https://www.um.edu.mt/library/oar/handle/123456789/308 Thu, 04 Jun 2026 19:04:56 GMT 2026-06-04T19:04:56Z https://www.um.edu.mt/library/oar/handle/123456789/147123 Title: Cracking the black box : the quest to understand the machines that run our lives Abstract: Artificial intelligence (AI) is now part of everyday life. It recommends what we watch online, helps banks approve loans, assists doctors in hospitals and even acts as a digital gatekeeper for who gets hired. Many people enjoy the convenience of these systems, yet, few truly understand how they work. That is where the ‘Explainable AI’ notion comes in. Essentially, it is a growing movement that is aimed at increasing AI transparency, to earn user trust. For years, AI systems were treated like mysterious ‘black boxes’. You feed information into them and they produce an answer. However, at times, it proves hard to clearly explain how they have reached their conclusions. Even the engineers who have built these systems sometimes struggle to fully understand the internal reasoning behind complex AI models. This becomes worrying when AI is used in areas such as healthcare, education, banking, policing or public services. Imagine applying for a loan and being rejected by an AI system without any explanation. Alternatively, consider a hospital using AI to help doctors diagnose patients without anyone being able to explain why the system recommended a particular treatment. In such situations, people may naturally ask: Why did the machine decide this? Explainable AI (XAI) tries to answer that question. Sat, 30 May 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/147123 2026-05-30T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146598 Title: User acceptance of edutainment mobile applications : advancing an experiential design-engagement model (EDEM) Authors: Camilleri, Mark Anthony; Camilleri, Adriana Caterina Abstract: Mobile users are increasingly engaging with edutainment gaming apps in formal and informal contexts. They are drawn to them for their educational and entertainment aspects. In this light, this study validates the Theory of Planned Behavior’s key measures and integrates them with game narratives and game aesthetics constructs to better understand the extent to which psychological and gaming design factors are predicting the individuals’ intentions to play with these learning technologies. The data were gathered through a survey questionnaire from one hundred eighty-six (n = 186) respondents, who were higher education students in a Southern European university. The quantitative findings analyzed through partial least squares (PLS) revealed that the gamers appreciate the edutainment platforms’ audiovisual effects as well as their storylines and narratives. The results reported that mobile users enjoyed playing with entertaining learning apps. Respondents indicated that they were willing to continue their gameplay in the future. In conclusion, this contribution raises awareness on the important synergies between gaming design elements and behavioral dimensions driving the users’ engagement with edutainment apps. It puts forward a robust theoretical framework that is empirically-grounded. Sat, 16 May 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/146598 2026-05-16T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146103 Title: Opening the black box : operational principles, tools and frameworks that advance explainable artificial intelligence (XAI) models Authors: Camilleri, Mark Anthony Abstract: As artificial intelligence (AI) models are increasingly becoming permeated across various domains, there are instances where they are generating hallucinations, misinformation and erroneous outputs. Various stakeholders, particularly the regulatory ones, are encouraging the developers of machine learning (ML) systems to clarify or justify their models' decisions, actions or predictions in a way that is understandable to their users. In this light, this article raises awareness on Explainable Artificial Intelligence (XAI) principles that are intended to increase transparency, accountability and fairness about the modus operandi of machine learning algorithms. A systematic review of the extant literature identifies key tools, frameworks and best practices that enhance the interpretability of AI models, including open-source techniques like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), among others. The synthesis of the findings also shed light on XAI challenges and limitations of black-box models. This contribution advances a conceptual framework for the responsible implementation of XAI and offers practical guidelines that promote the interpretability of AI systems, whilst addressing their opacity, as well as their biased outcomes. It puts forward theoretical and managerial implications as well as future research avenues. Thu, 01 Jan 2026 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/146103 2026-01-01T00:00:00Z https://www.um.edu.mt/library/oar/handle/123456789/146090 Title: Drivers of managements’ behaviour intention and expectation to adopt blockchain technology Authors: Chohen, R.; Konietzny, Jirka; Caruana, Albert Abstract: Blockchain technology offers significant potential for business applications through improved information sharing and decentralized validation. However, many managers remain reluctant to adopt digital trade via blockchain. This research proposes a model to understand the drivers of managers' behavioral intentions and expectations regarding blockchain adoption. By integrating interorganizational factors (competitive pressure and trading partner readiness) and intraorganizational factors (individual technological readiness, interdepartmental conflict/connectedness, and organizational structure), the study differentiates between rational "Type 2" thinking (intention) and more intuitive "Type 1" thinking (expectation). The model suggests that while profit-driven rationales dominate management decisions, technological readiness and organizational context significantly influence the likelihood of adoption. Sat, 01 Jan 2022 00:00:00 GMT https://www.um.edu.mt/library/oar/handle/123456789/146090 2022-01-01T00:00:00Z