, with the relationship between AI dependence and decision-making autonomy loss being significantly stronger among information technology students than among students in business and foreign language disciplines.Findings The findings highlight that the risks associated with AI in education are not inherent to the technology itself but are shaped by patterns of dependence and contextual use. The study contributes to emerging debates on responsible AI adoption in higher education and offers implications for educators and policymakers seeking to balance AI support with student autonomy, engagement, and ethical governance.Originality/value This is one of the first studies examining how artificial intelligence (AI) affects Vietnamese university students' decision-making autonomy loss, learning passivity, and perceived privacy and security concerns.
Purpose While generative artificial intelligence (AI) is increasingly used to generate feedback on student writing, little is known about how emotional prompts affect the structure of such feedback. This study aims to examine how large language models respond to positive, neutral and negative prompts in postgraduate scientific writing, focusing on how emotional cues shape sentiment polarity, thematic content and structural composition in AI-generated feedback and how these patterns inform learning-relevant feedback architectures.Design/methodology/approach An integrated sentiment-topic-network framework of VADER sentiment analysis, Latent Dirichlet allocation topic modelling and epistemic network analysis (ENA) was used to analyse 198 AI-generated feedback messages on 66 postgraduate scientific reports. Sentiment analysis classified emotional polarity, topic modelling identified thematic clusters within which sentiments were expressed, and ENA modelled co-occurrence patterns among cognitive, affective and dialogic codes to characterize feedback architectures under positive, neutral, and negative prompts.Findings Across all conditions, positive sentiment dominated even when prompts were neutral or negative, with only negative prompts causing substantial increases in negative codes. Topic-sentiment patterns showed that certain feedback themes were selectively intensified by emotional tone. ENA identified three distinct feedback architectures: supportive-integrative networks under positive prompts, fragmented-minimal networks under neutral prompts, and critical-precision networks under negative prompts. Together, these patterns indicate that emotions modulate not only what feedback is generated but also how cognitive and affective elements are integrated.Research limitations/implications Data were drawn from 66 postgraduate students at a single institution, yielding a small, context-specific convenience sample. Findings are therefore interpreted as exploratory patterns rather than statistically generalizable estimates, with analytic generalization aimed at comparable science, technology, engineering and mathematics (STEM)-oriented postgraduate contexts. We did not measure achievement gains or long-term retention. Future research should examine more complex emotional contexts and prompt designs, use mixed-method approaches that include qualitative analysis of emotional nuance and empirically test the three hypotheses about revision quality, perceived usefulness, trust and emotional safety.Practical implications Educators can embed prompt templates corresponding to supportive-integrative, fragmented-minimal and critical-precision architectures into course guidelines and teach students when and how to invoke them. Positive prompts can be used to normalize difficulty and sustain engagement in early drafting, with brief episodes of critical-precision feedback introduced once drafts are coherent. Developers of institutional AI tools can implement visible "feedback mode" selectors that map onto these architectures, allowing teachers to align system behaviour with course-level pedagogy rather than relying on a single default.Originality/value This study advances an integrated Sentiment-Topic-Network framework that links sentiment polarity, thematic focus and network-level co-occurrence patterns in AI-generated feedback. It empirically characterizes a robust positivity bias in large language model feedback under positive, neutral, and negative prompts and identifies three feedback architectures - supportive-integrative, fragmented-minimal and critical-precision - relevant for emotionally tuned feedback design. By translating these architectures into an adaptive model and testable hypotheses, the study offers a reusable lens for analysing emotionally primed AI feedback in postgraduate STEM-oriented learning contexts.
