Transforming Curriculum Design with Generative AI: A Model for Assessing Teacher Digital Competence

Synthesis: This PLS-SEM study of 434 in-service teachers in the Dominican Republic designs and psychometrically validates an extended Technology Acceptance Model (TAM) instrument for assessing teacher digital competence in integrating generative AI tools into curriculum planning. All 12 hypothesized paths were supported, and behavioural intention emerged as the main predictor of digital competence (β = 0.479), with Self Efficacy exerting the largest effect on perceived enjoyment (f² = 1.839). The model demonstrated strong reliability, convergent and discriminant validity, and good explanatory power (BI R² = 0.694; DC R² = 0.230), positioning behavioural intention as a strategic priority for Teacher AI Competency development.

Core Finding

The study confirms that an extended TAM framework — combining Self Efficacy, perceived usefulness, perceived ease of use, perceived enjoyment, and attitude of use — predicts teachers' behavioural intention to use GenAI tools in curriculum design, and that this behavioural intention is the strongest direct predictor of teachers' pedagogical digital competence (H12: β = 0.479, t = 10.226, f² = 0.298). The IPMA analysis reinforces this: behavioural intention combined the highest importance (≈0.49) and performance (≈86) for predicting digital competence, identifying it as the strategic lever for training interventions. Notably, self-efficacy was the exogenous root of the model, feeding usefulness (β = 0.446), ease of use (β = 0.500), and especially enjoyment (β = 0.805, f² = 1.839 — the largest effect in the model).

The Extended TAM Model

The instrument (40 items, 7-point Likert) operationalized seven latent factors, all specified as reflective: Digital Competence (DC), Behavioural Intention (BI), Attitude of Use (AOU), Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Perceived Enjoyment (PE), and Self-Efficacy (SE). Reliability was strong across all factors (Cronbach's α .952–.985; CR .961–.985), with convergent validity (AVE all > .50) and discriminant validity confirmed via both Fornell-Larcker and HTMT criteria.

All 12 hypotheses were supported:

• PU → AOU (β = 0.178) and PU → BI (β = 0.171) — perceived usefulness shapes both attitudes and intention.
• PEOU → AOU (β = 0.158) and PEOU → PU (β = 0.384) — ease of use feeds attitude and usefulness.
• PE → AOU (β = 0.530), PE → BI (β = 0.188), PE → PEOU (β = 0.323) — intrinsic enjoyment is the strongest antecedent of attitude.
• SE → PU (β = 0.446), SE → PEOU (β = 0.500), SE → PE (β = 0.805) — self-efficacy underpins the whole acceptance pathway.
• AOU → BI (β = 0.541) and BI → DC (β = 0.479) — the attitude–intention chain culminates in digital competence.

The model explained moderate-high variance in the perceptual factors (PU R² = 0.607, PEOU 0.614, PE 0.648, AOU 0.644, BI 0.694) but only weak variance in digital competence (DC R² = 0.230), signaling that unmeasured contextual factors also shape competence.

Self-Efficacy as the Root Driver

Self-efficacy is the sole exogenous variable and the most powerful effect in the model. Consistent with Bandura's Social Learning Theory, teachers confident in their ability to solve technical problems, learn autonomously, and innovate are substantially more likely to perceive GenAI tools as useful, easy, and enjoyable. The authors argue that a sense of effectiveness builds "technoptimism," reducing resistance to new technologies and accelerating implementation — a key consideration for Teacher AI Competency and Faculty Development programs.

Behavioural Intention as a Competence Lever

A novel contribution is the explicit demonstration that behavioural intention predicts digital competence — a relationship not previously established in the traditional TAM. The authors interpret this through practice: as teachers declare intent to learn and use AI tools, they actively seek methodological knowledge, experiment (even through failures), and build skills through use, leading to real competence gains. Conversely, replicating familiar methods and standard edtech limits competence development in a rapidly evolving technological landscape.

Context: The Digital Divide

The study is grounded in the Dominican Republic's pronounced dual digital divide: in rural areas 69.7% of low-income households lack home internet access versus 34.6% in urban contexts, with even high-income rural households (42.4%) lagging metropolitan areas (10.6%). These structural constraints mean individual-level determinants coexist with — and are constrained by — infrastructure and connectivity gaps, shaping the real possibilities of GenAI integration.

Relevance to the Wiki

This paper is a significant contribution to the Teacher AI Competency concept: it provides a validated, psychometric instrument and an extended-TAM model specifically for GenAI in curriculum planning, an area with limited empirical evidence in Latin American contexts. It connects individual acceptance psychology (Self Efficacy, Motivation) to Instructional Design practice and Faculty Development/professional development policy. Its core actionable message — that behavioural intention, fueled by self-efficacy and intrinsic enjoyment, is the primary lever for building teacher digital competence — offers a testable model for AI Education training design.

Connected Concepts

• Teacher AI Competency — the target construct the model is designed to assess and predict
• Self Efficacy — the exogenous root driving usefulness, ease of use, and enjoyment
• Motivation — perceived enjoyment as intrinsic motivation
• Instructional Design — curriculum planning and GenAI integration in teaching
• Generative AI — the tools (ChatGPT, DALL-E, Midjourney, Synthesia, HeyGen) being planned for
• Faculty Development — training implications
• Teacher Role — teachers as orchestrators rather than replaced by AI
• AI Education — broader field of AI integration in teaching
• Pedagogy — pedagogical digital competence and engaging pedagogies
• Assessment — GenAI tools for assessment efficiency

Connected Articles

• AI Acceptance Preservice Science Teachers 2026 — AI acceptance of pre-service teachers via TAM
• Teacher Education AI Literacy Sdt 2026 — Teacher education AI literacy
• GenAI Literacy Training Teacher Education Dbr 2026 — GenAI literacy training in teacher education
• AI Changing Teaching Workflows — AI changing teaching workflows
• Student Dependency On AI Literacy Self Efficacy 2026 — AI literacy and self-efficacy

Citation

Guillén-Gámez, F. D., Tomczyk, Ł., Habibi, A., & Díaz Vargas, B. L. (2026). Transforming curriculum design with generative AI: a model for assessing teacher digital competence. Educational Technology Research and Development.