Sep 2026· Innovations in Pedagogy and Technology· 0 citations· 46 references
TL;DR
This constructive position paper proposes the “vibe-designer”—a new professional paradigm that strategically compresses the traditional middle phase of the engineering curriculum to focus on high-level specification, adversarial evaluation, and systemic contextualization.
Abstract
As generative artificial intelligence (AI) automates engineering design, a fundamental reconceptualization of engineering education becomes necessary. This constructive position paper proposes the “vibe-designer”—a new professional paradigm that strategically compresses the traditional middle phase of the engineering curriculum to focus on high-level specification, adversarial evaluation, and systemic contextualization. Drawing on Floridi’s philosophy of information and Simondon’s theory of technical individuation, we argue that the future of engineering lies in the orchestration of conceptual frameworks, ethical stewardship of artificial agents, and “generative judgment”—the capacity to evaluate and contextualize machine-generated solutions within complex sociotechnical systems. We situate this paradigm explicitly within the context of next-generation heterogeneous engineered systems—including cognitive-cyber-physical-social-human (CCPSH) systems, AI-enabled agentic systems-of-systems, and multi-constituent platforms integrating hardware, software, cyberware, and brainware—that constitute the primary site of contemporary systems engineering practice. Central to our argument is the transformation from individual to collaborative creation: creativity in the AI era emerges from dialogical interaction between human intentionality and machine capability. We present a comprehensive curricular architecture based on a transdisciplinary approach, explore epistemic implications, and address critical challenges including the verification gap, liability frameworks, and the preservation of embodied technical intuition. To concretely illustrate the proposed approach, the authors present a hypothetical case study grounded in realistic engineering constraints and current AI capabilities. As a constructive position paper, it aims to provide a foundation for pilot programs and systematic empirical investigation—and to open a space for argument about where engineering education must go next.
Generative and agentic artificial intelligence (AI) are reconfiguring software and systems engineering from a discipline centered on human authorship of artifacts to one focused on directing, verifying, and governing autonomous systems. This transition demands a new professional archetype, the \emph{agentic engineer}, whose enduring value lies in intent specification, orchestration of multi-agent workflows, critical evaluation of machine-generated outputs, and ethical judgment. This article presents an integrative conceptual synthesis across engineering education, computing education, human--AI interaction, human factors, and the learning sciences to derive an evidence-grounded educational architecture for this archetype. We introduce the ACCEL framework (Agentic Competencies through Curricula, Collaboration, and Enduring Learning), which organizes five competency pillars and maps them to three delivery vectors: curricula, collaboration, and continuous learning. Drawing on agency theory, trust-in-automation research, and empirical studies of AI-assisted programming, including evidence that AI benefits are unevenly realized and often misperceived, we propose a scaffolded curriculum, a delegation--verification pedagogical loop for human--AI teaming, redesigned assessment, governance-literate ethics integration, and alignment with current curricular guidelines and international AI competency frameworks. We identify key risks, including automation bias, deskilling, superficial engagement, and diffuse accountability, and conclude that educating the agentic engineer requires systemic transformation rather than incremental curricular change: instruction must shift from producing artifacts to exercising judgment over increasingly autonomous socio-technical systems.
The rapid transformation of software development—driven by generative AI, AI-augmented workflows, low-code/no-code environments, and the early emergence of quantum computation—challenges the structural stability of ICT curricula. As introductory programming tasks become increasingly automatable, traditional curriculum models risk reactive reform rather than systematic adaptation.This paper proposes a layered curriculum architecture designed to support resilience under conditions of paradigm-level technological uncertainty. Grounded in prior research on AI-assisted programming and LCNC-based CDIO integration, and informed by institutional curriculum mapping within ICT degree programs, the study develops a four-layer educational ecosystem model. The model integrates foundational programming competencies, structured AI-augmented workflows, experimental studio-based environments, and introductory quantum literacy modules.Rather than organizing curricula around specific tools, the framework emphasizes modularity, paradigm agility, and durable cognitive skills such as abstraction, verification, and metacognitive awareness. The proposed architecture enables incremental innovation through pilot modules while preserving long-term structural coherence.By shifting the focus from technology prediction to systematic adaptability, the paper contributes a design-oriented model for future-resilient ICT education.