PurposeOver the years, marketing higher education has undergone a profound transformation due to advances in digital technology. Gen AI technology has progressively become more important both for teachers and students in marketing education. This study explores the use of Gen AI by marketing lecturers in higher education institutions (HEIs). We use the technology acceptance model (TAM) as a theoretical foundation. Using TAM, this study explores how marketing lecturers perceive Gen AI in improving teaching effectiveness and efficiency and how easy they find it in integrating it within their teaching practices.Design/methodology/approachThe study employed online structured interviews to understand the adoption of Gen AI followed by a team-based thematic analysis method. A total of 22 structured interviews were conducted with marketing lecturers from different UK universities. Convenience and snowball sampling were used to shortlist the interviewees. Finally, team-based thematic analysis was performed.FindingsThe results show that HEIs lecturers see Gen AI for supporting teaching, developing marketing activities and structuring content. Secondly, marketing lecturers are using Gen AI to create assignment support video guidance and to provide generic feedback. Finally, marketing lecturers use Gen AI for administrative tasks.Research limitations/implicationsThis study extends the literature on Gen AI and marketing higher education. This study also brings the social media data to understand the trends. Moreover, this study extends the TAM model. There are limitations, including specific geographical location, methodology and analysis techniques.Originality/valueThis study extends the literature on Gen AI and marketing higher education. This study also brings the social media data to understand the trends. Moreover, this study extends the TAM model. This study also guides the senior management in universities to invest more in the training of the marketing lecturers to effectively integrate Gen AI in education. Finally, this study explores the ethical use of Gen AI in teaching, assessments and feedback.
PurposeThis study aims to test large language models as a tool for multi-theory simulation that could potentially augment the doctoral student's (DS) research efficiency.Design/methodology/approachThe study proceeded in three phases, commencing with theory simulations where identical prompts were applied to GPT-5, DeepSeek-V3.2 and Mistral Small 3.1. Phase Two involved a quantitative evaluation by two independent raters using the percentage of maximum possible score and the average deviation index. Finally, Phase Three entailed a thematic analysis of the simulated outputs.FindingsLarge language models can augment research efficiency by simulating varied theoretical perspectives and assisting the student in assessing the suitability of theory before commencing a real-world study. Quantitative analysis showed that GPT-5 achieved the highest percentage maximum possible score, followed by DeepSeek-V3.2, and finally Mistral Small 3.1. Additionally, the average deviation index was 0.53, which is less than the critical value of 0.67. Therefore, the inter-rater reliability was established. The qualitative analysis revealed that two of the three models generated the same output, while the third generated a slightly different output.Originality/valueThis research integrates large language model simulation into educational research methodology before conducting empirical studies. This method could potentially augment doctoral research efficiency and present the DS with novel perspectives on their research areas.
Purpose-This study investigates the factors influencing and the impacts of responsible AI integration in Bangladesh's higher education (HE). It evaluates how ethical and social considerations (ESC), perceived benefits (PB) and perceived threats (PT) affect attitude (ATT), and how technological knowledge (TK), content knowledge (CK) and pedagogical knowledge (PK) influence responsible AI integration in higher education (RIAIHE), which subsequently drives the overall impact of responsible AI integration (IRIAIHE). The measured outcomes include academic performance, career guidance, motivation, self-reliance and social integration. Design/methodology/approach-Data were collected from 550 students and faculty at public, private and national universities across Bangladesh using a mixed-mode questionnaire. Participants represented a diverse range of genders, religions, educational backgrounds and disciplines. Partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4 was used for data analysis. Findings-Path analysis showed significant direct effects of ATT (p < 0.001), ESC (p < 0.001), PB (p < 0.001) and TK (p < 0.001) on RIAIHE and of RIAIHE on IRIAIHE (p < 0.001). However, CK, PK and PT did not have significant direct effects. Indirect effects revealed that ESC and PB significantly influenced RIAIHE and IRIAIHE through ATT, while TK impacted IRIAIHE via RIAIHE. The study also confirmed that ATT serves as a key mediator linking upstream variables to RIAIHE and IRIAIHE. Research limitations/implications-These findings emphasize the importance of attitude, developing technological skills, ethical awareness and a positive outlook on AI for promoting its responsible and effective use in higher education. Originality/value-The research offers practical insights for educators, academic leaders and policymakers aiming to harness AI for meaningful educational advancement.
