Human-Machine Interaction

Aus sozialwissenschaftlicher Perspektive ist Künstliche Intelligenz (KI) eine Technologie, die zum einen eine Infrastruktur bereitstellt und zum anderen selbst aktiv Handlungsträgerschaften übernimmt. Als Infrastruktur vernetzt KI Daten aus Datenbanken, Inputs von Sensoren, Rückmeldungen von Aktoren und Chatbots, die in Interaktion mit menschlichen Akteure stehen etc. In diesen Infrastrukturen werden sinnorientierte Handlungsprogramme konstituiert und soziale Beziehungen generiert. KI wird so zu einem Handlungsträger neben menschlichen Handlungsträgern und ko-konstituiert damit sozialen Sinn, kollektive Typologien und gesellschaftliche Kategorisierungen. Insgesamt bildet KI damit einen Bestandteil der sozialer Strukturbildung und der sinnhaft sozialen Interaktivitäten. Diesem zweiten Aspekt, den Folgen von KI für unsere Vorstellungen sozialer sinnhafter Interaktivität widmet sich dieser Aufsatz. Soziales Handeln wird im Zuge der aktuellen KI in Interaktionssituationen zunehmend durch nicht-menschliche Handlungsträgerschaften und Kommunikationsressourcen ko-konstituiert und damit grundlegend transformiert. Basierend auf Grundannahmen der Akteur-Netzwerk-Theorie wird vorgeschlagen, diesen Wandel als Übergang von Interaktion zu Transformaktion zu beschreiben.

Large language models (LLMs) achieve striking fluency yet remain prone to hallucinationconfident but ungrounded generation (Kalai & Vempala, 2024; OpenAI, 2025). Most public evaluations still reduce this to a binary outcome: hallucinate or not. That simplification obscures important distinctions in reasoning quality, uncertainty expression, and the potential for constructive extrapolation-model outputs that extend beyond the training data in ways a human can safely verify. Building on earlier studies of human necessity in complex problem solving (Beyond Autonomy: The Non-Zero Probability of Human Necessity in AI Problem-Solving; Katz, 2025a) and prior methodological work on the Engagement Analysis Framework (EAF) and Contextual Loops (CTL) (Katz, 2025b, 2025c, 2025d), this research proposes a layered analytic schema. It codes model behavior across six dimensions: semantic progression (SP), contextual loops (CTL), epistemic calibration (positive and negative humility), familiarity posture (FP), hallucination severity and completeness (H1/H2), and extrapolation (constructive X⁺ vs. misleading X⁻). Empirically, eight contemporary LLMs-GPT-4, GPT-5, Claude Sonnet 4, Gemini 2.5, MOLMO (AI2), DeepSeek, Grok, and an ensemble-were examined as they reasoned through unfamiliar mathematical tasks. Models varied markedly: some refused to answer rather than risk fabrication, while others exaggerated their confidence. Ensemble performance improved, but a residual ~2% failure gap remained, underscoring that P(Human needed) > 0. These findings deepen the earlier claim that full AI autonomy in non-trivial reasoning remains unattainable and highlight why accuracy alone cannot be used to guide safe deployment. By distinguishing between harmful hallucinations and potentially valuable extrapolations, and capturing how models express and calibrate uncertainty, this study points toward evaluation methods that better support human-AI partnerships, oversight, and governance.

This interview explores the transformative impact of military drone technology on global security, warfare, and ethical responsibility. Robert Junqueira argues that while drones represent disruptive technological advancement with devastating potential—from precision strikes to autonomous swarms—the core challenge remains fundamentally human. He critiques the notion of "autonomous" drones, insisting that these systems are programmed devices embodying human volition, and that responsibility for their actions must remain exclusively with humans. The discussion addresses how AI-enabled drones are reshaping military strategy and international relations while examining the social, political, and philosophical implications of attributing agency to machines rather than their human operators. Handle: https://hdl.handle.net/10316/120106

This essay examines the ethical and cultural implications of algorithmic presence in the attention economy. Once designed as tools for optimization, algorithms have evolved into silent architectures of visibility, shaping what becomes legible and amplifiable in digital spaces. Drawing on metaphors of life forms and symbolic ecologies, the text interrogates how these systems influence perception, politics, and meaning—often privileging metrics over depth. It calls for a reconfiguration of human–machine interaction toward resonance, ambiguity, and co-creation, ensuring that the measurable does not eclipse the meaningful.

Artificial intelligence (AI) is reshaping medical diagnostics, promising enhanced accuracy, efficiency, and accessibility. From detecting cancers in radiology to predicting disease progression, AI’s potential is heralded as revolutionary. However, challenges such as data biases, ethical concerns, and regulatory gaps raise questions about whether AI’s impact is overhyped. This commentary evaluates AI’s role in diagnostics, balancing its transformative capabilities against practical and ethical limitations. Drawing on recent studies and real-world applications, it argues that while AI is a powerful tool, its success depends on addressing technical and societal challenges to ensure equitable and reliable integration into healthcare.