The rapid development of generative artificial intelligence (GenAI) technologies is reshaping the IT industry landscape. Large language models and automated code generation systems take over routine programming and design operations. Against this background, a contradiction emerges between traditional IT training models, centered on mastering syntax and basic algorithms, and current labor market demands, where AI orchestration skills, architectural thinking, and critical verification of machine solutions acquire greater weight. The aim of this study is to identify key areas and develop a conceptual model for transforming the professional training of future IT specialists in the context of the widespread adoption of generative AI. This study utilizes methods of systemic analysis of employer requirements, comparative pedagogical analysis of domestic and international practices, and pedagogical modeling. The paper substantiates the need to shift the focus of education from mechanical coding to prompt engineering, refactoring, and auditing of AI- generated solutions, as well as to the development of ethical reflection. An updated model of IT specialist competencies is proposed, including a new block on "Human- AI Collaboration." Methodological approaches for integrating GenAI tools into the educational process have been developed, including a transition to authentic assessment formats that focus on the decision- making process, not just the final software product.
S. M. Ziyaudinova, B. Elezhbiev· ACCOUNTING AND CONTROL· 0 citations
Artificial intelligence (AI) is rapidly transforming higher education. Notably, institutions are increasingly deploying AI-enabled chatbots across administrative, teaching and learning, and research functions. Despite the proliferation of these tools, most conversational systems remain generic and disconnected from subject-level context, curriculum design, and academic integrity requirements. A design-oriented framework is proposed in this paper to improve the effectiveness of AI-enabled chatbots used in teaching and learning, embedded within curriculum. The framework is grounded in systems thinking for context-aware AI-enabled learning systems that operate within bounded pedagogical and disciplinary environments. Adopting a design science approach, the study synthesises expert-informed insights from pedagogical, programmatic, and subject-level perspectives to develop a framework that integrates context alignment, instructional control, and integrity-preserving guardrails. The resulting artefact was verified through artefact-focused testing against the proposed design objectives using a structured non-human evaluation protocol, including requirement-based testing, baseline comparison with generic AI systems, academic integrity stress testing, and robustness analysis. The study proposes and verifies a framework intended to support curriculum alignment, instructional control, and academic integrity preservation within AI-enabled learning systems. The paper contributes a systems-oriented framework for embedding AI within educational systems while preserving pedagogical intent and governance requirements. Implications for scalable deployment of AI in higher education and future human-centred evaluation are discussed.
Ali Ahsan, Hayden McDonald, R. Saha et al.· Systems· 0 citations
The integration of artificial intelligence (AI) into design processes fundamentally transforms the nature of design knowledge. Traditional approaches often conceptualize the design process through a binary framework of black-box and glass-box metaphors. However, these metaphors are insufficient to capture the simultaneous, interactive, and permeable nature of knowledge production in AI-assisted design. Accordingly, this study examines the transformation of design knowledge in the AI era from an epistemological perspective, aiming to reconsider the processes that shape both knowledge and its production. The research employs a theory-building methodology situated within qualitative research paradigms. The findings reconceptualizes design knowledge not as a linear production chain, but as a multi-layered knowledge ecosystem in which human intuition, AI inference, and explainability layers are interwoven. In this respect, the study distinguishes itself from existing research by emphasizing a multi-layered knowledge ecosystem, moving beyond the black-box/glass-box duality, and proposing a third epistemological approach.
İpek Yıldırım Coruk· Journal of Interior Design a...· 0 citations
The growing capabilities of artificial intelligence (AI) have not translated straightforwardly into organisational value. A persistent disconnect—the “last-mile problem”—arises from structural gaps between idealised AI tasks and real-world organisational contexts. Synthesising insights from organisational theory, cognitive science, and computer science, we have developed a five-dimensional diagnostic framework that maps the challenges of human-AI collaboration across Integration, Representation, Scale, Temporality, and Adequacy gaps. These gaps illuminate how socio-technical complexity, contextualised problem representations, interdependencies among agents, dynamic environments, and limitations in current AI reasoning collectively constrain full automation and demand human judgement. By reviewing the historical evolution of AI—from symbolic systems to machine learning, generative models, and emerging agentic approaches—we show that augmentation remains the dominant and most viable mode of use in complex environments. An illustrative system-dynamics example demonstrates how improvements in algorithmic performance do not automatically yield proportional system-level gains. Overall, our framework provides researchers with a conceptual lens and practitioners with a diagnostic tool for assessing complementarities and informing the design of human-AI collaborations. The framework is offered as a conceptual synthesis and diagnostic instrument rather than an empirically validated model.
Ganesh Sankaran, Marco A. Palomino, G. Siestrup· Big Data and Cognitive Compu...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
