Aug 2026· Encyclopedia· Vol 6, pp. 168· 0 citations· 39 references
TL;DR
This paper examines how eight EU and international competence frameworks codify AI-related knowledge for STEAM educators, using a dual analytical lens comprising AI-TPACK and STEAMCompEdu and proposes three emerging theoretical extensions to AI-TPACK to address the identified lacunae.
Abstract
Competence frameworks function as structured knowledge codification systems: authoritative, domain-wide representations of professional knowledge that define what counts as competence within a field and how it should be organised. As such, they constitute a form of professional knowledge encyclopaedia, structured to comprehensively map an emerging or established knowledge domain. This paper examines how eight EU and international competence frameworks codify AI-related knowledge for STEAM educators, using a dual analytical lens comprising AI-TPACK and STEAMCompEdu. Read through this dual lens, the cross-framework analysis identifies four knowledge dimensions insufficiently addressed in the existing landscape: interdisciplinary pedagogical knowledge, creative-aesthetic AI knowledge, ethics as embedded disciplinary practice, and inquiry-oriented AI pedagogy. Based on these findings, the paper proposes three emerging theoretical extensions to AI-TPACK to address the identified lacunae and discusses implications for the design of structured professional knowledge codification systems that better represent complex, interdisciplinary, and rapidly evolving knowledge domains.
As artificial intelligence (AI) becomes increasingly embedded in educational systems, teachers face new expectations for engaging AI as part of their instructional work and professional responsibilities. This paper argues that clarifying AI competencies for teaching requires more than listing skills: it requires a coherent account of (1) what these competencies involve, (2) how they are enacted in practice, and (3) what evidence can support teacher learning and professional judgment over time. We conceptualize these integrated capacities as AI competencies for teaching and distinguish them from broader notions of AI literacy by emphasizing application, professional judgment, and context sensitivity. Drawing on trends in how AI is reshaping teaching, we introduce a framing of AI’s roles in education (as tool, content, and context) to specify the instructional demands to which our current understanding of teaching competencies must respond. We then summarize features of existing competency frameworks to surface shared foundations and key tensions related to ethics, technical depth, professional agency, and domain specificity. We describe six interrelated domains of AI competencies for teaching, spanning AI content knowledge, AI technologies, pedagogical integration, ethical awareness, professional judgment and agency, and openness to continuous learning. To illustrate what these competencies look like in use, we present a scenario that makes teachers’ instructional decision-making visible when AI is present. Finally, we discuss progress-oriented, evidence-centered approaches, including implications for assessment, that can support teachers’ development of these competencies. This work ultimately positions AI competencies for teaching as central to sustaining ethically grounded, responsible, and professionally empowering teaching in AI-mediated educational environments.
Suggested citation: Ober, T. M., Tenison, C., Phelps, G., Renfrow-Symon, E., Mikeska, J., & Dean, V. J. (in press). AI competencies for teaching: From conceptual frameworks to enacted practices. ETS Research Report Series. https://doi.org/10.64634/z5j3ts49
Teresa M. Ober, Caitlin Tenison, Geoffrey Phelps et al.· ETS Research Report Series· 0 citations
Generative artificial intelligence (GenAI) is changing how organisations create, share, apply and govern knowledge. However, its organisational value does not follow automatically from tool availability. Many GenAI initiatives remain limited to pilots, isolated experiments or local productivity gains because the knowledge resources, competencies and governance routines required for reliable use are not sufficiently defined. This conceptual paper examines how AI readiness and competency models can support knowledge-based GenAI implementation. It asks how recurring implementation challenges can be translated into organisational readiness requirements and then into role-specific competency dimensions. The paper draws on a conceptual synthesis of scholarly literature on AI implementation, AI readiness, AI capability, knowledge management, AI literacy, human-AI collaboration, organisational learning and AI governance. The synthesis is complemented by selected practice-oriented reports and anonymised insights from prior exploratory qualitative research on digital and AI-related competencies. The paper develops a competency-based AI readiness framework that links implementation challenges, including unclear problem definition, weak workflow integration, data quality and availability issues, governance gaps and resistance to change, with strategic, process-related, data-related, governance-related and human capability requirements. Its central argument is that competency models can function as knowledge management instruments. They make implicit GenAI-related knowledge requirements visible and translate them into technical, strategic, communicative and ethical-governance competencies. Existing approaches mainly catalogue organisational readiness factors, capability resources or individual AI literacy skills. The proposed framework goes one step further by linking implementation challenges with readiness requirements and role-specific competencies. In this way, competency models connect AI readiness with capability development, organisational learning, responsible use and sustainable value creation. By focusing on GenAI-supported knowledge work, the paper connects AI readiness and AI capability research with knowledge management. It proposes a framework for operationalising AI readiness through competency models and develops conceptual propositions that can be examined in future empirical research. The framework highlights how systematic competency development can strengthen knowledge flows, human-AI collaboration and the responsible use of GenAI in organisations.