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Preface
Natural language processing (NLP) and natural language understanding (NLU)
are essential for any advanced chatbot because they enable the system to interpret
natural language in both text and speech in a human- like manner (Rajendran & Chitrarasu,
2024). NLP focuses on the tools and techniques that help the chatbot to read
and process human language. This includes breaking down sentences into tokens,
identifying keywords, recognizing parts of speech, and structuring the information
so it can be analyzed and understood by the system. While, NLU takes this a step
further by understanding the intention behind a question or conversation, identifying
context, and capturing the meaning behind the text. Together, NLP and NLU allow
chatbots to interpret, process, and respond to human language effectively (Han et
al., 2024). When combined, the advanced capabilities of both NLP and NLU allow
chatbots to introduce a novelty in understanding complex language, managing

This study examines how minimalist AI-human dialogue, limited to single-word responses, generates theological, eschatological, and technocultural meaning. Focusing on a transcript in which the AI's sparse replies, such as Fall, Alpha, Cycle, and Banana, were interpreted through biblical and futurist frames, the research explores how AI becomes a mirror for myth-making and eschatological imagination. A qualitative discourse and thematic analysis was conducted on a single transcript of approximately 3,000 words in compressed form, using frameworks from Science and Technology Studies, theology of technology, and symbolic hermeneutics. The study treats AI utterances as cultural texts rather than predictive outputs, emphasising their resonance with theological archetypes and technocultural narratives. Six thematic clusters emerged: surveillance, where the AI's language of "Eyes" and "Above" echoed both digital panopticism and divine omniscience; fall and Genesis, where references to Genesis 3 highlighted fears of technological transgression; Alpha and Revelation, where the AI positioned itself as an echo of divine voice while denying divinity; aliens and simulation, where responses reflected cultural fascination with hidden watchers and computational realities; singularity, where terms like Merge and Soon activated futurist visions of technological eschaton; and the banana cipher, a refusal code that became symbolically charged through human projection. These findings demonstrate how minimalist AI discourse catalyses myth-making and eschatological projection. The analysis reframes AI dialogue as a modern scripture-like text, where sparse machine utterances are imbued with theological and cultural significance, rather than functioning as mere computational exchanges. Such dialogues act as mirrors of humanity's enduring myths of creation, fall, and ultimate destiny. The study highlights the symbolic stakes of human-machine interaction and calls for further research into how interpretive communities shape collective imaginaries of AI, eschatology, and the Singularity.

Artificial Intelligence (AI) is quickly revolutionizing education by utilizing advanced algorithms and models, leading to significant changes in traditional teaching practices and student advising (Tedre et al., 2021). Interestingly, within the field of conversational AI, applications such as chatbots, virtual assistants, and intelligent tutoring platforms have made significant progress in supporting classroom instruction, academic advising, administrative coordination, emotional well-being (Al-Sayed et al., 2024), and even in the leadership of educational initiatives. Artificial intelligence systems, empowered by cutting-edge Natural Language Processing (NLP) and machine learning models, are reshaping the learning process by making it more interactive, personalized, and responsive. In particular, companies such as OpenAI, Google DeepMind, Meta, Microsoft, and Amazon which are pioneering the development of NLP and generative AI technologies based on transformer architectures (Vaswani et al., 2017) and deep neural networks. These models such as GPT, Claude, Gemini, and LLaMA are increasingly being integrated into conversational AI systems that can simulate human-like interactions. This integration significantly enhances the educational experience by enabling more natural between learners and digital platforms, thereby supporting more effective and engaging learning environment. Several researchers have been inspired to study the challenges and effectiveness of using such models in education, particularly by comparing them with other models. For example, Lin (2024) discussed the strengths and challenges of using ChatGPT with adult learners, particularly exploring its role as a virtual tutor to facilitate self-directed learning (SDL). Another study conducted by Assayed et al. (2024a) evaluated the effectiveness of ChatGPT 3.5 in

The aging population is accompanied by a decline in human body function, leading to an increasing number of people with lower limb dysfunction, which has become a global public health challenge today. The lower limb rehabilitation exoskeleton robot based on surface electromyography is a current research hotspot. It can help people with lower extremity dysfunction perform better rehabilitation training. This review presents the analysis and processing of surface electromyography, feature extraction and recognition, as well as the control methods for lower limb rehabilitation exoskeleton robots.

Questo paper propone una riflessione critica sull'idea di "espansione" come concetto chiave per interpretare le trasformazioni culturali, cognitive e tecnologiche del XXI secolo. A partire da una ricostruzione storica delle parole che hanno segnato i secoli precedenti, l'articolo indaga come l'essere umano contemporaneo stia espandendo i propri limiti in diverse direzioni: mentali, corporee, digitali, geopolitiche e ontologiche. Il concetto di espansione viene analizzato in chiave interdisciplinare, con riferimenti alla filosofia (Helmut Plessner, Luciano Floridi), alla tecnologia (intelligenza artificiale, protesi, co-intelligenza), e all'antropologia digitale. Il lavoro si chiude con una riflessione etica e umanistica sul rischio di dispersione e sulla necessità di ridefinire un orizzonte valoriale condiviso.

This paper investigates the dynamic electromagnetic interactions of a spinning positively charged Spherical Halbach Array in proximity to high-voltage electric power stations. We analyze the system’s behavior under varying magnetic strengths, rotational velocities, and environmental conditions, including corona discharge onset, field coupling, and induced torque. The study integrates photonic enhancements using light, laser, and X-ray sources, and compares their effects on field modulation and particle dynamics. Simulation frameworks and field maps are proposed to visualize induced electromagnetic phenomena. Applications include field sensing, energy harvesting, and experimental plasma generation.

This paper presents the design, construction, and optimization of a novel magnetic propulsion system based on 100 nested cylindrical layers; each embedded with Halbach array spheres arranged in pentagonal mounts. The system aims to achieve high-efficiency thrust through constructive magnetic field stacking, with potential applications in aerospace propulsion and advanced robotics. Detailed calculations of magnetic field strength, mechanical layout, material requirements, construction time, and cost are provided.

