Teacher Readiness-By-Design for AI-Rich Interactive Learning

Synthesis: Charles (2026) argues that teacher readiness for Generative AI-rich interactive learning should not be inferred from adoption, confidence or digital competence, because none of these explains the quality of the designs teachers actually produce. The article is conceptual, building its framework through an iterative theory-building synthesis that integrates technology acceptance, AI Literacy, Technological Pedagogical Content Knowledge (TPACK) and Intelligent-TPACK, teacher design capacity and Learner Agency, responsible AI, institutional support and instructional design research. Readiness-by-design is an enactment-oriented capability in which teachers mobilize professional resources, including AI-related pedagogical knowledge, pedagogical beliefs, Self-Efficacy and ethical orientation, through design mediation, within an institutional and policy ecology, to produce designs that are aligned, cognitively productive, inclusive, transparent and subject to human oversight. The contribution is mechanism-centered: design mediation is the observable process linking resources to quality, policy clarity and institutional support moderate that translation, and evidence from enacted designs feeds back into professional learning and governance. Six directional propositions, candidate indicators and a design-studio model make the framework testable; it has not yet been empirically validated.

Key Findings

• Readiness is reframed as an enactment-oriented capability: teachers mobilize professional resources through design mediation, within an institutional and policy ecology, to produce aligned, cognitively productive, inclusive and transparent designs under human oversight.
• The construct has four defining properties: enactment-oriented, mechanism-centered, ecological and recursive. These prevent it from becoming a synonym for AI literacy or general teacher competence.
• Design mediation is the central mechanism: observable decisions and artifacts including tool selection, alignment, task and prompt design, scaffolding, role clarity, transparency, verification, assessment redesign, accessibility planning and human oversight.
• Self-efficacy is a mobilization resource, not a quality indicator. Confidence may increase experimentation and persistence, but without AI-related pedagogical knowledge or ethical orientation it can accelerate poor design.
• Policy clarity and institutional support are separate moderators. Material support can be high while policy expectations stay ambiguous, and the converse can occur; translation is strongest when both are high.
• Six directional propositions make the framework falsifiable, each paired with distinguishing indicators and illustrative tests. It offers construct definitions and evidence sources rather than validated instruments.

How the framework was built

The article reports no newly collected empirical data. Its approach is a theory-building synthesis guided by conceptual framework analysis, which treats a framework as a network of differentiated concepts rather than a catalog of variables. Seven initial teacher literature streams were selected because each answered a distinct theoretical question, from technology acceptance through AI literacy, TPACK, self-efficacy, teacher design capacity and agency, responsible AI, and institutional and instructional design scholarship. Sources were prioritized from January 2019 to June 2026, with 2024 to 2026 empirical studies deliberately included to test whether emerging evidence supported or challenged the proposed relationships. A parsimony check retained a construct only when it performed a theoretically distinct role; integration then proceeded in five analytic stages.

The mediated pathway to design quality

Professional resources enter a process of design mediation; the institutional and policy ecology enables and moderates that process; design mediation produces the proximal outcome of AI-rich instructional design quality; evidence from enactment creates feedback; and sector and discipline condition selected relationships. Digital competence is foundational but insufficient alone, because operational fluency cannot determine whether an AI-supported task promotes explanation, inquiry, collaboration or independent judgment. AI-related pedagogical knowledge is the most proximal resource, covering prompt refinement, output evaluation, automated versus human-mediated Feedback, disclosure and verification routines. Purposeful tool selection begins with a learning problem rather than a product, and the teacher asks whether a non-AI alternative would be safer or more cognitively productive. Çelik et al. (2026) supply the empirical anchor: teachers with stronger AI-specific technological and pedagogical knowledge produced higher-level prompts and more adaptive lesson plans, supporting the treatment of prompts as mediating design artifacts.

Boundary conditions, policy and professional learning

The framework is intended to travel across K-12 and higher education, but not as a context-free universal. Sector is a boundary condition to be tested, because learner age, safeguarding duties, disciplinary norms, assessment regimes and teacher autonomy alter the strength of particular pathways. The sixth proposition predicts that the measurement structure is substantially invariant across sectors while selected paths differ, with safeguarding and scaffolding stronger in K-12 and assessment redesign and learner autonomy stronger in higher education. The ecology enables professional learning and resource access and moderates the translation of resources into design mediation, which is why policy clarity is positioned as pedagogical infrastructure covering acceptable use, approved platforms, data protection, disclosure, assessment and human review. In the proposed pedagogical design studio, teachers start from a learning problem, produce a design dossier, have colleagues critique it against a quality rubric, then pilot, gather evidence and revise. The article also declines to equate non-use with unreadiness, since a reasoned decision to withhold AI can demonstrate readiness.

What this means for practice

• Replace tool training with design-based professional learning, giving teachers supported opportunities to solve design problems, articulate pedagogical and ethical rationales, test artifacts and revise from evidence.
• Write policy as decision rules and exemplars, distinguishing low-stakes experimentation from high-stakes uses such as automated grading, and connecting academic-integrity policy to assessment design.
• Judge quality from designs, not usage frequency. Institutions can combine blind rubric ratings of artifacts with evidence about learner agency, participation and accessibility.
• Review governance in light of enacted evidence. Approved-tool lists, disclosure guidance and professional learning priorities should be revised when evidence reveals misconceptions, inequitable access or privacy risks.

Limitations

• The framework has not been empirically validated. The article states that it is conceptual, offering no measurement or causal evidence, so the six propositions remain directional claims awaiting test.
• Breadth can reduce construct precision. Digital competence, AI literacy, AI-related pedagogical knowledge and Intelligent-TPACK may overlap, and common-method bias threatens any design measuring all constructs through one questionnaire.
• Learners, leaders and wider systems remain underdeveloped. Learner readiness, leadership practice, vendor responsibility and parental expectations are not modeled, and learning, wellbeing, equity and agency are distal consequences beyond the design-quality outcome.

Citation

Charles, Tendai. (2026). Teacher Readiness-By-Design for AI-Rich Interactive Learning. Journal of Computer Assisted Learning, 42, e70316. https://doi.org/10.1002/jcal.70316