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.