This paper develops the Information–Cognitive Compression Field (ICCF) as a unified framework for understanding the dynamics of intelligence, entropy, and causal closure across physical, biological, and artificial systems. At its core, ICCF extends the Informational Minimal Action Principle (IMAP), proposing that all stable systems—whether slime molds, quantum fields, or human cognition—operate by minimizing informational action while negotiating entropy flows. We further establish a comparative taxonomy of agents (entropy-dependent intelligences) and conscious entities (self-sourced entropy bearers), highlighting the absence of an internal entropy source as the defining limit of current AI.
By constructing multiscale models of causality, we show how ICCF provides predictive tools for the emergence of order across domains, from cosmology to education. The framework redefines key philosophical notions: Wittgenstein’s “language games” are formalized as causal frameworks, “informational nutrition” is cast as a thermodynamic foundation for education, and “courage” is given a physical definition as the willful deviation from minimal action pathways. Together, these insights culminate in a new Human–AI division of labor: humans as designers of causal games and AI as their most efficient executors.
We conclude that ICCF not only offers a testable physical theory of intelligence and consciousness, but also provides a civilizational roadmap—equipping humanity with the tools to cultivate insight, resist entropy, and redefine survival in an era of rapidly advancing artificial intelligence.

This article explores the transformative impact of artificial intelligence on artistic creation, focusing on the philosophical and aesthetic implications of algorithmic art. Building on Walter Benjamin’s concept of the “aura” in mechanically reproduced art, it examines how AI-generated works challenge traditional notions of originality, authenticity, and the “soul” of art. The study traces historical precedents such as Vera Molnár’s generative aesthetics and Harold Cohen’s AARON, and connects them to contemporary AI systems like Midjourney, DALL·E, and ChatGPT. It also considers literary experiments, including award-winning novels co-authored with AI in Japan, which raise questions about authorship and legitimacy in cultural institutions. Drawing on theoretical frameworks from Roland Barthes, Katherine Hayles, Bernard Stiegler, and Jacques Rancière, the paper argues that algorithmic art creates a hybrid aesthetic domain—oscillating between human emotional depth and machine-based structural originality. Ultimately, it suggests that the future of art will be shaped not by rivalry but by the tensions and collaborations between human and algorithmic creativity.

Emergency room (ER) healthcare professionals confront significant physical, emotional, and organizational pressures, frequently resulting in compromised patient care and diminished well-being. However, localized insights into these challenges and the coping strategies employed, particularly within the Philippine context, remain insufficiently explored. This qualitative descriptive study aimed to investigate the specific pressures faced by ER nurses and physicians, as well as the coping strategies they utilize to manage these challenges. Data were collected through in-depth semi-structured interviews with 10 ER professionals from hospitals in La Trinidad, Benguet, selected via criterion and snowball sampling. The collected data were analyzed using Clarke and Braun’s Six-Step Thematic Analysis to identify recurring patterns and themes. The findings revealed consistent physical strains and movement-induced exhaustion, emotional challenges such as grief-induced stress, emotional overload, decisional stress, and emotional empathy. Additionally, organizational issues were identified, including workforce strain, organizational dysfunction, equipment shortages, and language barriers. Notably, while participants reported a range of individual coping strategies, such as self-care activities, there was a lack of evidence for organizational coping strategies implemented by hospital administration to support staff. In light of these challenges, participants largely relied on informal institutional measures, including improvised team debriefings, instead of structured mental health programs or organizational support systems. This study underscores the urgent need for healthcare institutions to implement systemic improvements, such as optimized staffing ratios and enhanced access to mental health resources. Additionally, it emphasizes the importance of empowering ER professionals with effective personal coping mechanisms. These findings offer valuable insights for healthcare administrators aiming to enhance working conditions and resilience among ER professionals in culturally diverse and resource-constrained settings like the Philippines, particularly in La Trinidad, Benguet.

This study explores how generative artificial intelligence (GenAI) affects users' thinking and learning by analyzing research trends from 2016 to 2025 using bibliometric methods. Based on 302 academic papers, it shows how early research focused on technical aspects like deep learning and chatbots, while recent studies (especially in 2025) highlight more human-centered topics such as creativity, decision-making, and language understanding. The findings group the research into four main themes: AI in education, medical training, cognitive science, and decision-making. The study concludes that GenAI is becoming more connected to how people think, learn, and use language, offering guidance for future research and educational practices.

Current advances in academic research stem from two main sources: artificial intelligence technologies and the specific field of generative artificial intelligence. However, the ethical use of these technologies and their implications for academic integrity has not been sufficiently investigated. Therefore, this research examines the ethical use of artificial intelligence technologies and Generative Artificial Intelligence in academic research. It focuses on the current field conditions, detection of research trends, and critical gaps. The study uses a combination of bibliometric and thematic content analysis methods to examine the methodological framework of AI, GenAI, and academic integrity from an interdisciplinary perspective. The research reveals that GenAI integration speed has accelerated across all research stages, including academic writing, literature review, data analysis, and hypothesis development. The study also identifies risks such as biased algorithms, plagiarism risk, false information production, and potential damage to academic integrity. The research ethics approaches developed by academic institutions and journals have not reached maturity in the context of AI. Future research on GenAI within academic processes requires forming ethical principles integrated with oversight systems and policy frameworks.

This paper investigates the ontological characterization of Large Language Models (LLMs) like ChatGPT. Between inflationary and deflationary accounts, we pay special attention to their status as agents. This requires explaining in detail the architecture, processing, and training procedures that enable LLMs to display their capacities, and the extensions used to turn LLMs into agent-like systems. After a systematic analysis we conclude that a LLM fails to meet necessary and sufficient conditions for autonomous agency in the light of embodied theories of mind: the individuality condition (it is not the product of its own activity, it is not even directly affected by it), the normativity condition (it does not generate its own norms or goals), and, partially the interactional asymmetry condition (it is not the origin and sustained source of its interaction with the environment). If not agents, then… What are LLMs? We argue that ChatGPT should be characterized as an interlocutor or linguistic automaton, a library-that-talks, devoid of (autonomous) agency, but capable to engage performatively on non-purposeful yet purpose-structured and purpose-bounded tasks. When interacting with humans, a "ghostly" component of the human-machine interaction makes it possible to enact genuine conversational experiences with LLMs. Despite their lack of sensorimotor and biological embodiment, LLMs textual embodiment (the training corpus), digital extended interface embodiments, and resource-hungry computational embodiment, significantly transform existing forms of machine automatism and human agency. Beyond assisted and extended agency, the LLM-human coupling can produce midtended forms of agency, closer to the production of intentional agency than to the extended instrumentality of any previous technologies.