Arno Onnen· European Conference on Knowl...· 0 citations
This study aims to advance knowledge management (KM) scholarship by theorizing how generative artificial intelligence (GenAI) reshapes knowledge practices in management education. Prior work on GenAI in higher education has been largely descriptive, mapping faculty experiences onto generic technology-adoption constructs while leaving its KM consequences under-theorized.
The authors address this gap by developing a process model of GenAI-mediated knowledge practices organized around three interrelated dynamics, namely knowledge integration, knowledge orchestration and epistemic risk. This study uses Dual Factor Theory and the Unified Theory of Acceptance and Use of Technology 2 as sensitizing frameworks rather than as the contribution itself.
The findings show that GenAI catalyzes knowledge integration by fusing artificial intelligence (AI)-generated content with disciplinary expertise, exposes weaknesses in knowledge orchestration where policy voids and uneven digital literacy fragment institutional coordination and generates qualitatively new epistemic risks such as hallucinated citations, opaque authorship and selective disclosure of AI use.
The research re-specifies dual-factor dynamics for GenAI-mediated knowledge work, demonstrating that enablers and inhibitors operate as independent epistemic forces rather than as opposing poles. Also, they extend KM scholarship on knowledge risk by identifying a class of AI-specific risks that conventional governance instruments are not designed to absorb. Finally, they offer practitioner-relevant guidance for academic leaders seeking to orchestrate responsible GenAI integration in knowledge-intensive institutions.
Wendai Yang, Sarthak Singh, S. Alshibani et al.· Journal of Knowledge Managem...· 0 citations
This paper conceptualizes Interdisciplinary Knowledge Integration and Application (IKIA) as a critical cognitive competence within the Cognitive Psychology Dimension of the VFC Competence Framework. In response to increasing demands for interdisciplinary problem-solving in education and workforce development, IKIA is positioned as a developmental construct that enables learners to synthesize, abstract, and transfer knowledge across disciplinary boundaries. Grounded in schema theory, experiential learning, cognitive load theory, and knowledge transfer models, the study explains how IKIA supports higher-order cognitive processing and adaptive expertise. The research applies the VFC’s KSAH model—Knowledge, Skills, Attitudes, and Habits—to systematically map the progression of IKIA from novice-level awareness to expert-level integration, innovation, and leadership. Using thematic synthesis and an analysis of empirical micro-cases, the paper illustrates how IKIA manifests cognitively, psychologically, and socially in real-world learning and professional contexts. In addition, the study examines the interaction between IKIA and complementary competencies, particularly Collaborative Intelligence & Influence (CI
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), highlighting how interdisciplinary integration is strengthened through social collaboration and collective reasoning. The paper concludes by proposing outcome-based learning indicators and outlining future research directions for the assessment, instructional design, and practical application of IKIA. Overall, this work advances the operationalization of interdisciplinary competence by framing IKIA as a measurable and teachable cognitive capacity, contributing to the development of ethically grounded competence-based education models for 21st-century learners.
Mohammad Moharram, A. Salem, Y. Eldin et al.· International Journal of Voc...· 0 citations
This study examines and refines an existing competence model by incorporating perspectives from diverse stakeholder groups and elaborated to provide an empirically grounded, didactically actionable framework for teacher education and future research.
Luca Mikula· The European Educational Res...· 0 citations
Artificial intelligence (AI) is increasingly embedded in organizational information environments, reshaping how knowledge is created, interpreted, and applied. As AI systems become active participants in organizational information processes, employees are required not only to operate digital technologies but also to interpret algorithmic outputs and collaborate effectively with intelligent systems. While prior research has extensively examined digital literacy, human–AI collaboration, and AI literacy, these streams of literature remain largely fragmented, providing limited conceptual integration regarding the competencies required for knowledge work in AI-enabled organizations. This study develops a conceptual framework that positions AI literacy as an extension of digital literacy within contemporary knowledge work environments. Drawing on human capital theory and sociotechnical systems theory, the paper explains how digital literacy provides foundational capabilities for navigating digital infrastructures, while AI literacy enables employees to critically evaluate algorithmic outputs and collaborate effectively with AI systems. The framework further proposes that human–AI collaboration acts as a key mechanism through which digital and AI-related competencies enhance knowledge work outcomes. The study contributes to the literature in three ways. First, it integrates fragmented research on digital literacy, AI literacy, and knowledge work into a unified conceptual perspective. Second, it reconceptualizes AI literacy as an emerging capability that extends digital literacy in AI-mediated organizational environments. Third, it proposes a theoretical framework explaining how human capabilities interact with intelligent technologies to support knowledge creation, decision-making, and organizational learning. By highlighting AI literacy as a strategic organizational capability, this study provides implications for information professionals and managers seeking to develop human competencies required for effective human–AI collaboration. The framework also provides a foundation for future empirical research examining how AI-related competencies influence knowledge work performance in digitally transformed organizations.
Unknown authors· Business Information Review· 0 citations