PurposeThis study aims to identify the key architectural components of an AI-driven personalized learning system grounded in mathematical proficiency, to explicate the workflow guiding its design and development, to describe the construction of personalized learning paths that support learner progression and to examine the system's diagnostic efficiency in accurately identifying learning gaps across the mathematical procedures (MAP) and structure of the observed learning outcomes (SLO) dimensions.Design/methodology/approachThe study adopts a design research methodology integrating psychometric modeling through the multidimensional random coefficients multinomial logit model, a decision-tree machine learning algorithm (depth = 3), and adaptive system design. Mathematical proficiency was assessed using the Wright map analysis within a multidimensional Rasch framework to define criterion-based proficiency zones across the MAP and SLO dimensions. Item-level response data from Thai Grade 7 students were used as independent inputs to the decision-tree model to diagnose proficiency levels (Levels 1-5) in each dimension separately, informing the development of expert-validated adaptive instructional designs aligned with cognitive diagnostic assessment principles.FindingsThe study develops a scalable intelligent personalized diagnostic and tutoring system (IPDTS) composed of six core modules, including diagnostic assessment, AI-driven personalized learning paths, adaptive domain instruction, personalized feedback, learning profile monitoring and interactive engagement tools. Results from the system development and evaluation phases indicate that the decision-tree-based personalized learning path model demonstrates moderate to strong diagnostic efficiency across MAP and SLO cognitive dimensions, particularly at clearly differentiated proficiency levels. The system effectively identifies learners' strengths and learning gaps, supports targeted instructional routing and provides interpretable diagnostic feedback. However, reduced diagnostic sensitivity was observed at intermediate proficiency levels, highlighting the need for richer data representation and further refinement during large-scale implementation.Originality/valueThis study proposes a novel AI-driven educational framework that integrates psychometric diagnostics, interpretable AI decision models and adaptive instructional strategies within a unified design research framework. Unlike conventional AI-based learning systems that rely primarily on performance data, the proposed approach emphasizes diagnostically informed personalization aligned with learning progression constructs, supporting scalability and equity in mathematical learning.
PurposeThis paper examines how BDA management capabilities influence effective decision-making processes within innovative organizational culture, serving as a mediating factor in Egyptian higher education in hospitality. In particular, it elaborates on how the four BDA dimensions (i.e. planning, decision-making, coordination, and control) cultivate innovation and enhance faculty members' strategic decision-making within higher education institutions.Design/methodology/approachData were collected via a cross-sectional survey of 368 faculty members at 20 hospitality colleges in Egypt and analyzed using SmartPLS v.4.4 to assess measurement reliability and validity, structural relationships, and mediation.FindingsResults showed that each BDA management capability positively affected innovative organizational culture and decision-making effectiveness. Further, innovative organizational culture partially mediated the effect of BDA capabilities on decision-making effectiveness.Originality/valueThis paper integrates the resource-based and knowledge-based views to provide a dual-theoretical contribution and focuses on the strategic and knowledge-based importance of BDA in higher education. It outlines the need to cultivate innovative culture to leverage data-driven capabilities, providing further insight for administrators and policymakers in hospitality colleges.
Purpose-This study examines the evolving influence of artificial intelligence on teachers over the past 2 decades, reflecting a broader computational turn in education. Design/methodology/approach-Using bibliometric methods on literature indexed in Scopus and Web of Science Core Collection, it identifies key shifts in scholarly attention, theoretical perspectives and pedagogical implications. Through keyword co-occurrence, burst analysis and multidimensional scaling, the study maps the field's evolution across four developmental stages (2002-2024). Findings-The findings reveal a gradual transition from belief-based discourse to attitude-behavior frameworks, reflecting how AI integration has reshaped the professional expectations and cognitive roles of educators. Emerging themes such as burnout, perceived risk and role transformation highlight broader challenges facing teachers in adapting to technological change. Originality/value-This review uncovers structural patterns within the literature and also offers critical insights into the redefinition of teaching in the AI era. It calls for historically grounded, interdisciplinary inquiry, thereby informing both future research and educational quality.
PurposeThe aim of this study is to examine the opinions of university students studying in the emergency and distance education period about asynchronous learning environments within the framework of the Technology Acceptance Model. Design/methodology/approachThe population of this study, which was conducted with the survey model, consists of students studying at the Faculty of Education, Faculty of Engineering, Faculty of Sports Sciences and Faculty of Fine Arts of a state university. A total of 800 people studying in different departments participated in the study. The learning management system (LMS) Acceptance Scale was used to collect data. FindingsAccording to the results of the study, it was determined that the total mean scores of the participants were at a good level; there was a moderate level of acceptance in the “social influence” dimension and a good level of acceptance in other sub-dimensions. It was determined that university students’ acceptance levels of LMSs differed according to gender, the way of connecting to the internet, age, faculty of study and the method of taking an important course related to their career. Students generally have high acceptance rates of LMSs in compulsory situations. However, they prefer face-to-face education, and their acceptance levels are likely to increase as the rate of using LMSs and their ability to direct their own learning increase. Originality/valueDetermining the suitability of LMSs for student needs is important in determining the impact of these environments on student performance, the effort they spend, the ease of use of the environments and the social influence of these environments on students in order to continue lessons in an effective and attractive manner.