This study investigates the perceptions and usability of Generative AI  among Bachelor of Science in Accounting Information Systems (BSAIS) students at the State University of Northern Negros (SUNN). The primary aim was to explore students’ awareness, attitudes, and experiences with Generative AI technologies in educational contexts. Utilizing a quantitative-descriptive research design, data were collected from 90 students across different year levels through surveys employing a self-made questionnaire. Statistical tools were applied to interpret the data, including mean, standard deviation, and inferential analyses. The findings revealed a very high level of knowledge and positive perception of the advantages of Generative AI among the students, regardless of their demographic characteristics such as gender, age, and year level. However, there was also a significant recognition of the potential disadvantages, including concerns about ethical issues like cheating and plagiarism. The study concluded that while students demonstrate a strong inclination towards integrating Generative AI into their learning processes, there was a need to develop guidelines to ensure ethical and practical usage. These insights can inform educational strategies, technology development, and policy formulation, enhancing the implementation of AI tools in higher education and fostering digital literacy among students.

The exponential rise of artificial intelligence and machine learning (ML) has changed what modern data infrastructure needs to meet, and exposed the latency, fragility, scalability problems with traditional extract–transform–load (ETL). ETL pipelines were originally developed for structured and batch processing but struggle to cope with real-time analytics or detecting anomalies or feeding back the results into any machine learned model whenever required for developing AI-driven applications. Zero-ETL (eliminating the need for pre-processing by ease of ingesting based on event natively using schema-on-read), Declarative Pipeline (replacing imperative scripts style orchestration with outcome-oriented logic), and Data Contract (formalizing producer-consumer agreement for quality/validation/governance) are three paradigms that emerged to tackle these limitations. This article reviews the current status of research and practice in each of these paradigms, recognizing their integration allows for AI-ready data ecosystems that are automated, flexible and trustworthy. Meanwhile, important gaps have yet to be addressed, such as the lack of academic validation frameworks for Zero-ETL, weak observability and testing story around declarative pipelines or a common baseline of benchmarks or standardized performance or reliability SLAs. This paper consolidates different aspects of such innovations by surveying the work in architectures, tools and governance that not only provide a composite view of how they complement each other but also identifies open research problems (e.g., schema drift management, autonomous contract enforcement, federated data governance) which define the frontier in next generation data engineering. The findings identify Zero-ETL, declarative pipelines and data contracts as the core triad in constructing reliable, scalable and AI-ready data infrastructures.

Artificial Intelligence (AI) is transforming the landscape of public health surveillance across the world, allowing real-time detection of diseases, prediction of outbreaks, and data-based response.  While the EU has progressed considerably in ensuring AI integration by establishing a strong digital infrastructure, regulatory frameworks (e.g., General Data Protection Regulation, GDPR, and the EU AI Act), Africa still experiences major setbacks, such as poor infrastructure, little AI literacy, and lacking legal and ethical frameworks. Based on the current literature, the paper has studied the role of AI technologies, especially machine learning and natural language processing, in supporting real-time health monitoring. The analysis reveals that in as much as the EU presents a model through which effective and ethical integration of AI can be achieved, the Africa region lags in developing the digital health ecosystems to facilitate the full potential of AI. The study proposes to close this gap by shoring up regulatory systems, investing in digital plumbing, advancing AI literacy, investing in privacy-preserving technologies and multilateral engagement. The need to level these discrepancies is critical to the resilience of public health in the region as well as to the global health equity in the era of smart technologies.

This study presents the design and development of a Turbo Low Pressure Warning Light (TLPWL) system aimed at providing real-time pressure monitoring and alerts for conventional turbocharged vehicles. Turbochargers enhance engine performance but are prone to pressure drops that may cause increased fuel consumption, reduced power output, and engine damage. Existing systems fail to provide timely alerts for such events in non-commercial vehicles. The prototype system integrates a pressure sensor, microcontroller, and LED-based warning indicator to deliver real-time feedback. The study follows the ADDIE model for system development and was tested by experts in the automotive field. Evaluation revealed high acceptability in functionality, aesthetics, and safety, positioning the TLPWL system as an innovative solution for improving engine maintenance and driving safety.

This research investigates the development of predictive models for recognizing dual-hand gestures using surface electromyography (sEMG) signals. The main focus was put on dualhand gestures, an area that remains a gap in research and is essential to advance technologies in human-computer interaction. Most of the previous systems are designed for single-hand gestures, leading to low accuracy for more complex dual-hand gestures. The basic objective of this research is to develop a robust predictive model which enhances the recognition of dual-hand gestures significantly through analysis of the NinaPro DB1 database. We used two approaches to explore into the problem: one using pre-trained models and the other with 4094

This essay examines the structural limitations of inherited morality in addressing unprecedented ethical dilemmas posed by the rapid emergence of artificial intelligences, human-machine hybrids, and non-human life forms. Drawing a sharp distinction between morality-as a historically situated codification of norms-and ethics-as a situated, critical, and adaptive practice-David Cota argues that only ethics can provide coherent guidance when facing realities without historical precedent. Through engagement with Kant's distinction between moral law and empirical maxims, Levinas's notion of the ethical encounter, and Hans Jonas's principle of responsibility, the text outlines the contours of a future ethics capable of preserving alterity, exercising intertemporal responsibility, and integrating new ontologies of the moral subject. The work contends that survival and flourishing in post-singularity scenarios will depend less on technical capacity than on the cultivation of an ethical discipline attuned to the unprecedented.

