Generative AI has transformed education, presenting both opportunities and challenges for Science teachers in integrating these tools into their classroom instruction (Barakat et al., 2025; Ramnarain et al., 2024). However, while there is increasing interest in GenAI, most research failed to look into educators’ perspectives, especially their satisfaction and motivation towards GenAI integration (Barakat et al., 2025). This study utilized a quantitative descriptive-comparative research design anchored on Fred Davis’ Technology Acceptance Model (TAM), which proposes how individuals come to accept and use novel technology. A total population of 30 university Science teachers who were teaching part-time or full-time in the A.Y. 2025-2026 and at least use GenAI-tools participated in the study via a structured questionnaire survey. Findings revealed that teachers reported moderate satisfaction (M = 3.38) and high motivation (M = 3.86) towards GenAI-tools integration, indicating receptiveness but not that high enthusiasm. Further analysis revealed no significant differences in satisfaction (p = 0.839, 0.287) and motivation (p = 0.438, 0.376) towards GenAI-tools integration across years in teaching experience and digital competence. This implies that teaching experience and digital competence do not act as barriers to adopting and appreciating GenAI in the classroom. This study recommends that professional development initiatives and workshops regarding GenAI do not need to be heavily segmented or customized based on a teacher’s tenure or current digital competence. Institution-wide training can be designed universally to benefit the entire faculty. However, to increase teacher satisfaction, institutions may need to introduce targeted support systems, such as providing specific use cases tailored for science instruction.