Human-Centric Artificial Intelligence Pedagogy (HCAP) framework developed from TPACK through integration of artificial intelligence literacy and competency

Synthesis: Chiu (2026) proposes the Human-Centric AI Pedagogy (HCAP) framework, an evolution of the Technological Pedagogical Content Knowledge (TPACK) model designed for the generative AI era. Arguing that AI's agentic autonomy, epistemic complexities, and ethical dimensions render the established TPACK framework insufficient, HCAP integrates five knowledge domains: AI-Technological, AI-Content, AI-Pedagogical, Human-AI Collaborative, and Ethical Knowledge. A three-round Delphi study with 30 teachers across diverse subjects produced a consensus on 25 critical knowledge items, providing an empirically grounded model that translates theoretical AI pedagogy into actionable teacher competencies and equips educators to move from merely using AI to strategically orchestrating human-AI collaborative learning.

Key Findings

HCAP extends TPACK with five interdependent knowledge domains: three that transform the original TPACK cores through a sociotechnical lens — AI-Technological Knowledge (I-TK), AI-Content Knowledge (I-CK), and AI-Pedagogical Knowledge (I-PK) — plus two entirely novel domains: Human-AI Collaboration Knowledge (HAIC-K) and Ethical Knowledge (Ethics-K).

The framework addresses four critical gaps in TPACK: tool agency (understanding AI's operational logic, probabilistic nature, and prompt engineering), epistemic challenges (teachers shifting from content deliverers to critical validators), ethical complexity (making ethical reasoning a core, explicit domain), and dynamic collaboration (conceptualizing human-AI collaboration where cognitive tasks are shared).

A three-round Delphi study with 30 teachers (adopting a 75% agreement threshold) refined and reached consensus on 25 critical knowledge items across the five domains, including: prompt engineering, limitation awareness, system auditing, data fundamentals, tool integration (I-TK); critical validation/source triangulation, bias detection, contextualization, disciplinary methodological critique (I-CK); reflective practice, assessment redesign, scaffolded co-agency design, personalization, ethical integration (I-PK); dynamic role allocation, interaction flow design, critical dialogue, critical interdependence, group dynamics collaboration (HAIC-K); and equity auditing, data privacy, usage protocols, societal impact, well-being advocacy, inclusive design (Ethics-K).

The knowledge domains are anchored in foundational literacies: I-TK in data and computational literacy, I-CK/I-PK in critical/algorithmic/media and scientific literacies, HAIC-K in collaborative and epistemic literacies, and Ethics-K in ethical and well-being literacies.

Study Design & Method

This study used a three-round Delphi method with a panel of 30 teachers from diverse subjects to establish expert consensus on the knowledge and skills required within each HCAP domain. An initial list of 20 knowledge items was refined iteratively: in Round One, seven items reached consensus and two new items were proposed; in Round Two, four more reached consensus, one new item (well-being advocacy) was proposed, and some items were split or renamed; in Round Three, nine more reached consensus and one new item (ethical integration) was proposed. The final list comprised 25 knowledge items. The study adopted a 75% agreement threshold (in line with prior Delphi research), and items endorsed for removal by more than half of the teachers were excluded. Teacher modifications to item labels and descriptions were incorporated across rounds.

Implications for AI in Education

The HCAP framework provides a concrete, empirically grounded roadmap for teacher education and Faculty Development, translating a theoretical model into actionable competencies for orchestrating human-AI collaborative learning. It positions teachers not merely as tool users but as strategic orchestrators and conductors of human-AI collaboration who use AI ethically, critically, and productively. The five-domain structure offers a practical basis for designing future-ready teacher training programs and professional development, connecting to AI Literacy, Teacher AI Competency, Teacher Role, and Instructional Design. It responds to the paradigm shift from passive, deterministic digital tools to active, agentic AI systems, and its ethical and collaboration domains address the human-centred concerns central to responsible AI Education in the generative AI era.

Limitations

The study's consensus was derived from a panel of 30 teachers, and the 75% agreement threshold and consensus criteria are inherently subjective (as the author notes, there are no universal standards for Delphi consensus). The knowledge items reflect a teacher-perspective view and were validated through expert consensus rather than classroom outcome data; the framework's implementation effectiveness requires empirical validation in practice. The Delphi refinement process involved renaming and restructuring items, so the final 25-item list reflects the specific panel's judgements and may not generalize to all educational contexts.

Connected Concepts

AI Literacy

Teacher AI Competency

Teacher Role

Pedagogical LLM Training

Faculty Development

Ethics

Instructional Design

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Citation

Chiu, T. K. F. (2026). Human-Centric Artificial Intelligence Pedagogy (HCAP) framework developed from TPACK through integration of artificial intelligence literacy and competency. Interactive Learning Environments.