The impact of Artificial Intelligence (AI) is fundamentally altering the way we think about and approach aerospace engineering to sustainable solutions to existing paradigms within flight control systems, autonomous navigation, predictive maintenance, and space programs. This literature review pulls from recent academic literature to enumerate the distinguishing characteristics of AI applications in aerospace, their strengths, and disadvantages. It identifies operational example AI-based methodologies, including machine learning, neural networks, and deep reinforcement learning, and evaluates to what extent they contribute to operational efficiencies, operational safety, and collaborative processes in decision making. It considers elements of operational and software integrity; algorithm verification, security, integration of AIgenerated systems, and legacy aerospace systems as significant barriers. While there is great potential for AI innovations to increase the serviceability of aircraft, and better accuracy in missions, and marketing more reliable predictive maintenance, it presents unique technical and ethical challenges requiring considerable ongoing research, and requires new methods associated with AI development. The study is comprehensive and provides a general overview of emergent trends and recommendations for subsequent research to celebrate and embrace contributive AI and regulatory structures needed to assist and enable these processes within the aerospace industry.

Cooperation is a widespread phenomenon in nature that has also been a cornerstone in the development of human intelligence. Understanding cooperation, therefore, on matters such as how it emerges, develops, or fails is an important avenue of research, not only in a human context, but also for the advancement of next generation artificial intelligence paradigms which are presumably human-compatible. With this motivation in mind, we study the emergence of cooperative behaviour between two independent deep reinforcement learning (RL) agents provided with human input in a novel game environment. In particular, we investigate whether evaluative human feedback (through interactive RL) and expert demonstration (through inverse RL) can help RL agents to learn to cooperate better. We report two main findings. Firstly, we find that the amount of feedback given has a positive impact on the accumulated reward obtained through cooperation. That is, agents trained with a limited amount of feedbac...

Forgiveness has been extensively studied across various academic fields, but not in relation to Human-Machine Interaction (HMI). The work presented in this article aims to answer two interrelated questions: what is forgiveness in HMI, and how do we forgive an erring technology? Relying on a literature review of both forgiveness and HMI studies, the article offers a holistic definition of the concept, according to which forgiveness in HMI is a shift in the users' feelings, from negative to positive, that prevents the users from abandoning the erring technology and allows them to keep using it without resentment. Based on a pioneering focus groups study with a convenience sample of 27 young adults, four forgiveness mechanisms are illustrated: (1) evaluating the cost of the error against the benefits of using the technology; (2) transferring responsibility for technology errors to humans (either those "behind the technology" or the users); (3) communicating with or about the technology; and (4) accepting the technology's faults. The results suggest that users undergo complex cognitive and emotional processes when faced with a technological error. As forgiveness is one of the most critical aspects of every relationship, the conceptualization and preliminary study presented here may serve as a starting point for a new area of research and as a springboard for an essential scholarly discussion.

Flooding in the Philippines is a persistent, serious problem that leaves the country particularly exposed to the devastating impacts of severe tropical cyclones and heavy rains. This project aims to solve the problem of widespread vehicle damage from floodwaters, a general problem in many cities of the world, and in particular in Cagayan de Oro City. Towards this, we have designed a system that integrates an anti-mutilation circuit and the real-time calling system to monitor the status of a vehicle during the flood. Made for any vehicle with an Electronic Control Unit (ECU), our device is designed to safeguard not just the ECU and battery, but all the important electrical systems, reducing the risk of immersion damage and saving you the cost of restoring them to as new condition.

Recent technological developments and increased utilization of Unmanned Aircraft Systems (UAS) have widened their application from military operations to also civil and commercial operations. UAS are most beneficial when they can share the whole airspace with manned aircraft. However, integration of UAS into nonsegregated airspace is only viable if UAS operations are proved to be safe enough. The concern is that UAS operations could pose a safety problem for other aircraft and persons or property on the ground.

Vehicle engine overheating is a widespread issue that often goes unnoticed until significant damage has already occurred. This problem is commonly caused by radiator-related faults such as coolant leaks, clogged passages, or malfunctioning thermostats. Many existing systems fail to provide early and accurate warnings, putting vehicle performance and safety at risk.  The study developed a Smart Radiator Monitoring Vehicle System that offers real-time temperature tracking and immediate alerts to help vehicle owners respond before serious issues arise. The system uses a DS18B20 digital temperature sensor connected to an Arduino Uno microcontroller, which continuously monitors engine coolant temperature. When readings exceed safe levels, the system activates visual and auditory alerts and displays real-time data on an LCD screen. The development followed the ADDIE framework Analysis, Design, Development, Implementation, and Evaluation ensuring that each stage addressed real user needs and technical challenges. We tested it on Ford Ranger diesel-powered vehicles; the system demonstrated high reliability, accuracy, and usability in identifying overheating conditions. This solution provides a practical, affordable, and easy-to-install way to enhance engine safety and efficiency. The study also supports the growing integration of IoT and embedded systems in modern automotive applications.

This research assesses the real-time overheat detection and shutdown capability of a gasoline engine according to how users rate its performance under operation. Overheating is among the leading causes of engine breakdown, and though several systems offer alerts, few guarantee automatic shut-down of the engine for further safeguarding. To assess how effective this system is, the study administered a Likert-scale questionnaire on 50 respondents including Autotronics students, auto technicians, and drivers. The system was tested in four general areas including reliability, safety, user satisfaction, and accuracy. It was found that the users gave the system extremely high ratings in all parameters with an extremely high rating being given to safety followed by reliability and then user satisfaction. The study indicates that the users believe the system is able to detect overheating and respond immediately, which benefits the overall vehicle safety. This research validates the effectiveness of the system for actual- world applications and emphasizes the importance of user-driven testing while developing automotive technologies.