PurposeThe purpose of this review study is to examine how students’ competency-based education is affected by technology-based collaborative learning. The study intends to determine how technology integration in collaborative learning environments affects students’ development of competencies necessary for success in academics and beyond through a thorough evaluation of the body of existing literature. Design/methodology/approachIn this study, major electronic bibliographic databases have been the focus of this narrative literature review for selecting related studies. Based on screening, inclusion and exclusion criteria, 105 articles from 1998 to 2024 were included in this study. The literature search produced seven themes describing advancement, advantages, challenges and mitigating strategies for implementing technology-based collaborative learning in Higher Education Institutions globally. FindingsTechnology has assimilated into contemporary education and provides a wealth of chances for group learning. Incorporating technology into collaborative learning methodologies for students improves their competency-based education. Technology also makes it easier for students to communicate and work together outside of the classroom by connecting them with resources, professionals and classmates from a variety of locations and backgrounds. Nevertheless, despite the possible advantages, there are obstacles to overcome in the successful application of technology-based collaborative learning. Practical implicationsThe findings will provide valuable insights that will help educators, policymakers, industry partners and researchers better prepare students for the demands of the digital age and improve the quality of education by identifying successful strategies for integrating technology into collaborative learning environments. Originality/valueThis study is unique because it synthesizes and critically analyzes previous research to identify the gaps in technology-based collaborative learning and how it affects students’ competency-based education in higher education institutions. There is limited work conducted in this domain based on this discourse.
PurposeThis study aims to enhance the interpretability and actionability of learning analytics (LA) dashboards for teachers by integrating generative artificial intelligence (GenAI) to produce tailored narratives. Design/methodology/approachThe research employs a mixed-methods approach, involving the design and implementation of GenAI-driven narrative features within existing dashboards. Prompt engineering techniques were used to generate narratives based on real course data. The approach was evaluated using questionnaires, interviews and structural content analysis of generated narratives. FindingsThe integration of GenAI-generated narratives improved teachers’ understanding and interpretation of dashboard data, supported decision-making and increased willing of adoption of the enhanced dashboards. Teachers highlighted the value of contextualized explanations and reported clarity and ease in interpreting student LA. However, the study also identified challenges related to data quality, transparency of GenAI outputs and the need for human oversight. Originality/valueThis work offers an innovative application of GenAI in educational technology by moving beyond data visualization to provide context-rich, automatically generated narratives. It advances the field by proposing and empirically evaluating specific prompt engineering strategies, including the segmentation of prompts into explanation, interpretation and recommendation segments, as well as mitigation of Large Language Model hallucinations through data injection. It addresses a critical gap in making LA dashboards more accessible and actionable for educators and contributes empirical insights into both the practical benefits and limitations of GenAI integration providing actionable guidelines for narrative generation in real educational settings.
PurposeWorkplace interaction and collaboration can be enhanced by networked learning. The study intends to explore networked learning in the workplace (knowledge sharing and connection buildings) and gain insights into how workers develop connections through learning analytics social network analysis (SNA).Design/methodology/approachSNA was employed to explore how learning connections were established amongst healthcare workers in a large hospital in Singapore. We examined both the total network interactions (density, diameter, average shortest path length) and the levels of interactions between individuals (degree, betweenness, closeness centralities). A total of 99 responses were included in the final data analysis, and Python packages such as NetworkX were used to perform SNA.FindingsThe network as a whole is sparse, as indicated by the low-density score (0.4%). The findings of the study reveal that the bigger sub-networks had more than one worker who interacted with more than one co-worker and these tend to have more edges in them interlinking workers from different departments. We also found that workers from the departments with the larger populations in the sub-networks were more likely to have the highest degree, betweenness and closeness centrality values. This indicates that the larger sub-networks hold more value in terms of understanding how workers with higher centrality values are nurtured.Originality/valueThis paper sheds light on the learning process that occurs when workers engage in networked learning and provides empirical findings with Singapore as the context of the study.
