# Determinants of perceived usefulness, satisfaction and behavioral intention of using AI in lesson planning among English teachers

## TL;DR

This study explores how English teachers perceive and intend to use AI for lesson planning, focusing on factors like usefulness and satisfaction.

## Contribution

The study integrates TAM2, DTAM, and SDT to propose a novel model explaining AI adoption in education.

## Key findings

- Output quality significantly enhances perceived usefulness and needs satisfaction.
- Job relevance and result demonstrability influence perceived usefulness but not needs satisfaction.
- Needs satisfaction mediates the relationship between perceived usefulness and behavioral intention.

## Abstract

Artificial Intelligence (AI) can help teachers plan lessons more efficiently, but it also raises concerns about increased cognitive load, loss of autonomy, and uniform lesson plans. This study aims to investigate drivers of English teacher perceived usefulness (PU), needs satisfaction (NS), and behavioral intention (BI) towards AI-assisted lesson planning tools. By integrating Technology Acceptance Model 2 (TAM2), Decomposed Technology Acceptance Model (DTAM) and Self-Determination Theory (SDT), we propose a research model positioning output quality (OQ), job relevance (JR), and result demonstrability (RD) as antecedents, PU and NS as mediators, and BI as the outcome variable. Data were collected from 485 English teachers via a questionnaire survey and data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results revealed that OQ significantly enhances both PU and NS (p < 0.001). JR and RD significantly and positively influence PU (β = 0.435, p < 0.001 for RD; β = 0.185, p < 0.001 for JR) but show no significant direct effect on NS (p > 0.05). Furthermore, both PU (β = 0.428, p < 0.001) and NS (β = 0.180, p < 0.001) directly and significantly predict BI, with NS serving as a significant mediator in the PU-BI pathway (β = 0.095, p < 0.05). These findings offer a solid theoretical and empirical foundation for understanding the cognitive and psychological mechanisms underlying teachers’ AI adoption behavior, and provide targeted practical implications for the design and promotion of AI educational tools.