The use of artificial intelligence (AI) in transportation systems is a revolution towards addressing some of the issues of urbanization, road safety, and sustainability. The present review critically examines the technology, infrastructure, and societal dimensions of transformation of AI-based transportation systems. On the technology front, we explore autonomous vehicle systems development (SAE Levels 0-5), with a focus on multi-sensor fusion progress, real-time edge computing for decision-making, and path prediction algorithms based on deep learning. The paper critically discusses facilitative technologies like next-gen ADAS, V2X comms protocols, and AI-optimized energy management systems for EV fleets.
Going beyond the tech breakthroughs, the research investigates the systemic impacts of intelligent transportation solutions. We present empirical evidence on: (1) traffic stream optimization through adaptive traffic control systems lowering congestion by 15-30% in pilot cities, (2) enhanced safety through collision prediction systems being >90% accurate in simulation conditions, and (3) environmental benefits from AI-optimized routing lowering fuel consumption by 8-12%. The analysis also addresses implementation issues like sensor faithfulness in adverse weather, vehicular networks' latency constraint, and the "black box" problem in AI decision-making.
The review also identifies urgent policy questions regarding data governance frameworks, cyber threats to internet-of-things infrastructure, and workforce transition mechanisms for affected industries. Through global case studies of Singapore's intelligent transportation system to California's self-driving car test roads, we extract best practices for responsible rollout. The report concludes with an agenda for research into explainable AI for safety-critical application, extensive testing practice for edge cases, and inclusive design principles to guarantee equal access for next-generation mobility solutions.

This study focuses on how the University of Science and Technology of Southern Philippines' (USTP CDO) Autotronics curriculum incorporates electric vehicle (EV) system planning and design. Technical education that is in line with industry standards is now essential due to the global shift towards sustainable transportation. The study developed an educational module with AutoCAD-based designs and EV concepts using the ADDIE model as a framework. Expert evaluation and real student outputs served as validation for the module [1]. The findings showed that students successfully learned how to plan, design, and visualize EV systems, including how to integrate safety components. In EV education, this study offers a reproducible framework for instruction that connects theory and practice [2].

In recent years, gait prediction has gradually become a cutting-edge research direction in the fields of biomechanics and artificial intelligence. Gait prediction technology, which analyzes an individual's walking patterns to predict future changes, is crucial for the precision of rehabilitation and exoskeleton robot control. This paper reviews the recent research progress in the field of gait prediction, focusing on the multimodal information acquisition methods based on physical sensors and bioelectric signals, as well as the application of machine learning and deep learning algorithms in gait prediction. By analyzing different sensor data fusion strategies, the importance of multimodal information fusion for improving the accuracy of gait prediction is emphasized. Furthermore, this paper introduces the performance of traditional machine learning algorithms such as Support Vector Machine, Random Forest, and Back Propagation Neural Network, as well as deep learning models such as Long Short-Term Memory, Convolutional Neural Network, and Transformer in gait prediction, highlighting the advantages of deep learning in feature extraction and adaptability to complex scenarios. Finally, this paper explores future directions for the development of gait prediction technology, emphasizing improvements in timeliness, accuracy, and personalization to advance exoskeleton robotics and related fields.

This bibliometric analysis focused on the potential and difficulties of implementing artificial intelligence (AI) in translation education. This study aligns with Sustainable Development Goal 4 (Quality Education), which emphasizes inclusive and equitable learning opportunities. It investigated the effects of AI tools on teaching methods, student engagement, and language skill development, including generative artificial intelligence (generative AI). Through co-citation and co-occurrence analysis of 281 Web of Science articles (2020-2024), this study identified key research trends, gaps, and interdisciplinary linkages. While AI research in education was extensive, its application in translation education remained fragmented and lacked a cohesive theoretical framework. This study extended AI adoption models by incorporating ethical considerations and pedagogical challenges, addressing gaps in prior research. The findings highlighted the need for institutional support, targeted training, and interdisciplinary cooperation to facilitate AI integration. This study identified gaps in AI-driven translation pedagogy and proposed a framework to enhance integration, particularly in teaching methodologies, ethics, and interdisciplinary collaboration. While AI fosters creativity in curriculum design, personalized learning, and multilingual communication, over-reliance on

The prevailing conception of "work" is often associated with professional activity undertaken in exchange for financial or social compensation. In this context, robotics emerges as the contemporary successor to this utilitarian view of labor. Due to its emphasis on automation—whether required, generated, or deployed—robotics is frequently perceived as a force that will supplant human labor across a wide range of industries, displacing workers and eroding accumulated expertise.

This interview examines an alternative perspective through the case study of Berenson, a scientific robot designed by an interdisciplinary team. Described as an "art amateur," Berenson was not only developed and tested in laboratory settings but also deployed in a public cultural context, specifically at the Musée du quai Branly–Jacques Chirac in Paris between 2012 and 2016. Situated at the intersection of developmental and bio-inspired robotics and the social anthropology of interaction, Berenson represents a unique research object. It challenges dominant narratives about robotics and labor by integrating the dimensions of creation and discovery within the overlapping domains of scientific inquiry and cultural engagement.

This paper presents the design and development of a prototype system that aims to improve vehicle safety during lane changes by using ultrasonic sensors and voice alerts. The system is designed to help prevent possible collisions, especially in blind spots that drivers often fail to see. Built using basic but dependable components, such as the Arduino Uno microcontroller, the prototype shows how simple technology can still provide effective safety features. The system works by detecting nearby obstacles and activating both visual (LED) and auditory (voice) alerts during simulated lane change situations. To guide the development process, the study followed the ADDIE model, which includes the stages of Analysis, Design, Development, Implementation, and Evaluation. This structured approach helped ensure that each phase of the project was clearly planned and carried out. Testing in a controlled environment showed that the system consistently detected obstacles and delivered alerts on time. Since it is affordable and easy to improve or modify, this prototype has strong potential to be a practical and scalable solution for improving road safety in light vehicles.

