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.
Abstract
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 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
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
Artificial Intelligence (AI) is increasingly transforming education, with the emergence of Agentic AI introducing new opportunities for adaptive, autonomous, and context-aware support. This perspective paper explores Agentic AI, a bridge connecting educational leadership, developmental supervision, reflective professional learning, educator readiness, and sustainable education. Drawing on Glickman's Developmental Supervision Model, Gibb's Reflective Cycle, and research on teacher workload and professional growth, the paper argues that Agentic AI can support differentiated supervision, continuous reflection, and personalized professional development while reducing administrative pressures. However, its successful implementation depends on supportive leadership, organizational readiness, and educators’ willingness to engage with innovation. From a perspective paper, Agentic AI is positioned not as a replacement for educators, but as a tool that has the potential to enhance human expertise and strengthen educational sustainability. By integrating technological innovation with human-centered educational practices, Agentic AI offers a pathway toward more adaptive, inclusive, and future-oriented educational systems.
Maram Al Hasan, Ayat Kashef, Wissal A. Mohsen et al.· Frontiers in Education· 0 citations
Artificial intelligence (AI) is increasingly embedded in English as a Foreign/Second Language (EFL/ESL) education, yet existing research remains fragmented across tools, skills, and short-term outcomes. This review examines how AI is integrated into EFL/ESL education across language skills, instructional domains, and educational contexts.
This PRISMA-guided systematic review synthesised 221 unique peer-reviewed publications. Moving beyond a tool-centred inventory, the review analysed AI through four interrelated dimensions: pedagogical roles, mediating processes, reported outcomes, and contextual constraints.
AI systems increasingly operated as multifunctional pedagogical actors rather than isolated instructional aids. The most frequently coded roles were teacher orchestration/support, content and materials generation, assessment or diagnosis, coaching or practice companionship, tutoring or scaffolding, and conversational partnership. AI-mediated learning was especially concentrated in writing and speaking/communication, where text-based, voice-based, multimodal, immersive, and adaptive systems supported feedback, revision, rehearsal, and learner–system interaction. Reported benefits included expanded practice opportunities, accelerated feedback cycles, redistributed instructional labour, skill development, learner autonomy, affective support, assessment and monitoring, collaboration, and multilingual engagement. Recurring challenges included technical reliability and feedback quality, teacher readiness, privacy and data security, learner over-reliance, infrastructural inequality, bias and cultural mismatch, authorship and academic-integrity concerns, and methodological weaknesses.
Interpreted through a three-layer framework of efficiency, pedagogy, and ideology, the synthesis conceptualises AI in EFL/ESL education as a pedagogical ecology in which tools, learners, teachers, feedback regimes, assessment practices, institutional infrastructures, and governance arrangements interact. The review provides a theory-informed framework for analysing, designing, and governing AI-mediated language education beyond simple claims of technological effectiveness.
Arash Javadinejad, M. Davari· Frontiers in Education· 0 citations
Architectural education is being reshaped as Artificial Intelligence (AI) challenges human-centered conceptions of creativity, authorship, and knowledge production. However, current discussions on AI-supported architectural education remain largely focused on tool adoption, productivity, creativity, and student perception, while the policy implications of AI for curriculum design, studio governance, assessment, educator training, and ethical accountability remain underdeveloped. Addressing this gap, the study develops a policy-oriented posthuman framework for interpreting AI integration in architectural pedagogy and translating it into responsible design education principles. The study adopts a two-stage review design that combines a conceptual framing review of posthuman pedagogy with a systematic synthesis of empirical and pedagogical studies on AI, computational design, and architectural education published between 2010 and 2025. The review identifies three interrelated dimensions of AI-supported posthuman learning: distributed agency, in which design intelligence is shared across students, educators, AI systems, datasets, interfaces, materials, and studio environments; situated knowing, in which AI becomes pedagogically meaningful only when embedded in reflective, material, and context-sensitive design inquiry; and ethical entanglement, in which authorship, bias, accountability, originality, dependency, and environmental responsibility become core educational concerns. Based on these findings, the paper proposes ecological intelligence as a design education policy principle: the capacity to think, design, evaluate, and act responsibly within interconnected human, technological, material, environmental, and institutional systems. The contribution of the study is twofold. First, it clarifies the theoretical relevance of posthuman pedagogy for AI-supported architectural education. Second, it translates this theoretical perspective into a policy-oriented pedagogical framework that can inform curriculum development, studio pedagogy, assessment criteria, and ethical governance in architectural education.