PurposeThis study proposes the Tangible User Interfaces (TUIs) design guideline to support learning to read and spell for children with dyslexia.Design/methodology/approachSeven experts with backgrounds in human-computer interaction, art and design verified the TUIs design guideline and provided feedback on the proposed design guideline of TUIs. It was then validated through an experimental study with 16 participants diagnosed as dyslexia from primary school between 7 and 12 years old. The participants were asked to complete the learning content (letter recognition and spelling) based on their reading level in the Malay language. Following that, the participants were assisted by the researcher to identify their system preferences through questionnaires.FindingsOn average, the results showed significant improvement among the children with dyslexia, particularly those who had difficulties with letter recognition and spelling when using the Tangibell prototype. The task results indicated that the design guideline was deemed suitable for children with dyslexia. Thus, the study meets its objective in validating the guideline through an experimental study with children with dyslexia.Originality/valueA dyslexia-friendly prototype called Tangibell was developed to evaluate the effectiveness of TUIs design guideline during learning activities with children with dyslexia. The design features of the prototype supported with Universal Design principles and Interaction Design dimensions adapted to the TUIs design concept tailored to the needs of children with dyslexia for a good and effective design. Collectively, TUIs design guideline may assist researchers and developers in developing prototypes tailored to the needs of children with dyslexia, contributing to better learning outcomes and improved educational experiences.
Purpose Drawing on secondary data, this viewpoint article suggests uses of ChatGPT for purposeful teaching in higher education to develop university graduate attributes. While literature focuses on ChatGPT's role in learning activities, this article re-establishes the central role of teachers and learners by illustrating how ChatGPT supports the achievement of university graduate attributes. The overarching aim is to model students' critical use of GenAI tools, enabling them to use ChatGPT competently and ethically both during their studies and in their future professions. Design/methodology/approach Adopting a humanistic pedagogical approach informed by Self-Determination Theory, this article integrates the concepts of critical friend and internal feedback to emphasise learners' autonomous construction of knowledge. It maps such theoretical principles to ChatGPT-based activities aimed at the achievement of selected graduate attributes. Findings Five graduate attributes are examined: autonomy, critical thinking, problem solving, professional mindset and responsible citizenship. For each attribute, definitions and learning activities/prompts are provided. The activities demonstrate how Self-Determination Theory core psychological needs - autonomy, competence, and relatedness - can be operationalised within a humanistic learning context through ChatGPT-supported dialogue and reflection. Originality/value This article positions ChatGPT as a theory-informed pedagogical tool that can contribute to holistic student development when used intentionally. It shifts the pedagogical discourse away from ChatGPT's functional role to considering its potential for cultivating graduate attributes that prepare students for professional and civic life.
PurposeThis study develops a comprehensive ethical framework for the responsible use of generative artificial intelligence (AI) in education with particular attention to secondary schooling, informed by analysis across K-12 and higher education contexts. It addresses critical gaps in current ethical guidelines, particularly concerning privacy, bias and transparency issues in educational settings.Design/methodology/approachA systematic literature review was conducted using major databases like Scopus and Web of Science to identify the ethical implications of AI use in education. The research synthesised findings from diverse sources to propose a refined ethical framework tailored for secondary education and informed by practices across the wider education sector.FindingsThe study highlights significant gaps in existing ethical frameworks concerning AI applications in secondary education. These include inadequate measures to address privacy concerns, persistent biases in AI algorithms, weak mechanisms for accountability and a lack of transparency in AI decision-making processes within educational contexts, alongside limited attention to academic integrity.Research limitations/implicationsThe research is constrained by the availability and scope of existing studies on AI ethics in education and by its reliance on secondary data. Further empirical work in school settings is needed to test the framework's effectiveness, adapt it to diverse contexts and refine its components based on real-world application.Practical implicationsThe proposed ethical framework provides educators and policymakers with practical guidelines for integrating AI technologies responsibly in secondary education. It emphasises safeguarding student privacy, ensuring fairness in AI applications, supporting academic integrity and enhancing transparency.Social implicationsImplementing the framework can support social equity in education by reducing the risk that AI technologies perpetuate existing biases and by promoting inclusive educational practices that respect student rights, diversity and the particular vulnerabilities of children and young people.Originality/valueThis study contributes original insights by synthesizing existing research into a coherent framework specifically designed for secondary education. It fills a notable gap in literature by focusing on the ethical challenges and needs at this educational level, which are often overlooked in broader AI ethics discussions.