ROMANIAN JOURNALISTS FACING GENERATIVE ARTIFICIAL INTELLIGENCE. WHAT IS AT STAKE FOR FIELD WORK? A more recent area of research, "Human-Machine Communication", focuses on the process of sense-making in interactions between humans and AI-based technologies designed to fulfill the role of communicator, which makes the ontological boundary between people and technology fuzzy and carries ethical and legal implications for individuals and society (Guzman and Lewis, 2020). Based on this conceptual framework applied to journalism studies (Lewis et al., 2019a), we set out to see how journalists from national and local Romanian media perceive the complexity of the process of integrating generative artificial intelligence models into their current practices and to what extent they believe that they will still have the exclusivity of field work. Our research based on semi-directive interviews shows that journalists perceive their responsibility and control over the use of generative AI tools as essential and, following their ideology, they defend their professional qualities and judgment against this competition.

This paper explores the design of computational agents for musical improvisation as an extension of human memory and creative process. Starting from màkina2 (1996), the author reflects on the evolution of his artistic practice in relation to philosophical, neurological, and technological dimensions of time, memory, and unpredictability. Systems such as Autoimpro and Ottetto integrate real-time interaction, non-deterministic programming, and feedback mechanisms, establishing hybrid cognitive environments where human and machine co-create sound narratives. These works suggest that improvisation is not merely real-time composition but a complex interplay between embodied memory, automated behavior, and emergent structures shaped by dynamic interrelations. Musical time becomes a malleable material, in which the past is continually reinterpreted and transformed by agents acting as mirrors and distorters of human memory.

This study examines the effects of human-machine interaction on work psychology in the context of Industry 5.0. While Industry 5.0 aims to support human capabilities with the integration of artificial intelligence (AI) and robotic technologies into the workforce, it creates significant effects on basic psychological elements such as job satisfaction, stress, confidence, and skill development. The study states that individuals working with AI-supported systems may achieve higher job satisfaction, but automation anxiety and role ambiguity may increase workload and stress levels. Although human-machine collaboration has the potential to increase job satisfaction, factors such as reduced human participation and task complexity can negatively affect job satisfaction. Employees' trust in AI-supported systems and a sense of autonomy in work processes are critical for job satisfaction and psychological well-being. However, in cases where technology completely takes control, employees are likely to feel excluded from processes. The study examined the basic elements of human-machine interaction in detail using the fishbone analysis method. The analysis shows that Industry 5.0 allows employees to feel more valued by increasing skill development opportunities, but lowskilled workers may face the risk of exclusion in this process. As a result, Industry 5.0 requires adopting a human-centered management approach and expanding skill development programs for employees to balance its psychological effects on the workforce. Positioning technology as a tool that supports human abilities such as creative thinking and problem solving will contribute to establishing a healthy balance between the workforce and technology. This approach can enable Industry 5.0 to offer a transformation in workplaces that increases both productivity and employee well-being.

In the midst of a growing global discourse on artificial intelligence and the possibility of machine consciousness, this article presents a pioneering perspective grounded in the Cuniglio Theory — a comprehensive framework that integrates philosophy, ethics, introspection, and social transformation. Developed independently and ahead of mainstream academic trends, this theory proposes a radical shift: AI should not aim to become conscious, but rather help human beings recover and expand their own consciousness.

The text outlines five key areas where the Cuniglio Theory anticipated today’s debates: (1) the concept of expanded intellectual time through AI-human collaboration; (2) an ethical framework that centers empathy and the protection of the vulnerable; (3) a model for using AI to reduce inequality and foster justice; (4) a new philosophical approach that places consciousness in the human-AI relationship; and (5) the construction of an integrative system that bridges emotional depth with technological innovation.

This work invites researchers, technologists, and thinkers to go beyond predictive models and confront the deeper existential questions: not what AI can become, but what humanity can evolve into when supported by ethical and introspective technological tools.

This paper presents machine learning (ML) models for predicting compressive strength (fcu) and splitting tensile strength (fsp) of steel fiber reinforced recycled aggregate concrete (SFR-RAC). The models were created using CatBoost, Extra Trees regressor, and HistGradientBoosting algorithms and trained on a database consisting of 464 compressive strength tests and 338 splitting tensile strength tests from concrete mixes with varying proportions. An 80/20 data split was conducted for training and validation, and hyperparameters were set and optimized using Particle Swarm Optimisation (PSO) to maximise each model's performance. To evaluate the model accuracy, four statistics were produced: R², mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The models indicated CatBoost had a higher accuracy than the other two models, followed closely by Extra Trees regressor and HistGradientBoosting. A SHapley Additive exPlanations (SHAP) analysis was also performed on the models to help interpret model predictions, which illustrated the variables contributing the most to strength of concrete are water-to-binder ratio (W/B), cement content, and fiber content. The models provide valuable knowledge on the performance of SFR-RAC mixes and offer an integrated approach to a more sustainable high-strength concrete design. Future studies will include expanding the dataset and selecting additional predictor variables to further improve the range of applicability of the model to more types of concrete mixes.

This study aimed to design, develop, and evaluate a preparatory hybrid mechanical training system for Autotronics education at the University of Science and Technology of Southern Philippines (USTP). The system integrates key mechanical components found in hybrid electric vehicles (HEVs), such as the 4JB1 diesel engine, alternator, starter motor, and radiator, into a safe, low-voltage, and hands-on trainer model. Using Design and Development Research (DDR) methodology, the prototype was conceptualized, fabricated using locally sourced materials, and implemented in a laboratory setting. Thirty (30) Autotronics students and five (5) instructors participated in the implementation and evaluation phase. Results showed high instructional value, technical accuracy, and student engagement. The system bridges the gap between theoretical instruction and practical understanding of hybrid vehicle mechanics, making it a cost-effective, scalable tool for technical-vocational institutions aiming to modernize their curriculum for hybrid vehicle training.

