A look at how Artificial Intelligence reshapes work in Europe shows both hope and unease coexist. Although most people see advantages, nearly two out of three think their jobs could vanish because of AI. A clear majority (84%) insist humans must stay in control. Based on data from Special Eurobarometer 554, this paper explores what lies behind such anxieties through the sociological lenses of thinkers like Marx and Weber. Findings point clearly that unequal risk shapes nervousness: those who feel economically challenged or lack tech confidence report higher stress levels. Still, fear of losing control shows up everywhere, even among those fluent in digital tools, echoing Weber’s unease with the hidden rules of bureaucratic systems. In response, our approach leans on a “Trust by Design” model shaped around the EU AI Act, where openness and shared decision-making help shift attitudes from pushback toward collaboration.

The Future of Jobs Report 2025, published by the World Economic Forum, ranks Artificial Intelligence and Big Data as leading transformative technologies shaping the global labor market, with remote and XR-enhanced technologies implicitly included under “AI and information processing.” yet their distinctive experiential potential deserves explicit attention in engineering education. Considering their growing adoption across industries for modeling, testing, and training - and their potential to complement traditional methods by fostering experiential and collaborative learning - these technologies play an important role in preparing future engineers for emerging roles in the future of jobs. This work explores how remote and XR-enhanced technologies can be integrated as complementary tools that naturally engage students’ curiosity, enhance learning, support inclusive and future-oriented learning. Selected examples from applied projects in mechanical engineering demonstrate practical strategies for integration, and show how these technologies, by improving motivation and conceptual understanding, can expand opportunities for their use while promoting ethical principles such as collaboration, equity, autonomy, accessibility, and lifelong learning, and offering scalability and safety. All these features are strengthened when integrated with AI and Big Data.

This qualitative study investigates the attitudes and behaviors of students in Bucharest, Romania, toward the increasing use of Artificial Intelligence in academia. Conducted through semi-structured interviews and guided by the theory of planned behavior, the research explores how students from diverse fields like sociology, computer science, and medicine perceive and utilize AI as a learning tool. The study addresses the technology's influence on evaluation processes, academic integrity issues such as plagiarism, and the broader ethical implications of its integration. The thematic analysis of the interview data reveals a complex and dualistic perception of AI's role in the modern educational landscape.

The findings highlight a significant divide in how AI is viewed. On one hand, students widely acknowledge its positive impact, primarily valuing it as a powerful tool for efficiency and enhanced learning. AI tools like ChatGPT are consistently used to save time by summarizing long texts, generating project outlines, and quickly locating specific information. This allows students to better manage demanding schedules, which often balance academic responsibilities with employment. Furthermore, AI functions as a personalized tutor, offering alternative explanations for complex concepts and assisting with technical tasks, which is particularly beneficial for students who need supplementary support outside of lectures. It also serves as a source of inspiration, helping to overcome creative blocks by providing a foundation of ideas upon which students can build.

On the other hand, the study uncovers deep-seated concerns about the negative consequences of AI reliance. A primary risk identified is the potential erosion of critical thinking and the promotion of intellectual laziness. The ease of generating complete assignments encourages a "copy-paste" mentality, which circumvents the fundamental learning process and is detrimental to long-term knowledge retention. This dependency raises significant issues of academic integrity, making plagiarism more accessible and widespread. Moreover, students are aware of the technology's limitations, including its tendency to "hallucinate" or produce inaccurate information and fabricated sources, which poses a threat to the quality of academic work.

The research concludes that the impact of AI is not inherent to the technology itself but is contingent on the user's intent and method of application. While responsible use can augment learning, its abuse leads to superficial understanding. A notable gap exists between prohibitive or nonexistent formal academic policies and the widespread, informal use of AI by students. This disconnect points to a need for universities to move beyond simple bans and instead develop clear guidelines for responsible use. Students advocate for pragmatic solutions, including formal training on AI ethics and the adaptation of evaluation methods to test genuine comprehension over rote memorization. Ultimately, the study suggests that AI's integration into professional and academic life is inevitable. The academic environment must therefore evolve from a position of resistance to one of proactive integration, preparing students for a future where human-AI collaboration is the norm and the ability to leverage these tools effectively is a critical skill.

Telecommunications degree programs should transition from "demonstrative" laboratories to development environments in which students design, implement and operate real systems. Integrating edge platforms (Raspberry Pi/ NVIDIA Jetson), Software-Defined Radio (SDR) and digital signal processing tools (GNU Radio/ MATLAB) enables students to transition from theoretical concepts to functional prototypes early in their studies. However, employers increasingly report that graduates have practical shortcomings, such as difficulty integrating theoretical concepts into real-world applications, gaps in hardware and software troubleshooting, inconsistent use of modern tools, unsafe RF operation and inability to reproduce experiments. The proposed approach addresses these issues by constantly emphasizing the connection between theory and practice through incremental spiral projects and clear maturity criteria.

Cybersecurity trainings are based on static, pre-defined "capture the flag" (CTF) tasks and attack blueprints. Although good learning exercises, these practices cannot mimic the spontaneity and worldliness of real cyber threats to Industry 4.0 and cyber-physical systems. The possible disruption brought by artificial intelligence (AI), especially large language models (LLMs) and reinforcement learning (RL), will enable the creation of dynamic and context-based adversarial plans.

The goal of this paper is to determine if an academic framework for training on AI-crafted attacks, instead of training on fixed exercise sets is feasible or not. Our specific interest lies in large language model (LLM)-enhanced reinforcement learning for designing unpredictable adversarial activity (e.g., phishing emails, SQL injection, variations on malware) that adapts continuously against student defences.

We propose a multi-level learning platform, based on LLMs, which constructs attacks uniquely designed for the student's skill level and defence tactics. The framework consists of:

1. Content generation using LLM for natural-language attacks such as phishing.

2. RL-driven adversarial modelling for evolving technical exploits such as injection or malware evasion.

3. Student defence platforms comprise intrusion detection laboratories, secure coding assignments, and network monitoring simulations.

The data gathering will obtain pre/post-competency assessments, defence success rates, and student perceptions. Comparison analysis will measure the disparity between the learning outcome of the AI-based group and a comparison group based on static CTF challenges.

It is anticipated that learners subjected to adaptive AI-generated assaults will exhibit enhanced critical thinking capabilities, quicker detection and response times, and greater retention of defensive techniques. Furthermore, it is expected that they will develop heightened confidence when confronting unpredictable adversaries, indicative of skills that are more applicable to real-world contexts within Industry 4.0. Additionally, the framework offers educators access to scalable, perpetually updated training resources, eliminating the necessity for manually created challenges.