PurposeThis study reviews documents and analyzes features of a knowledge base represented by AI-inclusive education through bibliometric analysis. The intention is to shed light on research subjects, theoretical frameworks, and publication trends in AI-inclusive education. The results will support the advancement of AI-powered inclusive education systems and aid in closing comprehension gaps. Design/methodology/approachThe findings reveal the primary study topics and their theoretical underpinnings, promoting further development of AI-driven solutions for inclusive education. FindingsAccording to the report, AI-inclusive education is fueled by technological breakthroughs, but it is imperative to integrate these tools with efficient teaching strategies. Originality/valueTo ensure that AI technology effectively promotes inclusive education, future research must address the difficulties of integrating AI into various educational settings and for various user groups.
Purpose-This study aimed to evaluate the impact of a personalized GPT, Alma de Tezontle, on the environmental awareness of university students. The GPT was designed using a validated personalization methodology grounded in life stories and dialogic interaction to foster empathy and recognition. The objective was to assess whether this narrative AI model could generate measurable changes in students' beliefs, behaviors and concern regarding the environmental crisis, particularly water pollution and to identify gender differences in its effect. Design/methodology/approach-A quasi-experimental design was implemented with 92 students from a university in western Mexico. Participants were divided into a control group (n = 32) and an experimental group (n = 60), which interacted with the GPT. The validated instrument adapted from the Andalusian Ecobarometer measured seven dimensions of environmental awareness. Data were analyzed using permutation-based nonparametric tests (maxT statistic) in R to evaluate significant changes by group and gender. Findings-Statistically significant changes were found in three dimensions: reduction of minimization beliefs (p < 0.0001), increase in pro-environmental behaviors (p = 0.0003) and heightened environmental concern (p = 0.0031). Gender differences emerged: men showed more cognitive shifts (belief reduction), while women expressed more behavioral changes and concern. Research limitations/implications-The sample was limited to one institution and a short-term intervention, restricting generalizability and long-term inference. The analysis relied solely on quantitative data, limiting insight into participants' subjective experiences and the mechanisms behind attitudinal change. Originality/value-This is one of the first empirical studies to evaluate a personalized GPT as a transformative educational agent. Its originality lies in combining narrative AI with environmental education, using a validated methodology to generate emotional engagement and measurable behavioral change.
PurposeAlthough Massive Open Online Courses (MOOCs) have experienced unprecedented growth globally, their popularity presents two of education's greatest challenges today: low completion rates and user retention. This study aims to examine the various factors that influence the continuous usage of MOOCs amongst learners by integrating two theoretical models: The Expectation-Confirmation Model in Information Systems (ECM-IS) and Task-Technology Fit (TTF). The study assesses the impact of information quality, system quality, service quality, confirmation, perceived usefulness and learning satisfaction on long-term continued engagement in MOOCs.Design/methodology/approachData were obtained from 377 respondents through a structured survey and analysed using partial least squares structural equation modelling (PLS-SEM) to identify factors determining success of MOOC.FindingsFindings suggest that information and system quality strongly affect confirmation with subsequent effects on perceived usefulness and learning satisfaction, key antecedents of continuous usage intention. It also indicates that the matching of MOOC platform features with users' learning tasks (TTF) positively influences satisfaction in learning by promoting subsequent long-term engagement.Originality/valueThis study provides some theoretical implications on the understanding of MOOC retention and some practical recommendations for MOOC providers in terms of content, system stability as well as support questionnaire adaptability to enhance user experience. Overall, the derived effectuation will offer a deeper understanding of the interconnection of variables that influence persistent usage with implications for practical strategies for sustaining learners in online education platforms.
