A layered curriculum architecture designed to support resilience under conditions of paradigm-level technological uncertainty is proposed, which enables incremental innovation through pilot modules while preserving long-term structural coherence.
Abstract
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 study proposes and verifies a framework intended to support curriculum alignment, instructional control, and academic integrity preservation within AI-enabled learning systems, and contributes a systems-oriented framework for embedding AI within educational systems while preserving pedagogical intent and governance requirements.
Ali Ahsan, Hayden McDonald, R. Saha et al.· Systems· 0 citations
A systematic review of peer-reviewed studies published between 2015 and 2024 concludes that while tools such as generative artificial intelligence, intelligent tutoring systems, virtual and augmented reality, and predictive analytics are increasingly present, their adoption is concentrated in specific areas, particularly problem-solving, simulation, and assessment.
Firas Almasri· International journal of tec...· 0 citations
Contribution: This paper presents a six-phase AI-assisted instructional design architecture based on the Curriculum as Code paradigm, integrating Generative AI with LaTeX and Python to automate the creation of reproducible, visually consistent, and technically precise materials for STEM education. Background: Creating customized instructional materials for active learning imposes a heavy workload on faculty. Standard presentation tools lack robust support for technical content, while current AI applications often hallucinate and fail to formalize the instructional authoring process, limiting their utility for rigorous academic design. Intended Outcomes: The framework aims to reduce preparation time while ensuring mathematical accuracy, adherence to institutional visual identity, and preservation of the instructor's tacit pedagogical knowledge through explicit rules. Application Design: The solution comprises a six-phase pipeline that replaces ad-hoc prompt engineering with a systematic workflow, utilizing text-based interfaces and code-driven generation (LaTeX/Beamer for slides, Python for figures), governed by pedagogical constraints, contextual calibrations, and automated review cycles. Findings: Validated over one year across 8 modules and 28 project contexts in a Project-Based Learning environment, the architecture significantly reduced instructor workload. Generated assets underwent independent peer review and were deployed by six different faculty members, confirming scalability beyond a single author. Based on over 600 voluntary student evaluations, materials achieved high quality ratings from 8.5 to 9.9/10. Results indicate high reproducibility, minimized hallucinations, and sustained pedagogical and visual fidelity, suggesting viability for broad STEM educational applications.
Artificial intelligence is changing the task composition of computing work faster than curricula and training typically adapt. This is a curriculum-framework paper, grounded in a structured narrative review of labor-market and software-engineering evidence and illustrated through an exploratory pilot course: the review supports the framework, and the pilot illustrates it rather than serving as primary evidence. The central claim is that near-term change is task reallocation rather than full replacement: routine implementation is increasingly automated while verification, systems thinking, security, and the ability to supervise and orchestrate AI (keeping a human in the loop) gain value. We organize the response as a capability-assurance framework anchored by a Capability Ladder: a five-level progression (trigger, automation, workflow, AI agent, agent team) that classifies the operational autonomy of AI-augmented work and the human supervision it requires. We map the ladder to course-level updates, workload-aware assessment, and stackable workforce credentials, and illustrate it through a two-semester pilot of a team-based, no-code course enrolling computing and business students. We argue for targeted modernization around durable capabilities rather than wholesale curriculum replacement, and we are explicit about evidence limits: labor signals are confounded by non-AI forces, industry reports are directional, and the pilot is exploratory.
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.
Ilya Levin, A. Gero· Innovations in Pedagogy and...· 0 citations
It is concluded 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.