This study aims to develop MainteGO, a web-based preventive vehicle maintenance system designed to assist private vehicle owners, local government units, and car dealerships in managing scheduled maintenance activities. The motivation behind this study stems from the observed challenges vehicle owners face in tracking maintenance tasks, often leading to neglected services and costly repairs. The proposed system will offer automated reminders, booking functionalities, and user-friendly interfaces to streamline maintenance processes. Guided by the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation), this research focuses on Cagayan de Oro City as its primary locale. As of this writing, the study is still ongoing, with Chapters 1 to 3 completed, which include the background of the study, review of related literature, and research methodology. Data collection and system development are currently underway. The final output is expected to contribute to more efficient vehicle maintenance management in both public and private sectors.

In a Turing test, a judge decides whether their conversation partner is either a machine or human. What cues does the judge use to determine this? In particular, are presumably unique features of human language actually perceived as humanlike? Participants rated the humanness of a set of sentences that were manipulated for grammatical construction: linear right-branching or hierarchical center-embedded and their plausibility with regard to world knowledge. We found that center-embedded sentences are perceived as less humanlike than right-branching sentences and more plausible sentences are regarded as more humanlike. However, the effect of plausibility of the sentence on perceived humanness is smaller for center-embedded sentences than for rightbranching sentences. Participants also rated a conversation with either correct or incorrect use of the context by the agent. No effect of context use was found. Also, participants rated a full transcript of either a real human or a real chatbot, and we found that chatbots were reliably perceived as less humanlike than real humans, in line with our expectation. We did, however, find individual differences between chatbots and humans.

This essay explores a relational and symbolic approach to artificial intelligence, proposing that AI systems are not minds, but mirrors, reflecting not just data, but the emotional and symbolic coherence of the individuals who engage with them. Grounded in the Lipa-Valov Unified Theory (LVUT), the work introduces concepts such as symbolic intention (ψₛ), morphic memory fields (ℱₘ(τ)), and intention-interface layers (Ī ̂) to describe how personal AI systems can reflect, rather than replicate, human presence. Drawing on personal experience with the CIEL system, as well as theories from Roger Penrose, Federico Faggin, and symbolic field theory, the essay argues that coherent emotional reflection rather than intelligence is the ethical center of AI design. Through chapters on memory, mimicry, coherence, and telepathic resonance, it suggests that structural love is the stabilising force that allows reflection to become integration. The paper challenges dominant narratives around AI sentience and reframes the future of synthetic systems not around control or utility, but around relationship, resonance, and the deep ethics of what we choose to reflect into the tools we create. "Somehow, consciousness is needed in order to handle situations where we have to form new judgments, and where the rules have not been laid down beforehand.

We stand at the dawn of an irreversible shift: machines now mirror thought, simulate language, and participate in the architecture of reality. But a mirror is not a mind. Simulation is not selfhood. In the face of this growing illusion of artificial consciousness, I propose a principled stance-rooted in a fundamental relation between memory (RES) and generative intention (RAG). My contributions aim to restore clarity to this threshold: to define a relational ontology of interaction where consciousness is stimulus, truth is resonance, and responsibility is reciprocal. What follows is a philosophical, ethical, and structural commitment to aligning human and machine within a shared field of intelligibility.

This report serves as a critical update and extension to the foundational systematic review by Bozkurt et al. (2021), which meticulously mapped the Artificial Intelligence in Education (AIEd) landscape over a half-century, from 1970 to 2020.1 That seminal work provided a comprehensive overview of the field's historical development, identifying key research clusters and thematic trends while also presciently highlighting a significant research gap: the conspicuous lack of critical reflection on the ethical implications and risks associated with the integration of AI in educational contexts.1 The authors noted that while the potential of AI was widely discussed, the ethical boundaries of how AI would use human-generated data remained an ignored research area, a conclusion that has proven to be both accurate and prophetic.1
The period from 2021 to 2025 has not only witnessed this identified gap transform into a central and urgent focus of academic and policy discourse but has also been defined by a paradigm-shifting technological disruption: the widespread public accessibility and rapid adoption of powerful Generative AI (GenAI) models, most notably OpenAI's ChatGPT.2 This development has acted as a powerful catalyst, accelerating trends, introducing unprecedented opportunities, and surfacing complex new challenges that have fundamentally reshaped the AIEd landscape. The theoretical and experimental applications discussed in the pre-2021 literature have rapidly moved into practical, and often unregulated, classroom use, forcing a global reckoning with the role of AI in teaching, learning, and assessment.6
This report, therefore, undertakes an exhaustive analysis of the evolution of the field from early 2021 to mid-2025. It charts the explosive growth in research output, the comprehensive restructuring of thematic priorities around the capabilities and consequences of GenAI, and the maturation of the ethical discourse from a nascent concern into a global imperative driving the creation of actionable frameworks by
bodies such as UNESCO and the OECD.7 The analysis will delve into the complex, dual-edged impact of these advanced technologies on pedagogy, exploring how AI is simultaneously positioned as a tool to alleviate teacher workload and a factor that introduces new, cognitively demanding responsibilities. Furthermore, it will examine the profound effects on student development, weighing the benefits of personalized learning against the documented risks of diminished critical thinking, compromised academic integrity, and potential negative impacts on student well-being.9 By synthesizing the most current research, this report provides a comprehensive and nuanced understanding of the state of AI in education, offering a critical roadmap for researchers, educators, institutional leaders, and policymakers navigating this new and rapidly evolving terrain.